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- Taylor & Francis
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- © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON).
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Abstract
GEOLOGY, ECOLOGY, AND LANDSCAPES 2020, VOL. 4, NO. 4, 257–268 INWASCON https://doi.org/10.1080/24749508.2019.1633219 RESEARCH ARTICLE Intra-annual variations of vegetation status in a sub-tropical deciduous forest-dominated area using geospatial approach: a case study of Sali watershed, Bankura, West Bengal, India Sadhan Malik , Subodh Chandra Pal , Biswajit Das and Rabin Chakrabortty Department of Geography, The University of Burdwan, Burdwan, West Bengal, India ABSTRACT ARTICLE HISTORY Received 25 November 2018 Intra-annual dynamics of vegetation status is very important to understand the spatial Accepted 14 June 2019 ecological environment. In this study, pixel wise temporal variation of vegetation status has been assessed using normalized differential vegetation index (NDVI) from Landsat 8 OLI data KEYWORDS of 2014. Month-wise vegetation statuses were employed to understand the status of vegeta- Vegetation dynamics; tion dynamics for the Sali River watershed. We have found that vegetation status (VS) of this normalized differential study area varied with respect to space and time. We have identified two dominant natures of vegetation index; Landsat 8 NDVI fluctuation, one with a single peak coming out of Sal forest and another with multiple OLI; remote sensing and GIS; Sali River watershed peaks which is from paddy fields. NDVI curve of natural vegetation follows a rainfall pattern and depicts a single peak during the rainy season with the moderate standard deviation (S.D.) and coefficient of variation (C.V.). On the other hand, the area associated with population pressure and agricultural fields shows multiple peaks as well as a high degree of S.D. and C. V. of NDVI during the months of April, August and December. 1. Introduction argued that strong pretences should be given towards close monitoring of vegetation for the assessment of the Human species are about to be acknowledged as the environmental system, which is also an urgent need for only intelligent and advanced species in this Spaceship the society. Earth, where each species doing their own role to sus- Traditional methods of vegetation monitoring (e.g., tain the environment (Pal, Chakrabortty, Malik, & Das, literature reviews, field surveys, map interpretation and 2018). Spatial distribution of natural vegetation and its collateral and ancillary data analysis) are time-consuming quality are a crucial part of our Earth’slife-supporting and not so effective to acquire information and often very system and environment for providing basic support much expensive and quite tough to conduct (Xiao, 2004; for sustaining life on this Earth and achieving sustain- Xie et al., 2008). In this connection, remote sensing (RS) able environmental goal. On the other hand, unplanned and geographical information system (GIS) provides deforestation issignificantly hampering the human a practical and economical means to study vegetation being as well as the lives of other species in a very cover and its quality, especially over large spatial and dangerous manner (UN Report, 2019). Quality of vege- temporal scale (Langley, Cheshire, & Humes, 2001; tation and its spatial coverage can affect numerous Nordberg & Evertson, 2005;Sarkar&Kafatos, 2004). aspect of the environment as well as living beings Normalized differential vegetation index (NDVI) has (Naidoo et al., 2019). In this regard, monitoring and been applied in this study to represent vegetation status assessing the status of vegetation is one of the significant (VS). Temporal variation of NDVI is strongly associated aspects of environmental health assessment and miti- with changes in the state of surface energy, water balance gating global climate change (Alberdi et al., 2019; (Chase, Pielke, Kittel, Nemani, & Running, 1996), carbon Bjorkman et al., 2019; UN report on environment, cycle (Betts, Beljaars, Miller, & Viterbo, 1996; Eastman, 2019). Apart from this, it also provides valuable infor- Coughenour, & Pielke, 2001), natural hazards (drought, mation regarding the natural and man-made