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Assessing the Health of Akamkpa Forest Reserves in Southeastern Part of Nigeria Using Remote Sensing Techniques

Assessing the Health of Akamkpa Forest Reserves in Southeastern Part of Nigeria Using Remote... Hindawi International Journal of Forestry Research Volume 2020, Article ID 8739864, 15 pages https://doi.org/10.1155/2020/8739864 Research Article Assessing the Health of Akamkpa Forest Reserves in Southeastern Part of Nigeria Using Remote Sensing Techniques Elijah S. Ebinne, Ojima I. Apeh , Raphael I. Ndukwu, and Edebo J. Abah Department of Geoinformatics & Surveying, University of Nigeria, Enugu Campus, Enugu, Nigeria Correspondence should be addressed to Ojima I. Apeh; ojima.apeh@unn.edu.ng Received 18 November 2019; Revised 10 June 2020; Accepted 20 June 2020; Published 27 July 2020 Academic Editor: Nikolaos D. Hasanagas Copyright © 2020 Elijah S. Ebinne et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Assessment of forest health is very vital because forests form the largest terrestrial ecosystems on earth. +e greenness of vegetation is one of the essential factors used in evaluating the health of forest reserves. +is study is aimed at assessing the health of fifteen forest reserves in Southeastern part of Nigeria using meteorological data and MOD13A1-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Related portions of the monthly MOD13A1 data, derived for the years 2010, 2014, and 2018, were downloaded, and the monthly mean values of the vegetation indices (NDVI and EVI) were estimated for each of the forest reserves using the Spatial Analysis Module in ArcGIS software. +e computed monthly mean values of NDVI range from 0.094 to 0.790 while that of EVI ranges from 0.11 to 0.624 and the rainfall data range from 0 to 780.2 mm/month within the period of study. Analyses of the correlation coefficients between monthly rainfall data and NDVI, monthly rainfall data and EVI, and that of NDVI and EVI range from −0.827 to 0.584; −0.715 to 0.914, and 0.598 to 0.980. +e obtained results indicate that some of the forest reserves are moderately healthy while some areas are under great stress. We can conclude that satellite remote sensing is a veritable tool in the assessment, management, and monitoring of forest health especially where there is little or no terrestrially acquired forest inventory data. but their acquisition is time-consuming, laborious, and cost- 1. Introduction intensive. To worsen the matter, there is paucity of long- Forest health in Cross River State (a State in Southeastern standing and standardized forest health inventory programs Nigeria) is threatened by so many factors such as colonial in the study area, thereby necessitating the use of remote nationalization and commodification of the forest estate, sensing techniques to assess the health of the forest reserves agricultural practices, government plantations and defor- in Cross River State following the outcry by [1] and the estation, uncontrolled extraction of nontimber forest reports from United Nations Programme [3, 4]. +is lack of products (NTFPs), highway construction and mining of terrestrially acquired forest inventory data makes it im- solid minerals, dereservation of large portions of some possible to integrate satellite remote sensing techniques with government forest reserves, and foresters and resistance to past inventory data in assessing the health of these forest decentralized forest management [1]. Forest health is a reserves under study in contrary to what is obtainable in condition of forest ecosystem that sustains their complexity some other studies [5–7]. +e Normalized Difference while providing for human needs [2]. Healthy forests, which Vegetation Index (NDVI) and Enhanced Vegetation Index could be assessed and monitored by many forest health (EVI) derived from MODIS (moderate resolution imaging indicators, are needed for aesthetical pleasure, satisfaction of spectroradiometer) coupled with meteorological data have human needs, and maintenance of sustainable ecosystem. been severally used to determine the greenness (which is an It is unarguable that the indicators of forest health indicator of healthy condition) of vegetation in many lo- obtained from forest inventory programs are more accurate calities [8–13]. 2 International Journal of Forestry Research decrease or increase in the value of NDVI and EVI. Holding Absorption and reflection of photosynthetically active radiation [14] over a given period of time, which is shown by all other factors that affect vegetation constant, a healthy forest reserve should yearly improve in greenness meaning the vegetation indices, can be used to assess the health of forest reserves. NDVI and EVI are vegetation indices used that the estimated mean values of the vegetation indices for measuring the greenness of the vegetation. +ey range should remain the same or increase from year to year. from −1 to +1, and they are very useful in monitoring and Additionally, only positive values of NDVI should corre- understanding environmental and climatic changes. Vegeta- spond to vegetated zones like the forest reserves [23]. +e tion indices are radiometric measures of photosynthetically objectives of this study were to (1) compute monthly mean active radiation absorbed by chlorophyll in the green leaves of values of NDVI and EVI from multitemporal MODIS data for each of the fifteen forest reserves; (2) determine corre- vegetation canopies and are therefore good surrogate mea- sures of the physiologically functioning surface greenness level lation coefficients between the rainfall data and the vege- tation indices; (3) determine the yearly seasonal variation of of a region (https://climatedataguide.ucar.edu/climate-data/ ndvi-normalized-difference-vegetation-index-3rd-generation- vegetation indices; and (4) analyze the computed parameters in order to assess the health of each of the fifteen forest nasagfsc-gimms). +e MODIS-derived vegetation indices (NDVI and EVI) reserves. +is study is very important in that it will help in have been severally used to analyze the spatiotemporal determining whether the forest reserves are actually dete- trends over Southern Asia [8]; extract and understand the riorating or improving in health and can be applied in the phenological information about evergreen, semievergreen, monitoring and management of natural resources against moist deciduous, and dry deciduous vegetation in India [9]; artificial and natural environmental hazards. forecast the seasonal crop yield in Canada [15]; identify the corn growth in Western Mexico [16]; estimate the forest 2. Materials and Methods productivity and dieback in a Mediterranean holm oak forest [10]; assess the seasonal and potential canopy photosynthesis +e designed workflow of this study is shown in Figure 1. in relation to seasonal changes in Leaf Area Index (LAI), chlorophyll concentration, and air temperatures of NE Argentina subtropical forests [11]; quantify the drought 2.1. Study Site. Cross River State is a coastal State in intensity and its geographical effects in order to support the Southeastern Nigeria and also one of the states within the drought monitoring applications [17]; investigate the bio- Niger Delta region. +e tropical climate of Cross River State mass of forest reserve and the periods of vegetation [12]; has an average annual temperature and rainfall as 26.1 C and ° ° depict the quality and relative health of a forest reserve [13]; 2750 mm, respectively. It lies within latitudes 4 48′45″N–6 52′ ° ° determine the onset of phenophases in spring and autumn 05″N and longitudes 7 49′ 35″E–9 22′05″E. In Cross River and also to assess forest growth and health condition [18]; State, there are four synoptic stations of the Nigerian Mete- evaluate the sensitivity of MODIS-based vegetation index to orological Agency strategically located in different regions heat and drought stress in temperate forests [19]; and (Calabar, Ikom, Obudu, and Ogoja) within the state. +e monitor the trends and changes in vegetation and evaluate coordinates (longitude and latitude) of the four meteorological the proxies for drought conditions [20]. stations where the rainfall data were obtained from are given as ° ° ° ° ° ° Several other factors can affect forest health and vitality Calabar (8.35 , 4.97 ), Ikom (8.72 , 5.97 ), Ogoja (8.80 , 6.70 ), ° ° which include abiotic factors (wild or human-induced fires, and Obudu (9.10 , 6.61 ). pollution, floods, nutrients, and extreme weather conditions Cross River State has rich natural resources, and among such as storms, hurricanes, droughts, snow, frost, wind, and them are huge forest reserves, cutting across various local sun); biotic factors (insect pests, diseases and invasive government areas of the State. Fifteen of these forest reserves species and can either consist of fungi, plants, animal, or (Afi River, Achara Ihe, Agoi, Cross River North, Cross River bacteria); and human factors (overexploitation, competing South, Ekinta River, Gabu, Ikom, Ikrigon, Lower Enyong, land uses, and poor harvesting techniques or management Oban Group, Obieze-Isu, Umon Ndealichi, Uwet Odot, and can negatively impact forest ecosystems) [21]. +e major Yache) are considered, and they are referred to as Akamkpa reasons for the degradation of the forest reserves under study forest reserves in this study. Figure 2 shows the map of Cross are deforestation and competing land uses. It is true that the River State and the fifteen forest reserves evaluated