environ- wind, floods, rain and anthropogenic activity) (Telesca & ments through quantifying vegetation cover from Lasaponara, 2006; Telesca, Lasaponara, & Lanorte, 2008) smaller to larger scales at a given point of time or over and so on. These components are strongly related to the a continuous period (Xiao, 2004; Xie, Sha, & Yu, 2008) dynamic changes in spatial coverage of vegetation and helps in the protection and restoration (Telesca & Lasaponara, 2006) and feedback on climate programmes of vegetation (Egbert, Park, Price, Lee, & (Eswaran, Lal, & Reich, 2001; Foley, Kutzbach, Coe, & Nellis, 2002; He, Zhang, Li, & Shi., 2005). Above all, Levis, 1994). So, the annual variation of vegetation study Knight, Lunetta, Ediriwickrema and Khorram (2006) CONTACT Subodh Chandra Pal geo.subodh@gmail.com Department of Geography, The University of Burdwan, Burdwan, West Bengal 713104, India © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 258 S. MALIK ET AL. is an important part for the understanding and manage- Bengal, India. According to the watershed atlas of ment of the environment. The inter-annual and inter- India, Sali River (2A2D4 is the watershed number seasonal variability of NDVI has been studied by many according to Watershed Atlas of India) can be called scholars (Anyamba & Eastman, 1996; Eastman & Fulk, as watershed because it has an area within 750 sq. km 1993; Guo, Zhang, Yuan, Zhao, & Xue, 2015;Li & (Figure 1). This river has originated from a village Kafatos, 2000; Myneni, Los, & Tucker, 1996; Nemani near Gangajalghati police station of Bankura District. et al., 2003;Sarkar&Kafatos, 2004). Most of their studies After that, this river crossed Gangajalghati, Barjora, were focused on the factors behind the natural oscillation Sonamukhi and Indus P.S. and fall into Damodar of natural vegetation. Bjorkman et al. (2019)reviewedthe River immediately to the north of the Somsar, temporal changes in vegetation status in Arctic environ- a village in the Indus. It has a large course of ments to highlight the demand for further geographically 73.6 km and it drained approximately 714 sq. km of inclusive, combined and comprehensive monitoring the inter-fluvial parts of Damodar and Dwarkeswar efforts that can better resolve the interacting impacts of rivers. This river facilitates local irrigation in its warming and other local and regional ecological factors, neighbouring areas. Cainozoic laterite can be found whereas Fu et al. (2018) studied similar kinds of study in its upper part and some Pleistocene middle to from climate changes for Qaidam Basin. Yang et al. upper and Holocene sediment can be found on the (2018) evaluated the health situation of Poyang Lake lower part (Geological and Mineral Map of West using vegetation-based indices. Demisse et al. (2018) Bengal, Govt. of India, 1999). Climatically, this river and Nanzad et al. (2019) applied vegetation indices to watershed belongs to the dry tropical and sub-humid evaluate the drought conditions. having yearly rainfall of 1480.62 mm. Most of the Sarkar and Kafatos (2004) studied on the Indian rainfall occurred during four wet months (June, subcontinent for several years to study the variability July, August and September). Maximum and mini- of vegetation and found the dominance of local cli- mum temperature ranges between 45°C and 10°C mate anomaly in determining the vegetation. Several normally (Indian Meteorological Department) kinds of datasets, e.g., NOAA-AVHRR, SPOT-VGT, (Table 1). Here, several protected forests can be MODIS and GIMMS have been used (NOAA- found such as Beliatore Protected Forest, AVHRR, SPOT-VGT, MODIS and GIMMS) to Sonamukhi Protected Forest, Patsol Protected study the variability of vegetation at the local, regio- Forest, Gangabandh Protected Forest, Gangajalghati nal and global scale (de Jong, de Bruin, de Wit, Protected Forest and so on. Nature of forest type is Schaepman, & Dent, 2011; Detsch, Otte, Appelhans, open mixed jungle, dense mixed jungle mainly Sal Hemp, & Nauss, 2016; Dubovyk, Landmann, (Shorea robusta), fairly dense mixed jungle, dense Sal, Erasmus, Tewes, & Schellberg, 2015; Fensholt & open Sal and so on. Proud, 2012; Guo et al., 2015; Hou, Zhang, & Wang, 2011; Jeong, HO, GIM, & Brown, 2011; 2.2 Methods Lanorte, Lasaponara, Lovallo, & Telesca, 2014; Lu, Kuenzer, Wang, Guo, & Li, 2015; Martínez & The study considered the vegetation dynamics using Gilabert, 2009; Schucknecht, Erasmi, Niemeyer, & a spectral index of NDVI and statistical investigation Matschullat, 2013; Sobrino & Julien, 2011; Teferi, has been done to explore the intra-annual vegetation Uhlenbrook, & Bewket, 2015; Zhao et al., 2013). dynamics. In this study, we have used 10 Landsat 8 Primary objective of this study is to understand the images from different months of the year of 2014, intra-annual variation of vegetation status over downloaded from GLOVIS (Table 2). After complet- a monsoon-dominated climatic area using NDVI on ing the required atmospheric correction, NDVI has Landsat 8 dataset. This kind of study will enable us in been extracted for the respective time periods. Pixel- a more robust identification of changes in vegetation wise information about vegetation status has been status with greater spatial detail. It would provide us analysed to understand the temporal vegetation with a unique perspective to understand the spatial–tem- dynamics and its variation has been assessed through poral dynamics of vegetation status, which will help us using the coefficient of variation (C.V.) and standard significantly in the monitoring and management of the deviation (S.D.) of NDVI. Along with this, rainfall vegetation status. pattern has been compared with 2014 vegetation dynamics, which will help us to understand the asso- ciation between rainfall and vegetation dynamics. 2. Database and methodology A major limitation of this study is that the research work was done using 10 months’ data sets as we have 2.1 Study area found that the cloud-free data were available only for Sali River is a right-bank tributary of the Damodar 10 months (excluding July and September months) River within Bankura District (22°38´ to 23°38´ north for the year of 2014. Beyond that year, the very same latitude and 86°36´ to 87°46´ east longitude) of West cloud-free data were not available at all. GEOLOGY, ECOLOGY, AND LANDSCAPES 259 Figure 1. Location map of the study area. 260 S. MALIK ET AL. Table 1. Climatic condition of study area. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec AvgTemp (C) 19.5 22.1 27.4 31.4 31.5 31.1 29 28.9 28.8 27.3 22.9 19.8 Min. Temp (C) 12.7 15.2 20.2 24.4 26.2 26.5 25.8 25.8 25.5 23.1 17 13.2 Max. Temp (C) 26.4 29.1 34.7 38.4 36.8 35.7 32.2 32 32.2 31.5 28.9 26.4 Precipitation (mm) 13 19 21 33 67 179 255 253 227 76 12 2 Table 2. Details of satellite data. also used to find out the relation between rainfall and Data Type Date of Acquisition vegetation in this area. After collecting all the informa- Landsat 8 24 January 2014; 24 February 2014; 24 March 2014; 24 April 2014; 24 May 2014; tion about NDVI and its selected field area expression, 16 June 2014; 19 August 2014; 22 October 2014; temporal variation, frequency distribution, simple line 23 November 2014; 25 December 2014 graph, C.V. and its S.D. were computed to express the nature of vegetation dynamics. 2.3 Normalized Difference Vegetation Index (NDVI) analysis 3. Results and analysis NDVI is one of the most frequently used methods for vegetation cover study. It is generally calculated by the 3.1 Status of vegetation covers in the Sali River ratio between the red and near-infrared band (Bannari, Status of vegetation can be defined by several vegetation Morin, Bonn, & Huete, 1995;Rouse,Haas,Deering,& indices, but we have relied on the NDVI for understand- Schell, 1974). NDVI is very helpful to sense diverse ing the vegetation coverage as well as its quality due to its physical characteristics of vegetation canopy such as simplicity, reliability and popularity. In this study, we leaf area index (Carlson & Ripley, 1997), fractional vege- have calculated the NDVI for different months of 2014. tation cover (Carlson & Ripley, 1997; Guerschman et al., In January 2014, we have found the high degree of NDVI 2009;Jiaet al., 2016;Scanlon,Albertson, Caylor,& along the Damodar River as well as along the main Sali Williams, 2002;Xiaoetal., 2005), vegetation condition River(NDVI>0.3).Itismaybeduetothefactthatduring (Fensholt, 2004; Telesca et al., 2008)and biomass this time, river base flow is occurring and with the help of (Carlson & Ripley, 