in this mechanism that determines forest health is complex [22], study. but in this study, we have chosen to assess the forest health +e major activities affecting the health of these forest by estimating the greenness (which is a direct measure of reserves are deforestation, logging and timber extraction, photosynthetic potential resulting from the composite establishment of tree-crop agricultural plantations (e.g., property of total leaf chlorophyll, leaf area, canopy cover, cocoa, rubber, oil palm, cashew, and gmelina) using the and canopy architecture) of vegetation using vegetation taungya system, construction of highways (e.g., Calabar- indices derived from MODIS data for the years 2010, 2014, Ikom-Ogoja road, Ogoja-Obudu-Ikom road, Calabar-Oban- and 2018 coupled with monthly rainfall data of the study Ekang road, Calabar-Itu-Ikot Ekpene road, and Ikang- area. Calabar), mining of solid minerals, farming, extraction of +e premise of the study is that NDVI and EVI are nontimber forest products (e.g., wild edible vegetables, wild indicators of vegetation health since deterioration and im- edible fruits, medicinal plants, fuel wood, building materials, provement of ecosystem in vegetation is often reflected in a and arts and craft materials), dereservation of large areas of International Journal of Forestry Research 3 Data Monthly Daily MODIS rainfall data data Subsetting of Averaging data Scaling of data Monthly rainfall data Zonal statistical analysis Statistical analyses Monthly mean values of NDVI/EVI Health of forest reserves Figure 1: Schematic workflow of the study. Figure 2: Representation of the location site used in the present research. 4 International Journal of Forestry Research certain forest reserves, and rapid urbanization [1, 3]. monthly rainfall data, and its unit is in millimetres (mm) According to [4], “the main threats to the forest reserves (https://www.nimet.gov.ng/). include subsistence and commercial agriculture, fuel wood collection (firewood and charcoal), overharvesting of timber (illegal), forest fires, settlements, and infrastructure devel- 2.4. Data Analysis and Interpretation. +e monthly mean values of the vegetation indices were thoroughly examined opment.” Exotic tree species like teak and gmelina arborea are found in some of these forest reserves. to see if they are within the range of low or moderate or high vegetation so as to determine the health condition of each of the forest reserves. Vegetation greenness is shown 2.2. Acquisition and Processing of Satellite Data. +e monthly by the mean values of the monthly NDVI. Very low values data of MODIS (MOD13A1) of years 2010, 2014, and 2018 (≤0.1) of NDVI represent the barren areas of rock, sand, or were downloaded from NASA website. MODIS images are snow while moderate values (0.2 to 0.3) represent shrub distributed as hierarchical data format (HDF) with tiles of and grassland and high values (0.6 to 0.8) represent 10 ×10 arc degree and projected in the sinusoidal projection. temperate and tropical rainforests [13, 26]. According to +e geometric correction was applied to reproject the raw these studies [26, 27] mean NDVI for deciduous and ev- data into UTM WGS84 datum. +is study used the 16-bit ergreen forests should range from 0.42 to 0.51 and 0.52 to signed integer MOD13A1 for vegetation indices. +e blue, 0.69, respectively. As revealed in an earlier study over a red, and near-infrared reflectance (centred at 469 nano- large portion of the Sahel region [23], only positive values meters, 645 nanometers, and 858 nanometers, respectively) of NDVI should correspond to vegetated zones such as the were used to determine the MODIS daily vegetation indices. forest reserves. +e higher the index, the greater the +ree different composite sets (8-day, 16-day, and dual-8- chlorophyll content of the forest reserves and the healthier day composite periods) of 12-month time series MOD- the forest reserves. IS_500 m NDVI and EVI data spanning from January to EVI is directly related to the NDVI and gives more December for years 2010, 2014, and 2018 were computed weight to the near-infrared band. +e NDVI and EVI are using the Spatial Analysis Module in ArcGIS software in very similar, with the NDVI being directly related to primary order to analyze seasonal vegetation dynamics and corre- production and the EVI being more heavily weighted to the lation with meteorological data. Mapping Leaf Area Index in very dense plant canopies [28]. +e data were imported into ArcGIS 10.1 software for +e advantage of using EVI is that it uses additional processing. +e boundary shapefile of Cross River State was wavelengths of light to correct for variations in solar inci- used to subset the global data. +e boundaries of the fifteen dence angle, atmospheric conditions like distortions in the forest reserves were extracted from the forest reserve map reflected light caused by the particles in the air, and signals obtained from the State Forestry Commission. +e Spatial from the ground cover below the vegetation. NDVI is highly Analyst Module in ArcGIS Software was used to estimate the sensitive to soil background and atmospheric effects, while monthly mean values of NDVI and EVI of the fifteen forest EVI is less sensitive to them, thereby making it to provide a reserves. +e resulting table was then saved in Microsoft better quantification of vegetation abundance and physio- Excel for further statistical analyses. +e obtained values of logical activity [24, 29]. Overall, the correlation coefficient NDVI and EVI were multiplied by the scale factor of 0.0001. between NDVI and EVI is expected to be highly positive. +e equations used for computing EVI (equation (1)) and In order to allow for proper assessment of the health of NDVI (equation (2)) are [24, 25]: the fifteen forest reserves, the monthly rainfall data and the NIR − Red monthly values of NDVI and EVI were averaged and cor- (1) EVI � G , related. Some earlier studies [27, 30–32] have shown the NIR + C1Red − C2Blue + L correlation between monthly rainfall data and NDVI. +is NIR − Red led to the determination of the coefficient of correlation (2) NDVI � , NIR + Red between the rainfall data and the vegetation indices as a means of externally validating and/or assessing the values of where NIR, Red, and Blue are the full or partially atmo- NDVI and EVI. We adopted this approach (using coefficient spheric-corrected surface reflectances and C1 and C2 are the of correlation) most especially because of the lack of ter- coefficients of the aerosol resistance term (which uses blue restrially acquired forest inventory data in the study area that band to correct for aerosol influences in the red band)’ could further validate the MODIS-derived values of NDVI C1 � 6; C2 � 7.5. L is the canopy background adjustment for and EVI. As pointed out in those earlier studies [27, 30–32], correcting the nonlinear, differential NIR, and red radiant which is a means of interpreting the results, NDVI is not transfer through a canopy; L � 1. immediately responsive to rainfall but tends to lag behind rainfall by one or two months. +us, rainfall in the con- current month plus two or more antecedent months were 2.3. Meteorological Data. +e monthly rainfall data of the period under study were obtained from the Data Manage- included and examined to determine the correlation with NDVI. ment Unit of the Nigerian meteorological Agency (NIMET) +e rainfall data and NDVI and EVI values corre- for the purpose of carrying out trend analysis in relation to sponding to each of the four seasons (winter, spring, the vegetation indices. Casella and Splayed manual rain summer, and autumn) of the year were extracted and gauge was the instrument used by NIMET for measuring the International Journal of Forestry Research 5 averaged so as to assess the seasonal variation of the veg- 0.271 (Winter; Lower Enyong) to 0.466 (Autumn; Ekinta etation indices, thereby determining the health of the forest River). reserves seasonally. +is was done by averaging the rainfall and NDVI and EVI values of the various months (December, 3.3. Yearly Mean Values of Rainfall Data and Vegetation January, and February (winter); March, April, and May Indices. +e yearly mean values of rainfall, NDVI, and EVI (Spring); June, July, and August (Summer), and September, as computed are shown in Table 2. October, and November (Autumn)) corresponding to the For 2010, NDVI ranges from 0.404 (Gabu) to 0.555 four seasons. (Cross River North) while EVI ranges from 0.286 (Gabu) to Assuming all other factors that affect or influence 0.423 (Ikom). For 2014, NDVI ranges from 0.457 (Gabu) to vegetation are constant, it is expected that the estimated 0.618 (Ikom) while EVI ranges from 0.293 (Yache) to 0.464 mean values of vegetation indices should remain the same (Ikom). For 2018, NDVI ranges from 0.433 (Yache) to 0.603 or increase from year to year, thereby showing im- (Ikom) while EVI ranges from 0.277 (Gabu) to 0.438 (Ikom). provement in the health of the forest reserves. One of the ways of interpreting the estimated mean values of the vegetation indices is to determine their percentage change 3.4. Correlation Coefficients of the Monthly Data. Table 3 lists (equation (3)) over the years (2010 to 2014, 2014 to 2018, the various correlation coefficients of the parameters and 2010 to 2018). +e percentage change is calculated as (rainfall, NDVI, and EVI) that were examined to determine follows: the health condition of the fifteen forest reserves under study. Corel_RF_NDVI, Corel_RF_EVI, and Cor- current