1997). Pettorelli et al. (2005)used localized irrigation techniques, people used to cultivate this index to assess the environmental change and its vegetables in their land. On the other hand, Sal forest in impact. It can provide a useful measure of photosynthe- this area does not represent the high amount rather than tically dynamic biomass (Tucker & Sellers, 1986)land they are showing some medium NDVI (0.1–0.3). cover variations (Cuomo, Lanfredi, Lasaponara, Maximum portion of this watershed was under low Macchiato, & Simoniello, 2001; Huemmrich, Black, NDVI status (less than 0.2). In the month of January, Jarvis, McCaughey, & Hall, 1999; Lanfredi, Lasaponara, waterbody showed very low NDVI (less than 0). The Simoniello, Cuomo, & Macchiato, 2003; Myneni et al., average NDVI of January month was 0.147. In case of 1996; Telesca & Lasaponara, 2006; Telesca et al., 2008). It February, we have found that except few patches most of is also correlated with rainfall, temperature and evapo- the area was under low NDVI (less than 0.2) and average transpiration in a broad range of environmental condi- NDVI value is relatively lower (0.140) than January tions (Cihlar, Laurent, & Dyer, 1991;Gray&Tapley, (Figure 2). This may be the result of a long dry spell 1985;Sarkar&Kafatos, 2004). period in this area and devoid of a crop in the field. NDVI has been calculated following Rouse et al. During the month of March, some portion of the (1974), which is given below watershed area represented a moderate concentration of NDVI ¼ðNIR REDÞ=ðNIR þ REDÞ (1) NDVI and these are nothing but the agricultural field area with the irrigation facilities and these areas during April where RED is the surface reflectance of wavelengths turned into very high-quality vegetation (NDVI >0.4). in the visible (λ ~ 0.6 μm) and NIR is the surface Another significant fact was that overall watershed area reflectance of wavelengths (λ ~ 0.8 μm) of the spec- showed medium NDVI (0.2–0.3). Apart from these con- trum, respectively. centrated agricultural field areas, forest area up to April Several places were visited for field verifications using continuously showed a relatively low amount of NDVI theGarminGPS.Inthisstudy,wehavecollected 91 compared to its surrounding watershed due to the sample data from eight different places maintaining absence of green leaves (Figure 3.left).But afterthis minimum 100 m distance between two sample points month of April, the forest cover area started to represent based on accessibility and nature of land use and land higher NDVI with the knocking of new leaves through- cover. Several field visits were done during pre-monsoon, out the forest cover area (Figure 3 (right)). monsoon and post-monsoon seasons of the year to After that, during the month of May, the highest understand the changing nature of vegetation dynamics. NDVI was recorded along Damodar River (Figure 4 Monthly total rainfall data for the months of 2014 were (left)), whereas forest area still represented moderate GEOLOGY, ECOLOGY, AND LANDSCAPES 261 Figure 2. NDVI of Sali River for the month of January (left) and February (right). Figure 3. NDVI of Sali River for the month of March (left) and April (right). Figure 4. NDVI of Sali River for the month of May (left) and June (right). NDVI (0.2–0.3). From the month of June to October, upcoming months of June and monsoon precipita- the highest NDVI can be found over the forest cover tion (179 mm). The highest reflection of NDVI occu- area (>0.4). Rest of the parts remains low to moder- pied by the forest area, whereas the rest of them ate. At this stage, small amount of precipitation reflecting moderate NDVI (Figure 4 (right)). (67 mm) occurred, which leads to the reactivation With the passage of time, monsoon precipitation of vegetation in this area. Moderate-to-high amount takes over the region, leading to the highest reflec- of NDVI can be found over this watershed with the tance of NDVI among the all other months of 262 S. MALIK ET AL. Figure 5. NDVI of Sali River for the month of August (left) and October (right). the year. During the month of August (Figure 5. left), 3.2 Pixel-wise variation of vegetation status of the highest amount of NDVI found over the Sal Sali River dominated forest area. Agricultural fields were asso- C.V. is an important indicator for the understanding ciated with starting of cultivation leading towards the of the nature of variation. In this study, we have development of low-to-moderate NDVI. During this computed pixel-wise C.V. and we have found that month, 55% of the watershed area covered with mod- very high degree of variation (C.V. >80%) found erate-to-high NDVI (>0.4). Vegetation condition over water logging area, such as over Gangdua Dam improved during the upcoming months of October and near the confluence point of Sali River (Figure 7 (Figure 5 (right)). As a result, about 90% of the area (right)). High degree of variation (C.V. = 60% to represented moderate-to-high NDVI (0.3–0.4). This 80%) is associated with the agricultural field with may be the result of monsoon precipitation over the the irrigation facility, located in the north-eastern watershed area. part of the watershed, e.g., Paschim Dubrajpur, Vegetation status again started to decrease. In the Kamalpur, Palashbani and so on. A moderate degree case of November, 74% of the study area comes of variation (C.V. = 40–60%) dominantly found in under a low status of vegetation condition (NDVI the northern, north-eastern, and eastern part of the 0.2–0.3). Here, during this month, Sal forests remain basin, mainly along the Damodar River and around at the top of the NDVI value compared to its other irrigation-fed agricultural land. Besides this, several areas in this watershed (Figure 6 (left)). In the case of small and discrete patches of moderate variation of December, forest area (60%) still remains high rela- NDVI (40–60%) were found in the area throughout tively high to its surrounding area (Figure 6 (right)). the basin in a scattered manner. However, a lower However, along the Damodar River, 22% area of Sali degree of NDVI variation (C.V. = 20–40%) domi- River represented a higher amount of NDVI nated throughout the Sali River basin and covered (0.3–0.4). This concentration of the higher amount more than half of its area. It is mainly located in the of NDVI may be the result of agricultural activity southern and western parts of the river basin, which with the help of local irrigational facilities. is associated with Sal forest, shrub and single crop Figure 6. NDVI of Sali River for the month of November (left) and December (right). GEOLOGY, ECOLOGY, AND LANDSCAPES 263 Figure 7. Pixel wise variation of NDVI, S.D. of NDVI (left) and C.V. of NDVI (right). Figure 8. Month wise Variation of NDVI. agricultural land. Lowest variation with C.V. less than 3.3 Nature of variation of vegetation status of 20% was found in very few places covered with forest Sali River watershed and shrub. Here, it can be said that the quality of So, from the above study, we have found that the NDVI of vegetation varies within a year and the variation over Sali River is not uniform in nature and it has spatial as vegetation-covered area is comparatively lower than well as temporal variation in the vegetation. VS for the the non-vegetation area (agricultural field and built- months of January, February, March, April and May are up area). Forest cover areas of this watershed repre- more or less similar in nature. Although May month- sents a variation of NDVI throughout the year and it indicates some sort of transition phase (Figures 4 (left), 7, ranges from 20–60% C.V. as well as S.D. of this area and 9), the month of June can be said as absolutely showing similar kinds of result (Figure 7 (left)). So, transition month. Apart from these, August and we can say that there is a variation of NDVI within October represent higher concentration inthe high a year. NDVI region. Rest of them, i.e., November and With the help of the percentage bar graph and December, are more or less equal to pre-monsoon sea- box-plot graph, it becomes very clear that the sons, like January, February, etc. NDVI value of this region is not static in nature Figure 10 was constructed based on the (Figure 8 and 9). It varies from time to time. selected sample location points and their respec- During the first three months (January, February tive NDVI representations. If we judge the graph and March), lower amount of NDVI (0.2–0.3) carefully, it can be found that there are two dis- dominated over 70% of the area of the basin. tinct