year − previous year percentage change � × 100, el_NDVI_EVI in Table 3 mean correlation coefficients be- previous year tween rainfall and NDVI, correlation coefficients between (3) rainfall and EVI, and correlation coefficients between NDVI and EVI, respectively. where current year � estimated yearly mean values of the vegetation indices of the current years (2014 and 2018) and previous year � estimated yearly mean values of the vege- 4. Discussion tation indices of the previous years (2010 and 2014). Based on the index scale of different land areas for vegetation If the percentage change is positive, it means that the greenness [13, 26], the obtained values (Figures 3 and 4) forest reserve in question improved in health but if other- indicate that some of the forest reserves are moderately wise, then, such forest reserve deteriorated in health within healthy while some are stressed. +e various reasons why that period. some of these forest reserves are being stressed have been pointed out in [1, 3, 4]. +e major reasons for the degra- 3. Results dation of these forest reserves are deforestation and com- peting land uses. +ere is no consistent pattern between the 3.1. Monthly Mean Values of Rainfall Data and Vegetation values of monthly rainfall data and the vegetation indices for Indices. +e results of the monthly rainfall data, NDVI, and the years 2010, 2014, and 2018. For the multitemporal years EVI for each of the years are shown in Figure 3. +e results of (2010, 2014, and 2018) under study, the overall trend in the monthly NDVI and EVI thematic output maps for years monthly maximum NDVI is positive over the study area 2010, 2014, and 2018 are also shown in Figure 4. Generally, showing the greening of the forest reserves with a net in- the monthly rainfall values range from 0 (December, Jan- crease in biomass production during the period. +is is uary, and February) to 611.4 mm (June) for year 2010; 0 consistent with earlier study over a large portion of the Sahel (December and January) to 716.2 mm (July) for year 2014, region [23]. and 0 (January) to 780.2 mm (August) for year 2018. It was observed (Table 1) that there is a general increase in the mean values of vegetation indices from winter to 3.2. Seasonal Variation of Rainfall Data and Vegetation autumn, and this confirms that the more the rainfall during Indices. Seasonal variation of each of the fifteen forest re- the preceding season the more the greenness of vegetation serves with rainfall, NDVI, and EVI of years 2010, 2014, and during the following season [23, 30, 31]. 2018 is appended (Table S1, Table S2, and Table S3), but an Table 2 shows that most of the forest reserves are moderately healthy but some (Gabu, Ikrigon, and Yache) are overview is shown in Table 1. In 2010, the NDVI values at all the four seasons range stressed, judged from the yearly mean values of the vege- from 0.246 (Summer; Ekinta River and Obieze-Isu) to tation indices obtained. Overall, the obtained mean values of 0.646 (Autumn; Ekinta River) while EVI values are from the vegetation indices show the yearly chlorophyll content of 0.201 (Winter; Gabu) to 0.517 (Autumn; Ikom). In 2014, each of the fifteen forest reserves. +ey were expected to the NDVI values at all the four seasons range from 0.315 remain the same or increase on a yearly basis assuming all (Summer; Ekinta River) to 0.675 (Spring; Lower Enyong) factors that affect chlorophyll content are constant. while EVI values are from 0.194 (Winter; Gabu) to 0.496 +ere is relative agreement between the values ob- (Summer; Ikom). In 2018, the NDVI values at all the four tained from the percentage change (Table 4) in NDVI and seasons range from 0.431 (Summer; Oban group) to those of EVI within the period of 2010–2014, but a general 0.664 (Autumn; Ekinta River) while EVI values are from disagreement (which could be as a result of high 6 International Journal of Forestry Research (O) Achara Ihe (O) Achara Ihe 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (B) Afi River (B) Afi River 0.8 600 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (D) Yache (D) Yache 0.8 600 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (H) Agoi (H) Agoi 0.8 800 0.7 700 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 Figure 3: Continued. NDVI/EVI NDVI/EVI NDVI/EVI NDVI/EVI Rainfall (mm) Rainfall (mm) Rainfall (mm) Rainfall (mm) International Journal of Forestry Research 7 (F) Cross River North (F) Cross River North 0.9 600 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (G) Cross River South (G) Cross River South 0.9 600 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (J) Ekinta River (J) Ekinta River 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (E) Gabu (E) Gabu 0.8 600 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 Figure 3: Continued. NDVI/EVI NDVI/EVI NDVI/EVI NDVI/EVI Rainfall (mm) Rainfall (mm) Rainfall (mm) Rainfall (mm) 8 International Journal of Forestry Research (A) Ikom (A) Ikom 0.8 600 0.7 0.6 0.5 0.4 300 0.3 0.2 0.1 0 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (C) Ikrigon (C) Ikrigon 0.8 600 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (N) Lower Enyong (N) Lower Enyong 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (I) Oban group (I) Oban group 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 Figure 3: Continued. NDVI/EVI NDVI/EVI NDVI/EVI NDVI/EVI Rainfall (mm) Rainfall (mm) Rainfall (mm) Rainfall (mm) International Journal of Forestry Research 9 (K) Obieze-Isu (K) Obieze-Isu 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (M) Umon Ndealichi (M) Umon Ndealichi 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (L) Uwet Odot (L) Uwet Odot 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 Figure 3: Monthly rainfall, NDVI, and EVI data for each of the three years. sensitivity of NDVI to soil background and atmospheric as expected and confirmed in other studies. +ere is a effects) was observed between the values within the period general negative correlation between monthly rainfall data of 2014–2018 and 2010–2018. +e greening of the forest and the vegetation indices (NDVI and EVI) indicating that reserves (except Afi River) increased within 2010–2014 an increase in monthly rainfall does not directly increase the showing an improvement in their health conditions. value of the NDVI in that particular month [27, 30–32]. +e Within 2014–2018, most of the forest reserves (except Afi lower correlation coefficients between monthly NDVI and River and Ekinta River) deteriorated in health, and many rainfall observed in this study were earlier confirmed by of them (except Cross River North, Cross River South, Herrmann et al. [23] who stated that “areas within the Ekinta River, Ikom, and Obieze-Isu) also deteriorated in semiarid zone where moisture availability is more a function health between 2010 and 2018 depicting negative changes of exogenous stream flow, such as the Niger Delta, stand out in their percentages. of the zonal pattern by their lower correlation coefficients.” Generally, in Table 3, NDVI and EVI values of each of At this juncture, we have to point out that the limitations the forest reserves have high positive correlation coefficients of this study are majorly lack of terrestrially acquired forest NDVI/EVI NDVI/EVI NDVI/EVI Rainfall (mm) Rainfall (mm) Rainfall (mm) 10 International Journal of Forestry Research 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , MAY 2010 EVI, MAY 2014 EVI, MAY 2018 EVI , MAR 2010 EVI, MAR 2014 EVI, MAR 2018 E E E E E D D D D D E C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O O O O I O I I O I I I K K K K K K M M M M M M N N N N L N L L L N L L J J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, MAY 2010 NDVI, MAY 2014 NDVI, MAY 2018 NDVI, MAR 2010 NDVI, MAR 2014 NDVI, MAR 2018 E E E E E D D E D D D D C C C C C C B B B B B B A A A A A A F F F F F G F G G G G G H H H H H H O I O O I O O O I I I I K K M K K K M K M M M M N N L N L N N L N L L L J J J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , JAN 2010 EVI, JAN 2014 EVI, JAN 2018 EVI , FEB 2010 EVI, FEB 2014 EVI, FEB 2018 E E D D E E D E D D D C C C C C C B B B B A A A A F F G F F F G G G G G H H H H H O I O I I O I O I K K K I K K M M M K M M N N N M L L L N N N L L L J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, JAN 2010 NDVI, JAN 2014 NDVI, JAN 2018 NDVI, FEB 2010 NDVI, FEB 2014 NDVI, FEB 2018 E E D D D E E E D D C C C C B B B B A A A A A F F G G F F G F G H H H H H H O I O I O I O I O K K I K M I M K M K M K M N N M N L L L N L N L N L J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , APRIL 2010 EVI, APRIL 2014 EVI, APRIL 2018 EVI , JUN 2010 EVI, JUN 2014 EVI, JUN 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O I O I O I O I O O I I K K K K K K M M M M M M N N N N N N L L L L L L J J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, APRIL 2010 NDVI, APRIL 2014 NDVI, APRIL 2018 NDVI, JUN 2010 NDVI, JUN 2014 NDVI, JUN 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F G F G G G G G H H H H H H O I O O I O O I O I I I K K K K K M K M M M M M N N N N N L L N L L L L J J J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 Figure 4: Continued. 