patterns of NDVI reflection which can be 264 S. MALIK ET AL. Temporal Variation of NDVI 100% 90% 80% Series6 70% 60% Series5 50% 40% Series4 30% Series3 20% 10% Series2 0% Series1 Figure 9. Comparative representation of NDVI. Figure 10. Month wise distribution of NDVI. Figure 11. Intra-annual variation of NDVI and distribution of rainfall in this area. easily identifiable such as single and multiple multiple (three peaks) peaks represent the agricul- peaks of NDVI. The single peak during monsoon tural field, settled area and so on. So, the natural season is the representative of forest or natural variation of vegetation actually follows the strong vegetation (Figure 11). On the other hand, pattern of precipitation. GEOLOGY, ECOLOGY, AND LANDSCAPES 265 there were two kinds of distribution in the variation 4. Discussion of NDVI, such as variation of a single peak (in Understanding vegetation dynamics is an important brown colour) and variation of multiple peaks (in aspect of the environmental monitoring. In this violet colour). Along with this, it was also found study, we have tried to detect the natural variability that the build-up areas and agricultural fields were of NDVI using Landsat 8 data to get more accurate associated with multiple peaks of NDVI distribution information about the vegetation. The temporal reso- and on the other hand, forest-dominated areas were lution of Landsat 8 dataset is about 16 days and it is associated with a single peak of NDVI distribution generally obstructed by the presence of cloud during which is following the rainfall graph. Therefore, it monsoon season. Along with this, vegetation status is can be said that rainfall pattern is playing not uniform throughout the year. As Sal forest is a dominant role on the variation of NDVI which deciduous in nature, from May to June, it remains is quite similar to the findings of Sarkar and Kafatos low to medium; from August to October, it achieves (2004) and Cissé et al. (2016). In this regard, it can high NDVI; and from November to December, again be said that the status of vegetation varies with the it represents low-to-medium NDVI. During the variationinprecipitation andonthe otherhand, month of January to April, the forest area represents rainfall duration and its intensity are affected by low NDVI because during this time very few leaves climate change (National Research Council, 1999; were available in the forest area to reflect NDVI Trenberth, Zhang, & Gehne, 2017). Therefore, it signature. On the other hand, rest of the forest can be presumed that long and continuous and areas, which is associated with diversified land use increasing intensity of climate change may have and land cover such as bare land, agricultural land, led to a definite impact on natural vegetation. settled area, waterbody and so on, represent diversi- Another concern is that the recent studies show fied nature of NDVI throughout the year. From our that the climate change is an evident fact (National sample locations, we have found the same kinds of Research Council, 1999). It is also a serious concern result. As these areas are associated with agricultural for human as well as for other living beings on this fields and irrigation facilities, resulting most of the planet Earth. Due to climate change, it was found that land types are two crops and in certain cases, it is the temporal duration of precipitation decreased as three crops. Due to this reason, during months of well as the intensity of rainfall has increased April, August and December, these areas were asso- (Trenberth et al., 2017). We have found that forest ciated with relatively high NDVI. This has led us to in this region, as well as other areas, is strongly represent the consensus that natural forest has accus- associated with the annual nature of precipitation. tomed to regional climate and based on the climatic So, long, continuous and increasing intensive climate character, vegetation changes their dynamics through change may have a definite impact on natural vegeta- the formation of a single peak within a year. tion, particularly the availability of water. However, the rest of them is associated with human modification through agricultural practices leading to two or three oscillating peaks within a year. So, there 5. Conclusion is a distinct nature of NDVI variation which is depen- In this study, intra-annual variability of vegetation dent on the nature of land use and land cover dynamics has been studied using remotely sensed characters (Figure 11.). Landsat 8 data and it has been found that vegetation Studies from different parts of the monsoon- status is not uniform especially over time. The highest dominated countries indicated that variation of concentration of NDVI or good quality of vegetation NDVI is related to El Nino-Southern Oscillation has been found over the forest cover area as well as (ENSO) and its associated “teleconnection” for over the agricultural area with irrigation facility. Africa, Australia and South America (Anyamba & Variation of vegetation status over irrigation-fed region Eastman, 1996; Eastman & Fulk, 1993;Li&Kafatos, is very high and it became less in case of forest area due 2000;Sarkar & Kafatos, 2004), whereas others have to having minor changes in vegetation quality and found its association with sea surface temperature lowest over the rest of the area. Vegetation status of anomaly (Myneni et al., 1996;Sarkar&Kafatos, this region is changing with respect to the changing 2004). Cissé et al. (2016) studied on the monsoon- nature of monsoon precipitation. Therefore, it can be dominated region of Ferlo Basin (Senegal) and said that intra-annual variation of vegetation status found that precipitation amount and its duration should be considered during the assessment of vegeta- control the spatiotemporal variation in vegetation tion status of any region. In this study, we have focused growth. In the case of Indian subcontinent, Sarkar only one year and further study can be done consider- and Kafatos (2004) found that meteorological para- ing long temporal scale to understand the changing meters play a dominant role in NDVI variation. In nature of vegetation dynamics with respect to time as this case, it has been found from Figure 11 that 266 S. MALIK ET AL. well to detect the impact of climate change on vegeta- circulation model to global changes in leaf area index. Journal of Geophysical Research: Atmospheres, 101(D3), tion dynamics. 7393–7408. Cihlar, J., Laurent, L. S., & Dyer, J. A. (1991). Relation between the normalized difference vegetation index Acknowledgements and ecological variables. Remote Sensing of Environment, 35(2–3), 279–298. We cordially thank the Dept. of Geography, The University Cissé, S., Eymard, L., Ottlé, C., Ndione, J. A., Gaye, A. T., & of Burdwan, for the infrastructural assistance such as use of Pinsard, F. (2016). Rainfall intra-seasonal variability and different kinds of remote sensing and GIS software and vegetation growth in the Ferlo Basin (Senegal). Remote writing software. We would also like to express our thanks Sensing, 8(1), 66. to the editors and reviewers for their valuable comments Cuomo, V., Lanfredi, M., Lasaponara, R., Macchiato, M. F., and suggestion in regard to the upgradation of this & Simoniello, T. (2001). Detection of interannual varia- manuscript. tion of vegetation in middle and southern Italy during 1985–1999 with 1 km NOAA AVHRR NDVI data. Journal of Geophysical Research: Atmospheres, 106 Compliance with ethical standards (D16), 17863–17876. On behalf of all authors, the corresponding author states de Jong, R., de Bruin, S., de Wit, A., Schaepman, M. E., & that there is no conflict of interest. Dent, D. L. (2011). Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115(2), 692–702. Disclosure statement Demisse, G. B, Tadesse, T, Bayissa, Y, Atnafu, S, Argaw, M, & Nedaw, D. (2018). Vegetation condition prediction for No potential conflict of interest was reported by the drought monitoring in pastoralist areas: a case study in authors. ethiopia. International Journal Of Remote Sensing, 39 (14), 4599–4615. doi:10.1080/01431161.2017.1421797 Detsch, F., Otte, I., Appelhans, T., Hemp, A., & Nauss, T. ORCID (2016). Seasonal and long-term vegetation dynamics from 1-km GIMMS-based NDVI time series at Mt. 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Journal
Geology Ecology and Landscapes – Taylor & Francis
Published: Oct 1, 2020
Keywords: Vegetation dynamics; normalized differential vegetation index; Landsat 8 OLI; remote sensing and GIS; Sali River watershed