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 International Journal of Forestry Research 11 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , JUL 2010 EVI, JUL 2014 EVI, JUL 2018 EVI , AUG 2010 EVI, AUG 2014 EVI, AUG 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O I O O I O I O O I I I K K K K K K M M M M M M N N N L N L N L N L L L J J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, JUL 2010 NDVI, JUL 2014 NDVI, JUL 2018 NDVI, AUG 2010 NDVI, AUG 2014 NDVI, AUG 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F G F G G G G G H H H H H H O I O O I O O O I I I I K M K K M K K M K M M M N L N L N L N L N L N L J J J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , SEP 2010 EVI, SEP 2014 EVI, SEP 2018 EVI , OCT 2010 EVI, OCT 2014 EVI, OCT 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O O O O I O I I O I I I K K K K K K M M M M M M N N N N N N L L L L L L J J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, SEP 2010 NDVI, SEP 2014 NDVI, SEP 2018 NDVI, OCT 2010 NDVI, OCT 2014 NDVI, OCT 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O I O O I O O I O I I I K K K K K M K M M M M M N N N N N L L N L L L L J J J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , NOV 2010 EVI, NOV 2014 EVI, NOV 2018 EVI , DEC 2010 EVI, DEC 2014 EVI, DEC 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O I O I O I O I O O I I K K K K K K M M M M M M N N N L N N N L L L L L J J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, NOV 2010 NDVI, NOV 2014 NDVI, NOV 2018 NDVI, DEC 2010 NDVI, DEC 2014 NDVI, DEC 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O O O O O I O I I I I I K K K K K M K M M M M M N L N N L N N L L N L L J J J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 Figure 4: Monthly NDVI and EVI thematic output maps for each of the three years. 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 12 International Journal of Forestry Research Table 1: Yearly seasonal variation of vegetation indices. Minimum Maximum Mean Season Year NDVI EVI Rainfall NDVI EVI Rainfall NDVI EVI Rainfall 2010 0.303 0.179 0.000 0.684 0.437 89.000 0.529 0.33584 35.644 Winter 2014 0.325 0.165 0.000 0.750 0.497 61.600 0.549 0.35602 21.602 2018 0.249 0.126 0.000 0.736 0.454 149.600 0.508 0.29702 46.456 2010 0.264 0.180 25.500 0.741 0.568 446.800 0.528 0.410 180.864 Spring 2014 0.354 0.226 79.300 0.780 0.550 411.200 0.585 0.419 259.751 2018 0.322 0.209 39.500 0.772 0.528 209.200 0.562 0.377 161.740 2010 0.094 0.110 141.400 0.763 0.624 611.400 0.322 0.281 395.240 Summer 2014 0.103 0.124 149.800 0.790 0.555 716.200 0.437 0.344 371.711 2018 0.159 0.110 169.300 0.778 0.479 780.200 0.465 0.327 421.656 2010 0.295 0.271 26.600 0.774 0.620 555.200 0.530 0.386 305.247 Autumn 2014 0.210 0.184 104.400 0.744 0.552 501.500 0.534 0.362 282.807 2018 0.398 0.229 107.200 0.758 0.545 557.600 0.601 0.395 321.033 Table 2: Yearly rainfall data and vegetation indices. Rainfall NDVI EVI Forest reserves 2010 2014 2018 2010 2014 2018 2010 2014 2018 Afi River 190.08 194.65 236.12 0.539 0.504 0.53 0.385 0.348 0.349 Achara Ihe 260.43 274.58 255.85 0.485 0.566 0.548 0.38 0.4 0.37 Agoi 260.43 274.58 255.85 0.501 0.553 0.54 0.382 0.401 0.359 Cross River North 190.08 194.65 236.12 0.555 0.587 0.603 0.406 0.412 0.409 Cross River South 190.08 194.65 236.12 0.476 0.548 0.559 0.351 0.381 0.362 Ekinta River 260.43 274.58 255.85 0.45 0.469 0.561 0.344 0.354 0.377 Gabu 202.43 169.83 169.22 0.404 0.457 0.437 0.286 0.294 0.277 Ikom 190.08 194.65 236.12 0.524 0.618 0.603 0.423 0.464 0.438 Ikrigon 190.08 194.65 236.12 0.434 0.479 0.475 0.312 0.32 0.306 Lower Enyong 260.43 274.58 255.85 0.484 0.545 0.54 0.335 0.359 0.315 Oban group 260.43 274.58 255.85 0.491 0.495 0.537 0.359 0.357 0.338 Obieze-Isu 260.43 274.58 255.85 0.41 0.543 0.512 0.318 0.384 0.329 Umon Ndealichi 260.43 274.58 255.85 0.499 0.553 0.565 0.37 0.396 0.363 Uwet Odot 260.43 274.58 255.85 0.493 0.544 0.567 0.36 0.388 0.355 Yache 202.43 169.83 169.22 0.415 0.433 0.433 0.29 0.293 0.286 Table 3: Correlation coefficients for monthly data. Corel_RF_NDVI Corel_RF_EVI Corel_NDVI_EVI Forest reserves 2010 2014 2018 2010 2014 2018 2010 2014 2018 Ikom 0.416 0.172 −0.211 0.589 0.661 0.059 0.931 0.598 0.729 Afi River 0.13 −0.27 −0.546 0.326 −0.056 −0.503 0.854 0.921 0.98 Ikrigon 0.096 0.228 0.043 0.585 0.555 0.289 0.719 0.883 0.948 Yache 0.386 0.061 0.285 0.715 0.435 0.508 0.881 0.799 0.954 Gabu 0.421 0.584 0.391 0.801 0.914 0.619 0.833 0.752 0.941 Cross River North 0.294 0.007 −0.127 0.658 0.148 0.136 0.769 0.909 0.902 Cross River South −0.139 −0.072 −0.122 0.171 0.101 0.171 0.89 0.876 0.911 Agoi −0.436 −0.72 −0.414 −0.062 −0.625 −0.189 0.823 0.976 0.885 Oban group −0.633 −0.827 −0.79 −0.433 −0.715 −0.609 0.903 0.954 0.933 Ekinta River −0.493 −0.764 −0.388 −0.33 −0.616 −0.261 0.926 0.958 0.975 Obieze-Isu −0.665 −0.677 −0.519 −0.581 −0.534 −0.322 0.929 0.936 0.874 Uwet Odot −0.559 −0.755 −0.568 −0.405 −0.603 −0.382 0.889 0.927 0.923 Lower Enyong −0.306 −0.738 −0.538 −0.338 −0.635 −0.581 0.912 0.931 0.832 Achara Ihe −0.404 −0.604 −0.093 −0.29 −0.47 0.16 0.94 0.95 0.85 Umon Ndealichi −0.373 −0.763 −0.551 −0.103 −0.669 −0.389 0.808 0.96 0.909 International Journal of Forestry Research 13 Table 4: Percentage change of vegetation indices. NDVI (% change) EVI (% change) Forest reserves 2010–2014 2014–2018 2010–2018 2010–2014 2014–2018 2010–2018 Afi River −6.49 5.16 −2.34 −9.61 0.29 −9.35 Achara Ihe 16.70 −3.18 16.58 5.26 −7.50 −2.63 Agoi 10.38 −2.35 10.21 4.97 −10.47 −6.02 Cross River North 5.77 2.73 11.82 1.48 −0.73 0.74 Cross River South 15.13 2.01 23.65 8.55 −4.99 3.13 Ekinta River 4.22 19.62 32.27 2.91 6.50 9.59 Gabu 13.12 −4.38 11.54 2.80 −5.78 −3.15 Ikom 17.94 −2.43 18.68 9.69 −5.60 3.55 Ikrigon 10.37 −0.84 13.14 2.56 −4.38 −1.92 Lower Enyong 12.60 −0.92 16.72 7.16 −12.26 −5.97 Oban group 0.81 8.48 12.81 −0.56 −5.32 −5.85 Obieze-Isu 32.44 −5.71 32.08 20.75 −14.32 3.46 Umon Ndealichi 10.82 2.17 17.84 7.03 −8.33 −1.89 Uwet Odot 10.34 4.23 20.56 7.78 −8.51 −1.39 Yache 4.34 0.00 6.21 1.03 −2.39 −1.38 inventory data and unavailability of long-standing and each of the fifteen forest reserves, indicate that some of standardized forest health inventory programs that could the forest reserves are moderately healthy while some are have further validated the obtained results, but the obtained greatly stressed. +e greening of the forest reserves as results corroborated the findings of these studies [1, 3, 4] shown in positive trends in NDVI indicates a net increase which show that some of the forest reserves are greatly in biomass production during the period under study. stressed. Data Availability 5. Conclusion +e MODIS data used to support the findings of the study are publicly available. Due to several underlying factors that determine vegetation health, assessing forest health is a complex process. However, in this study, we assessed the health of fifteen Conflicts of Interest forest reserves in Cross River State, Nigeria, using the meteorological data and MOD13A1-derived Normalized +e authors declare that they have no conflicts of interest. Difference Vegetation Index (NDVI) and Enhanced Veg- etation Index (EVI) for the years 2010, 2014, and 2018. Supplementary Materials Monthly mean values of the vegetation indices (NDVI and EVI) were estimated for each of the forest reserves. +e Table S1 shows the seasonal variation of rainfall, NDVI, relationship between the vegetation indices and rainfall and EVI of year 2010 for the fifteen forest reserves under data was analyzed by computing correlation coefficients. To study. It is observed that there is a general increase in the assess the health of each of the fifteen forest reserves, mean values of vegetation indices as we move from percentage change between the estimated mean values of Winter to Autumn, and this confirms that the more the the vegetation indices and seasonal variation of vegetation rainfall during the preceding season the more the indices was determined on yearly basis for the period of greenness of vegetation during the following season. study. Table S2 shows the seasonal variation of rainfall, NDVI, +e computed monthly mean values of NDVI and and EVI of year 2014 for the fifteen forest reserves under EVI range from 0.094 to 0.790 and from 0.11 to 0.624, study. It is observed that there is a general increase in the respectively, while rainfall data range from 0 to mean values of vegetation indices as we move from 780.2 mm/month within the period of study. +e com- Winter to Autumn, and this confirms that the more the puted yearly mean values of NDVI and EVI range from rainfall during the preceding season the more the 0.404 to 0.618 and from 0.277 to 0.464, respectively, greenness of vegetation during the following season. while rainfall data range from 169.22 to 274.58 mm/ Table S3 shows the seasonal variation of rainfall, NDVI, month within the period of study. Similarly, the corre- and EVI of year 2018 for the fifteen forest reserves under lation coefficients between monthly rainfall data and study. It is observed that there is a general increase in the monthly mean values of NDVI and EVI varied from mean values of vegetation indices as we move from −0.827 to 0.584 and −0.715 to 0.914. Equally, the rela- Winter to Autumn, and this confirms that the more the tionship between NDVI and EVI ranges from 0.598 to rainfall during the preceding season the more the 0.980. +e obtained mean values of the vegetation in- greenness of vegetation during the following season. dices, which are measures of the chlorophyll content of (Supplementary Materials) 14 International Journal of Forestry Research and EVI for seasonal crop yield forecasting at the ecodistrict References scale,” Remote Sensing, vol. 6, no. 10, pp. 10193–10214, 2014. [1] O. O. O. Enuoh and F. E. Bisong, “Colonial forest policies and [16] P.-Y. Chen, G. Fedosejevs, M. Tiscareño-LoPez, ´ and tropical deforestation: the case of Cross River State, Nigeria,” J. G. Arnold, “Assessment of MODIS-EVI, MODIS-NDVI Open Journal of Forestry, vol. 5, no. 1, pp. 66–79, 2015. and VEGETATION-NDVI composite data using agricultural [2] J. O’Laughlin, R. 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Assessing the Health of Akamkpa Forest Reserves in Southeastern Part of Nigeria Using Remote Sensing Techniques

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Hindawi International Journal of Forestry Research Volume 2020, Article ID 8739864, 15 pages https://doi.org/10.1155/2020/8739864 Research Article Assessing the Health of Akamkpa Forest Reserves in Southeastern Part of Nigeria Using Remote Sensing Techniques Elijah S. Ebinne, Ojima I. Apeh , Raphael I. Ndukwu, and Edebo J. Abah Department of Geoinformatics & Surveying, University of Nigeria, Enugu Campus, Enugu, Nigeria Correspondence should be addressed to Ojima I. Apeh; ojima.apeh@unn.edu.ng Received 18 November 2019; Revised 10 June 2020; Accepted 20 June 2020; Published 27 July 2020 Academic Editor: Nikolaos D. Hasanagas Copyright © 2020 Elijah S. Ebinne et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Assessment of forest health is very vital because forests form the largest terrestrial ecosystems on earth. +e greenness of vegetation is one of the essential factors used in evaluating the health of forest reserves. +is study is aimed at assessing the health of fifteen forest reserves in Southeastern part of Nigeria using meteorological data and MOD13A1-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Related portions of the monthly MOD13A1 data, derived for the years 2010, 2014, and 2018, were downloaded, and the monthly mean values of the vegetation indices (NDVI and EVI) were estimated for each of the forest reserves using the Spatial Analysis Module in ArcGIS software. +e computed monthly mean values of NDVI range from 0.094 to 0.790 while that of EVI ranges from 0.11 to 0.624 and the rainfall data range from 0 to 780.2 mm/month within the period of study. Analyses of the correlation coefficients between monthly rainfall data and NDVI, monthly rainfall data and EVI, and that of NDVI and EVI range from −0.827 to 0.584; −0.715 to 0.914, and 0.598 to 0.980. +e obtained results indicate that some of the forest reserves are moderately healthy while some areas are under great stress. We can conclude that satellite remote sensing is a veritable tool in the assessment, management, and monitoring of forest health especially where there is little or no terrestrially acquired forest inventory data. but their acquisition is time-consuming, laborious, and cost- 1. Introduction intensive. To worsen the matter, there is paucity of long- Forest health in Cross River State (a State in Southeastern standing and standardized forest health inventory programs Nigeria) is threatened by so many factors such as colonial in the study area, thereby necessitating the use of remote nationalization and commodification of the forest estate, sensing techniques to assess the health of the forest reserves agricultural practices, government plantations and defor- in Cross River State following the outcry by [1] and the estation, uncontrolled extraction of nontimber forest reports from United Nations Programme [3, 4]. +is lack of products (NTFPs), highway construction and mining of terrestrially acquired forest inventory data makes it im- solid minerals, dereservation of large portions of some possible to integrate satellite remote sensing techniques with government forest reserves, and foresters and resistance to past inventory data in assessing the health of these forest decentralized forest management [1]. Forest health is a reserves under study in contrary to what is obtainable in condition of forest ecosystem that sustains their complexity some other studies [5–7]. +e Normalized Difference while providing for human needs [2]. Healthy forests, which Vegetation Index (NDVI) and Enhanced Vegetation Index could be assessed and monitored by many forest health (EVI) derived from MODIS (moderate resolution imaging indicators, are needed for aesthetical pleasure, satisfaction of spectroradiometer) coupled with meteorological data have human needs, and maintenance of sustainable ecosystem. been severally used to determine the greenness (which is an It is unarguable that the indicators of forest health indicator of healthy condition) of vegetation in many lo- obtained from forest inventory programs are more accurate calities [8–13]. 2 International Journal of Forestry Research decrease or increase in the value of NDVI and EVI. Holding Absorption and reflection of photosynthetically active radiation [14] over a given period of time, which is shown by all other factors that affect vegetation constant, a healthy forest reserve should yearly improve in greenness meaning the vegetation indices, can be used to assess the health of forest reserves. NDVI and EVI are vegetation indices used that the estimated mean values of the vegetation indices for measuring the greenness of the vegetation. +ey range should remain the same or increase from year to year. from −1 to +1, and they are very useful in monitoring and Additionally, only positive values of NDVI should corre- understanding environmental and climatic changes. Vegeta- spond to vegetated zones like the forest reserves [23]. +e tion indices are radiometric measures of photosynthetically objectives of this study were to (1) compute monthly mean active radiation absorbed by chlorophyll in the green leaves of values of NDVI and EVI from multitemporal MODIS data for each of the fifteen forest reserves; (2) determine corre- vegetation canopies and are therefore good surrogate mea- sures of the physiologically functioning surface greenness level lation coefficients between the rainfall data and the vege- tation indices; (3) determine the yearly seasonal variation of of a region (https://climatedataguide.ucar.edu/climate-data/ ndvi-normalized-difference-vegetation-index-3rd-generation- vegetation indices; and (4) analyze the computed parameters in order to assess the health of each of the fifteen forest nasagfsc-gimms). +e MODIS-derived vegetation indices (NDVI and EVI) reserves. +is study is very important in that it will help in have been severally used to analyze the spatiotemporal determining whether the forest reserves are actually dete- trends over Southern Asia [8]; extract and understand the riorating or improving in health and can be applied in the phenological information about evergreen, semievergreen, monitoring and management of natural resources against moist deciduous, and dry deciduous vegetation in India [9]; artificial and natural environmental hazards. forecast the seasonal crop yield in Canada [15]; identify the corn growth in Western Mexico [16]; estimate the forest 2. Materials and Methods productivity and dieback in a Mediterranean holm oak forest [10]; assess the seasonal and potential canopy photosynthesis +e designed workflow of this study is shown in Figure 1. in relation to seasonal changes in Leaf Area Index (LAI), chlorophyll concentration, and air temperatures of NE Argentina subtropical forests [11]; quantify the drought 2.1. Study Site. Cross River State is a coastal State in intensity and its geographical effects in order to support the Southeastern Nigeria and also one of the states within the drought monitoring applications [17]; investigate the bio- Niger Delta region. +e tropical climate of Cross River State mass of forest reserve and the periods of vegetation [12]; has an average annual temperature and rainfall as 26.1 C and ° ° depict the quality and relative health of a forest reserve [13]; 2750 mm, respectively. It lies within latitudes 4 48′45″N–6 52′ ° ° determine the onset of phenophases in spring and autumn 05″N and longitudes 7 49′ 35″E–9 22′05″E. In Cross River and also to assess forest growth and health condition [18]; State, there are four synoptic stations of the Nigerian Mete- evaluate the sensitivity of MODIS-based vegetation index to orological Agency strategically located in different regions heat and drought stress in temperate forests [19]; and (Calabar, Ikom, Obudu, and Ogoja) within the state. +e monitor the trends and changes in vegetation and evaluate coordinates (longitude and latitude) of the four meteorological the proxies for drought conditions [20]. stations where the rainfall data were obtained from are given as ° ° ° ° ° ° Several other factors can affect forest health and vitality Calabar (8.35 , 4.97 ), Ikom (8.72 , 5.97 ), Ogoja (8.80 , 6.70 ), ° ° which include abiotic factors (wild or human-induced fires, and Obudu (9.10 , 6.61 ). pollution, floods, nutrients, and extreme weather conditions Cross River State has rich natural resources, and among such as storms, hurricanes, droughts, snow, frost, wind, and them are huge forest reserves, cutting across various local sun); biotic factors (insect pests, diseases and invasive government areas of the State. Fifteen of these forest reserves species and can either consist of fungi, plants, animal, or (Afi River, Achara Ihe, Agoi, Cross River North, Cross River bacteria); and human factors (overexploitation, competing South, Ekinta River, Gabu, Ikom, Ikrigon, Lower Enyong, land uses, and poor harvesting techniques or management Oban Group, Obieze-Isu, Umon Ndealichi, Uwet Odot, and can negatively impact forest ecosystems) [21]. +e major Yache) are considered, and they are referred to as Akamkpa reasons for the degradation of the forest reserves under study forest reserves in this study. Figure 2 shows the map of Cross are deforestation and competing land uses. It is true that the River State and the fifteen forest reserves evaluated in this mechanism that determines forest health is complex [22], study. but in this study, we have chosen to assess the forest health +e major activities affecting the health of these forest by estimating the greenness (which is a direct measure of reserves are deforestation, logging and timber extraction, photosynthetic potential resulting from the composite establishment of tree-crop agricultural plantations (e.g., property of total leaf chlorophyll, leaf area, canopy cover, cocoa, rubber, oil palm, cashew, and gmelina) using the and canopy architecture) of vegetation using vegetation taungya system, construction of highways (e.g., Calabar- indices derived from MODIS data for the years 2010, 2014, Ikom-Ogoja road, Ogoja-Obudu-Ikom road, Calabar-Oban- and 2018 coupled with monthly rainfall data of the study Ekang road, Calabar-Itu-Ikot Ekpene road, and Ikang- area. Calabar), mining of solid minerals, farming, extraction of +e premise of the study is that NDVI and EVI are nontimber forest products (e.g., wild edible vegetables, wild indicators of vegetation health since deterioration and im- edible fruits, medicinal plants, fuel wood, building materials, provement of ecosystem in vegetation is often reflected in a and arts and craft materials), dereservation of large areas of International Journal of Forestry Research 3 Data Monthly Daily MODIS rainfall data data Subsetting of Averaging data Scaling of data Monthly rainfall data Zonal statistical analysis Statistical analyses Monthly mean values of NDVI/EVI Health of forest reserves Figure 1: Schematic workflow of the study. Figure 2: Representation of the location site used in the present research. 4 International Journal of Forestry Research certain forest reserves, and rapid urbanization [1, 3]. monthly rainfall data, and its unit is in millimetres (mm) According to [4], “the main threats to the forest reserves (https://www.nimet.gov.ng/). include subsistence and commercial agriculture, fuel wood collection (firewood and charcoal), overharvesting of timber (illegal), forest fires, settlements, and infrastructure devel- 2.4. Data Analysis and Interpretation. +e monthly mean values of the vegetation indices were thoroughly examined opment.” Exotic tree species like teak and gmelina arborea are found in some of these forest reserves. to see if they are within the range of low or moderate or high vegetation so as to determine the health condition of each of the forest reserves. Vegetation greenness is shown 2.2. Acquisition and Processing of Satellite Data. +e monthly by the mean values of the monthly NDVI. Very low values data of MODIS (MOD13A1) of years 2010, 2014, and 2018 (≤0.1) of NDVI represent the barren areas of rock, sand, or were downloaded from NASA website. MODIS images are snow while moderate values (0.2 to 0.3) represent shrub distributed as hierarchical data format (HDF) with tiles of and grassland and high values (0.6 to 0.8) represent 10 ×10 arc degree and projected in the sinusoidal projection. temperate and tropical rainforests [13, 26]. According to +e geometric correction was applied to reproject the raw these studies [26, 27] mean NDVI for deciduous and ev- data into UTM WGS84 datum. +is study used the 16-bit ergreen forests should range from 0.42 to 0.51 and 0.52 to signed integer MOD13A1 for vegetation indices. +e blue, 0.69, respectively. As revealed in an earlier study over a red, and near-infrared reflectance (centred at 469 nano- large portion of the Sahel region [23], only positive values meters, 645 nanometers, and 858 nanometers, respectively) of NDVI should correspond to vegetated zones such as the were used to determine the MODIS daily vegetation indices. forest reserves. +e higher the index, the greater the +ree different composite sets (8-day, 16-day, and dual-8- chlorophyll content of the forest reserves and the healthier day composite periods) of 12-month time series MOD- the forest reserves. IS_500 m NDVI and EVI data spanning from January to EVI is directly related to the NDVI and gives more December for years 2010, 2014, and 2018 were computed weight to the near-infrared band. +e NDVI and EVI are using the Spatial Analysis Module in ArcGIS software in very similar, with the NDVI being directly related to primary order to analyze seasonal vegetation dynamics and corre- production and the EVI being more heavily weighted to the lation with meteorological data. Mapping Leaf Area Index in very dense plant canopies [28]. +e data were imported into ArcGIS 10.1 software for +e advantage of using EVI is that it uses additional processing. +e boundary shapefile of Cross River State was wavelengths of light to correct for variations in solar inci- used to subset the global data. +e boundaries of the fifteen dence angle, atmospheric conditions like distortions in the forest reserves were extracted from the forest reserve map reflected light caused by the particles in the air, and signals obtained from the State Forestry Commission. +e Spatial from the ground cover below the vegetation. NDVI is highly Analyst Module in ArcGIS Software was used to estimate the sensitive to soil background and atmospheric effects, while monthly mean values of NDVI and EVI of the fifteen forest EVI is less sensitive to them, thereby making it to provide a reserves. +e resulting table was then saved in Microsoft better quantification of vegetation abundance and physio- Excel for further statistical analyses. +e obtained values of logical activity [24, 29]. Overall, the correlation coefficient NDVI and EVI were multiplied by the scale factor of 0.0001. between NDVI and EVI is expected to be highly positive. +e equations used for computing EVI (equation (1)) and In order to allow for proper assessment of the health of NDVI (equation (2)) are [24, 25]: the fifteen forest reserves, the monthly rainfall data and the NIR − Red monthly values of NDVI and EVI were averaged and cor- (1) EVI � G , related. Some earlier studies [27, 30–32] have shown the NIR + C1Red − C2Blue + L correlation between monthly rainfall data and NDVI. +is NIR − Red led to the determination of the coefficient of correlation (2) NDVI � , NIR + Red between the rainfall data and the vegetation indices as a means of externally validating and/or assessing the values of where NIR, Red, and Blue are the full or partially atmo- NDVI and EVI. We adopted this approach (using coefficient spheric-corrected surface reflectances and C1 and C2 are the of correlation) most especially because of the lack of ter- coefficients of the aerosol resistance term (which uses blue restrially acquired forest inventory data in the study area that band to correct for aerosol influences in the red band)’ could further validate the MODIS-derived values of NDVI C1 � 6; C2 � 7.5. L is the canopy background adjustment for and EVI. As pointed out in those earlier studies [27, 30–32], correcting the nonlinear, differential NIR, and red radiant which is a means of interpreting the results, NDVI is not transfer through a canopy; L � 1. immediately responsive to rainfall but tends to lag behind rainfall by one or two months. +us, rainfall in the con- current month plus two or more antecedent months were 2.3. Meteorological Data. +e monthly rainfall data of the period under study were obtained from the Data Manage- included and examined to determine the correlation with NDVI. ment Unit of the Nigerian meteorological Agency (NIMET) +e rainfall data and NDVI and EVI values corre- for the purpose of carrying out trend analysis in relation to sponding to each of the four seasons (winter, spring, the vegetation indices. Casella and Splayed manual rain summer, and autumn) of the year were extracted and gauge was the instrument used by NIMET for measuring the International Journal of Forestry Research 5 averaged so as to assess the seasonal variation of the veg- 0.271 (Winter; Lower Enyong) to 0.466 (Autumn; Ekinta etation indices, thereby determining the health of the forest River). reserves seasonally. +is was done by averaging the rainfall and NDVI and EVI values of the various months (December, 3.3. Yearly Mean Values of Rainfall Data and Vegetation January, and February (winter); March, April, and May Indices. +e yearly mean values of rainfall, NDVI, and EVI (Spring); June, July, and August (Summer), and September, as computed are shown in Table 2. October, and November (Autumn)) corresponding to the For 2010, NDVI ranges from 0.404 (Gabu) to 0.555 four seasons. (Cross River North) while EVI ranges from 0.286 (Gabu) to Assuming all other factors that affect or influence 0.423 (Ikom). For 2014, NDVI ranges from 0.457 (Gabu) to vegetation are constant, it is expected that the estimated 0.618 (Ikom) while EVI ranges from 0.293 (Yache) to 0.464 mean values of vegetation indices should remain the same (Ikom). For 2018, NDVI ranges from 0.433 (Yache) to 0.603 or increase from year to year, thereby showing im- (Ikom) while EVI ranges from 0.277 (Gabu) to 0.438 (Ikom). provement in the health of the forest reserves. One of the ways of interpreting the estimated mean values of the vegetation indices is to determine their percentage change 3.4. Correlation Coefficients of the Monthly Data. Table 3 lists (equation (3)) over the years (2010 to 2014, 2014 to 2018, the various correlation coefficients of the parameters and 2010 to 2018). +e percentage change is calculated as (rainfall, NDVI, and EVI) that were examined to determine follows: the health condition of the fifteen forest reserves under study. Corel_RF_NDVI, Corel_RF_EVI, and Cor- current year − previous year percentage change � × 100, el_NDVI_EVI in Table 3 mean correlation coefficients be- previous year tween rainfall and NDVI, correlation coefficients between (3) rainfall and EVI, and correlation coefficients between NDVI and EVI, respectively. where current year � estimated yearly mean values of the vegetation indices of the current years (2014 and 2018) and previous year � estimated yearly mean values of the vege- 4. Discussion tation indices of the previous years (2010 and 2014). Based on the index scale of different land areas for vegetation If the percentage change is positive, it means that the greenness [13, 26], the obtained values (Figures 3 and 4) forest reserve in question improved in health but if other- indicate that some of the forest reserves are moderately wise, then, such forest reserve deteriorated in health within healthy while some are stressed. +e various reasons why that period. some of these forest reserves are being stressed have been pointed out in [1, 3, 4]. +e major reasons for the degra- 3. Results dation of these forest reserves are deforestation and com- peting land uses. +ere is no consistent pattern between the 3.1. Monthly Mean Values of Rainfall Data and Vegetation values of monthly rainfall data and the vegetation indices for Indices. +e results of the monthly rainfall data, NDVI, and the years 2010, 2014, and 2018. For the multitemporal years EVI for each of the years are shown in Figure 3. +e results of (2010, 2014, and 2018) under study, the overall trend in the monthly NDVI and EVI thematic output maps for years monthly maximum NDVI is positive over the study area 2010, 2014, and 2018 are also shown in Figure 4. Generally, showing the greening of the forest reserves with a net in- the monthly rainfall values range from 0 (December, Jan- crease in biomass production during the period. +is is uary, and February) to 611.4 mm (June) for year 2010; 0 consistent with earlier study over a large portion of the Sahel (December and January) to 716.2 mm (July) for year 2014, region [23]. and 0 (January) to 780.2 mm (August) for year 2018. It was observed (Table 1) that there is a general increase in the mean values of vegetation indices from winter to 3.2. Seasonal Variation of Rainfall Data and Vegetation autumn, and this confirms that the more the rainfall during Indices. Seasonal variation of each of the fifteen forest re- the preceding season the more the greenness of vegetation serves with rainfall, NDVI, and EVI of years 2010, 2014, and during the following season [23, 30, 31]. 2018 is appended (Table S1, Table S2, and Table S3), but an Table 2 shows that most of the forest reserves are moderately healthy but some (Gabu, Ikrigon, and Yache) are overview is shown in Table 1. In 2010, the NDVI values at all the four seasons range stressed, judged from the yearly mean values of the vege- from 0.246 (Summer; Ekinta River and Obieze-Isu) to tation indices obtained. Overall, the obtained mean values of 0.646 (Autumn; Ekinta River) while EVI values are from the vegetation indices show the yearly chlorophyll content of 0.201 (Winter; Gabu) to 0.517 (Autumn; Ikom). In 2014, each of the fifteen forest reserves. +ey were expected to the NDVI values at all the four seasons range from 0.315 remain the same or increase on a yearly basis assuming all (Summer; Ekinta River) to 0.675 (Spring; Lower Enyong) factors that affect chlorophyll content are constant. while EVI values are from 0.194 (Winter; Gabu) to 0.496 +ere is relative agreement between the values ob- (Summer; Ikom). In 2018, the NDVI values at all the four tained from the percentage change (Table 4) in NDVI and seasons range from 0.431 (Summer; Oban group) to those of EVI within the period of 2010–2014, but a general 0.664 (Autumn; Ekinta River) while EVI values are from disagreement (which could be as a result of high 6 International Journal of Forestry Research (O) Achara Ihe (O) Achara Ihe 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (B) Afi River (B) Afi River 0.8 600 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (D) Yache (D) Yache 0.8 600 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (H) Agoi (H) Agoi 0.8 800 0.7 700 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 Figure 3: Continued. NDVI/EVI NDVI/EVI NDVI/EVI NDVI/EVI Rainfall (mm) Rainfall (mm) Rainfall (mm) Rainfall (mm) International Journal of Forestry Research 7 (F) Cross River North (F) Cross River North 0.9 600 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (G) Cross River South (G) Cross River South 0.9 600 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (J) Ekinta River (J) Ekinta River 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (E) Gabu (E) Gabu 0.8 600 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 Figure 3: Continued. NDVI/EVI NDVI/EVI NDVI/EVI NDVI/EVI Rainfall (mm) Rainfall (mm) Rainfall (mm) Rainfall (mm) 8 International Journal of Forestry Research (A) Ikom (A) Ikom 0.8 600 0.7 0.6 0.5 0.4 300 0.3 0.2 0.1 0 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (C) Ikrigon (C) Ikrigon 0.8 600 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (N) Lower Enyong (N) Lower Enyong 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (I) Oban group (I) Oban group 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 Figure 3: Continued. NDVI/EVI NDVI/EVI NDVI/EVI NDVI/EVI Rainfall (mm) Rainfall (mm) Rainfall (mm) Rainfall (mm) International Journal of Forestry Research 9 (K) Obieze-Isu (K) Obieze-Isu 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (M) Umon Ndealichi (M) Umon Ndealichi 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 (L) Uwet Odot (L) Uwet Odot 0.8 800 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Months NDVI-2010 EVI-2010 Rain-2010 NDVI-2014 EVI-2014 Rain-2014 NDVI-2018 EVI-2018 Rain-2018 Figure 3: Monthly rainfall, NDVI, and EVI data for each of the three years. sensitivity of NDVI to soil background and atmospheric as expected and confirmed in other studies. +ere is a effects) was observed between the values within the period general negative correlation between monthly rainfall data of 2014–2018 and 2010–2018. +e greening of the forest and the vegetation indices (NDVI and EVI) indicating that reserves (except Afi River) increased within 2010–2014 an increase in monthly rainfall does not directly increase the showing an improvement in their health conditions. value of the NDVI in that particular month [27, 30–32]. +e Within 2014–2018, most of the forest reserves (except Afi lower correlation coefficients between monthly NDVI and River and Ekinta River) deteriorated in health, and many rainfall observed in this study were earlier confirmed by of them (except Cross River North, Cross River South, Herrmann et al. [23] who stated that “areas within the Ekinta River, Ikom, and Obieze-Isu) also deteriorated in semiarid zone where moisture availability is more a function health between 2010 and 2018 depicting negative changes of exogenous stream flow, such as the Niger Delta, stand out in their percentages. of the zonal pattern by their lower correlation coefficients.” Generally, in Table 3, NDVI and EVI values of each of At this juncture, we have to point out that the limitations the forest reserves have high positive correlation coefficients of this study are majorly lack of terrestrially acquired forest NDVI/EVI NDVI/EVI NDVI/EVI Rainfall (mm) Rainfall (mm) Rainfall (mm) 10 International Journal of Forestry Research 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , MAY 2010 EVI, MAY 2014 EVI, MAY 2018 EVI , MAR 2010 EVI, MAR 2014 EVI, MAR 2018 E E E E E D D D D D E C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O O O O I O I I O I I I K K K K K K M M M M M M N N N N L N L L L N L L J J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, MAY 2010 NDVI, MAY 2014 NDVI, MAY 2018 NDVI, MAR 2010 NDVI, MAR 2014 NDVI, MAR 2018 E E E E E D D E D D D D C C C C C C B B B B B B A A A A A A F F F F F G F G G G G G H H H H H H O I O O I O O O I I I I K K M K K K M K M M M M N N L N L N N L N L L L J J J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , JAN 2010 EVI, JAN 2014 EVI, JAN 2018 EVI , FEB 2010 EVI, FEB 2014 EVI, FEB 2018 E E D D E E D E D D D C C C C C C B B B B A A A A F F G F F F G G G G G H H H H H O I O I I O I O I K K K I K K M M M K M M N N N M L L L N N N L L L J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, JAN 2010 NDVI, JAN 2014 NDVI, JAN 2018 NDVI, FEB 2010 NDVI, FEB 2014 NDVI, FEB 2018 E E D D D E E E D D C C C C B B B B A A A A A F F G G F F G F G H H H H H H O I O I O I O I O K K I K M I M K M K M K M N N M N L L L N L N L N L J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , APRIL 2010 EVI, APRIL 2014 EVI, APRIL 2018 EVI , JUN 2010 EVI, JUN 2014 EVI, JUN 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O I O I O I O I O O I I K K K K K K M M M M M M N N N N N N L L L L L L J J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, APRIL 2010 NDVI, APRIL 2014 NDVI, APRIL 2018 NDVI, JUN 2010 NDVI, JUN 2014 NDVI, JUN 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F G F G G G G G H H H H H H O I O O I O O I O I I I K K K K K M K M M M M M N N N N N L L N L L L L J J J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 Figure 4: Continued. 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 International Journal of Forestry Research 11 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , JUL 2010 EVI, JUL 2014 EVI, JUL 2018 EVI , AUG 2010 EVI, AUG 2014 EVI, AUG 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O I O O I O I O O I I I K K K K K K M M M M M M N N N L N L N L N L L L J J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, JUL 2010 NDVI, JUL 2014 NDVI, JUL 2018 NDVI, AUG 2010 NDVI, AUG 2014 NDVI, AUG 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F G F G G G G G H H H H H H O I O O I O O O I I I I K M K K M K K M K M M M N L N L N L N L N L N L J J J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , SEP 2010 EVI, SEP 2014 EVI, SEP 2018 EVI , OCT 2010 EVI, OCT 2014 EVI, OCT 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O O O O I O I I O I I I K K K K K K M M M M M M N N N N N N L L L L L L J J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, SEP 2010 NDVI, SEP 2014 NDVI, SEP 2018 NDVI, OCT 2010 NDVI, OCT 2014 NDVI, OCT 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O I O O I O O I O I I I K K K K K M K M M M M M N N N N N L L N L L L L J J J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 EVI , NOV 2010 EVI, NOV 2014 EVI, NOV 2018 EVI , DEC 2010 EVI, DEC 2014 EVI, DEC 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O I O I O I O I O O I I K K K K K K M M M M M M N N N L N N N L L L L L J J J J J J 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 850000 900000 950000 1000000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 NDVI, NOV 2010 NDVI, NOV 2014 NDVI, NOV 2018 NDVI, DEC 2010 NDVI, DEC 2014 NDVI, DEC 2018 E E E E E E D D D D D D C C C C C C B B B B B B A A A A A A F F F F F F G G G G G G H H H H H H O O O O O I O I I I I I K K K K K M K M M M M M N L N N L N N L L N L L J J J J J J 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 350000 400000 450000 500000 Forest reserve >0.2–0.5 Forest reserve >0.2–0.5 <0.2 >0.5–1 <0.2 >0.5–1 Figure 4: Monthly NDVI and EVI thematic output maps for each of the three years. 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 550000 600000 650000 700000 750000 12 International Journal of Forestry Research Table 1: Yearly seasonal variation of vegetation indices. Minimum Maximum Mean Season Year NDVI EVI Rainfall NDVI EVI Rainfall NDVI EVI Rainfall 2010 0.303 0.179 0.000 0.684 0.437 89.000 0.529 0.33584 35.644 Winter 2014 0.325 0.165 0.000 0.750 0.497 61.600 0.549 0.35602 21.602 2018 0.249 0.126 0.000 0.736 0.454 149.600 0.508 0.29702 46.456 2010 0.264 0.180 25.500 0.741 0.568 446.800 0.528 0.410 180.864 Spring 2014 0.354 0.226 79.300 0.780 0.550 411.200 0.585 0.419 259.751 2018 0.322 0.209 39.500 0.772 0.528 209.200 0.562 0.377 161.740 2010 0.094 0.110 141.400 0.763 0.624 611.400 0.322 0.281 395.240 Summer 2014 0.103 0.124 149.800 0.790 0.555 716.200 0.437 0.344 371.711 2018 0.159 0.110 169.300 0.778 0.479 780.200 0.465 0.327 421.656 2010 0.295 0.271 26.600 0.774 0.620 555.200 0.530 0.386 305.247 Autumn 2014 0.210 0.184 104.400 0.744 0.552 501.500 0.534 0.362 282.807 2018 0.398 0.229 107.200 0.758 0.545 557.600 0.601 0.395 321.033 Table 2: Yearly rainfall data and vegetation indices. Rainfall NDVI EVI Forest reserves 2010 2014 2018 2010 2014 2018 2010 2014 2018 Afi River 190.08 194.65 236.12 0.539 0.504 0.53 0.385 0.348 0.349 Achara Ihe 260.43 274.58 255.85 0.485 0.566 0.548 0.38 0.4 0.37 Agoi 260.43 274.58 255.85 0.501 0.553 0.54 0.382 0.401 0.359 Cross River North 190.08 194.65 236.12 0.555 0.587 0.603 0.406 0.412 0.409 Cross River South 190.08 194.65 236.12 0.476 0.548 0.559 0.351 0.381 0.362 Ekinta River 260.43 274.58 255.85 0.45 0.469 0.561 0.344 0.354 0.377 Gabu 202.43 169.83 169.22 0.404 0.457 0.437 0.286 0.294 0.277 Ikom 190.08 194.65 236.12 0.524 0.618 0.603 0.423 0.464 0.438 Ikrigon 190.08 194.65 236.12 0.434 0.479 0.475 0.312 0.32 0.306 Lower Enyong 260.43 274.58 255.85 0.484 0.545 0.54 0.335 0.359 0.315 Oban group 260.43 274.58 255.85 0.491 0.495 0.537 0.359 0.357 0.338 Obieze-Isu 260.43 274.58 255.85 0.41 0.543 0.512 0.318 0.384 0.329 Umon Ndealichi 260.43 274.58 255.85 0.499 0.553 0.565 0.37 0.396 0.363 Uwet Odot 260.43 274.58 255.85 0.493 0.544 0.567 0.36 0.388 0.355 Yache 202.43 169.83 169.22 0.415 0.433 0.433 0.29 0.293 0.286 Table 3: Correlation coefficients for monthly data. Corel_RF_NDVI Corel_RF_EVI Corel_NDVI_EVI Forest reserves 2010 2014 2018 2010 2014 2018 2010 2014 2018 Ikom 0.416 0.172 −0.211 0.589 0.661 0.059 0.931 0.598 0.729 Afi River 0.13 −0.27 −0.546 0.326 −0.056 −0.503 0.854 0.921 0.98 Ikrigon 0.096 0.228 0.043 0.585 0.555 0.289 0.719 0.883 0.948 Yache 0.386 0.061 0.285 0.715 0.435 0.508 0.881 0.799 0.954 Gabu 0.421 0.584 0.391 0.801 0.914 0.619 0.833 0.752 0.941 Cross River North 0.294 0.007 −0.127 0.658 0.148 0.136 0.769 0.909 0.902 Cross River South −0.139 −0.072 −0.122 0.171 0.101 0.171 0.89 0.876 0.911 Agoi −0.436 −0.72 −0.414 −0.062 −0.625 −0.189 0.823 0.976 0.885 Oban group −0.633 −0.827 −0.79 −0.433 −0.715 −0.609 0.903 0.954 0.933 Ekinta River −0.493 −0.764 −0.388 −0.33 −0.616 −0.261 0.926 0.958 0.975 Obieze-Isu −0.665 −0.677 −0.519 −0.581 −0.534 −0.322 0.929 0.936 0.874 Uwet Odot −0.559 −0.755 −0.568 −0.405 −0.603 −0.382 0.889 0.927 0.923 Lower Enyong −0.306 −0.738 −0.538 −0.338 −0.635 −0.581 0.912 0.931 0.832 Achara Ihe −0.404 −0.604 −0.093 −0.29 −0.47 0.16 0.94 0.95 0.85 Umon Ndealichi −0.373 −0.763 −0.551 −0.103 −0.669 −0.389 0.808 0.96 0.909 International Journal of Forestry Research 13 Table 4: Percentage change of vegetation indices. NDVI (% change) EVI (% change) Forest reserves 2010–2014 2014–2018 2010–2018 2010–2014 2014–2018 2010–2018 Afi River −6.49 5.16 −2.34 −9.61 0.29 −9.35 Achara Ihe 16.70 −3.18 16.58 5.26 −7.50 −2.63 Agoi 10.38 −2.35 10.21 4.97 −10.47 −6.02 Cross River North 5.77 2.73 11.82 1.48 −0.73 0.74 Cross River South 15.13 2.01 23.65 8.55 −4.99 3.13 Ekinta River 4.22 19.62 32.27 2.91 6.50 9.59 Gabu 13.12 −4.38 11.54 2.80 −5.78 −3.15 Ikom 17.94 −2.43 18.68 9.69 −5.60 3.55 Ikrigon 10.37 −0.84 13.14 2.56 −4.38 −1.92 Lower Enyong 12.60 −0.92 16.72 7.16 −12.26 −5.97 Oban group 0.81 8.48 12.81 −0.56 −5.32 −5.85 Obieze-Isu 32.44 −5.71 32.08 20.75 −14.32 3.46 Umon Ndealichi 10.82 2.17 17.84 7.03 −8.33 −1.89 Uwet Odot 10.34 4.23 20.56 7.78 −8.51 −1.39 Yache 4.34 0.00 6.21 1.03 −2.39 −1.38 inventory data and unavailability of long-standing and each of the fifteen forest reserves, indicate that some of standardized forest health inventory programs that could the forest reserves are moderately healthy while some are have further validated the obtained results, but the obtained greatly stressed. +e greening of the forest reserves as results corroborated the findings of these studies [1, 3, 4] shown in positive trends in NDVI indicates a net increase which show that some of the forest reserves are greatly in biomass production during the period under study. stressed. Data Availability 5. Conclusion +e MODIS data used to support the findings of the study are publicly available. Due to several underlying factors that determine vegetation health, assessing forest health is a complex process. However, in this study, we assessed the health of fifteen Conflicts of Interest forest reserves in Cross River State, Nigeria, using the meteorological data and MOD13A1-derived Normalized +e authors declare that they have no conflicts of interest. Difference Vegetation Index (NDVI) and Enhanced Veg- etation Index (EVI) for the years 2010, 2014, and 2018. Supplementary Materials Monthly mean values of the vegetation indices (NDVI and EVI) were estimated for each of the forest reserves. +e Table S1 shows the seasonal variation of rainfall, NDVI, relationship between the vegetation indices and rainfall and EVI of year 2010 for the fifteen forest reserves under data was analyzed by computing correlation coefficients. To study. It is observed that there is a general increase in the assess the health of each of the fifteen forest reserves, mean values of vegetation indices as we move from percentage change between the estimated mean values of Winter to Autumn, and this confirms that the more the the vegetation indices and seasonal variation of vegetation rainfall during the preceding season the more the indices was determined on yearly basis for the period of greenness of vegetation during the following season. study. Table S2 shows the seasonal variation of rainfall, NDVI, +e computed monthly mean values of NDVI and and EVI of year 2014 for the fifteen forest reserves under EVI range from 0.094 to 0.790 and from 0.11 to 0.624, study. It is observed that there is a general increase in the respectively, while rainfall data range from 0 to mean values of vegetation indices as we move from 780.2 mm/month within the period of study. +e com- Winter to Autumn, and this confirms that the more the puted yearly mean values of NDVI and EVI range from rainfall during the preceding season the more the 0.404 to 0.618 and from 0.277 to 0.464, respectively, greenness of vegetation during the following season. while rainfall data range from 169.22 to 274.58 mm/ Table S3 shows the seasonal variation of rainfall, NDVI, month within the period of study. Similarly, the corre- and EVI of year 2018 for the fifteen forest reserves under lation coefficients between monthly rainfall data and study. It is observed that there is a general increase in the monthly mean values of NDVI and EVI varied from mean values of vegetation indices as we move from −0.827 to 0.584 and −0.715 to 0.914. 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Journal

International Journal of Forestry ResearchHindawi Publishing Corporation

Published: Jul 27, 2020

References