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USING RGB COLOR LIGHTS TO DETERMINE THE EFFECTS OF HUMAN PRESSURE ON BIG SCALE AFRICAN NATIONAL PARKS
USING RGB COLOR LIGHTS TO DETERMINE THE EFFECTS OF HUMAN PRESSURE ON BIG SCALE AFRICAN NATIONAL...
Gatwaza, Olivier Clement; Cao, Xin
GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2021.1923288 RESEARCH ARTICLE USING RGB COLOR LIGHTS TO DETERMINE THE EFFECTS OF HUMAN PRESSURE ON BIG SCALE AFRICAN NATIONAL PARKS Olivier Clement Gatwaza and Xin Cao School of Landscape Architecture, Beijing Forestry University, Beijing, China ABSTRACT ARTICLE HISTORY Received 15 June 2020 One of the reasons for humans not being actively involved in protecting natural landscapes Accepted 24 April 2021 from the wave of pressures to which they are exposed is the ground-based image that sends to people a wrong illusion that nature is very large and unlimited, with inexhaustible resources. KEYWORDS This study focused on National Parks (NPs), considered as one of the last refuges of wildlife on Africa; anthropogenic earth, yet overlooked by various Land Use & Land Cover (LULC) assessments. The study uses pressures; remote sensing; Landsat images and mapping approach to capture the most significant LULC changes that rgb color bands; protected happened between 1985 and 2015 on five Big Scale African NP (BSANP), quantify their spatial areas extent and identify their level of human intrusion. The results show that NPs in desert regions, Banc d’Arguin NP and Namib Naukluft NP recorded a slight change in LULC. An increase of 18 and 8.3% in burned areas were, respectively, recorded in Manovo-Gounda-Saint Floris NP and Southern NP. The annual growth of 0.76% in green LULC for Random NP is a sign that there is still some hope for conservation in Africa. Our results show that Africa’s PAs are severely affected by human activities and that it is high time to come to their rescue before it’s too late. Tracking the fluctuations in LULC due to human intrusion into NPs is a reliable strategy to ensure their sustainable conservation. 1 Introduction 2008). The current human consumption of natural Quantifying and monitoring the spatial and temporal resources has exceeded the global carrying capacity and dynamics of the earth’s surface Land Use and Land lead to the depletion of the global natural resource stocks Cover (LULC) is a critical environmental challenge that in a way that compromises the ability of future genera- society must address. Such dynamic forces are key deter- tions to meet their own needs (Simpson et al., 2000). minants of ecosystem vulnerability and global environ- In response to the challenges of biodiversity loss mental change with potential severe impacts on human and increasing severe damage to the global environ- livelihoods and global economy (Guzha et al., 2018). In ment, Protected Areas (PAs) were created as cycle-like process, a series of major “natural” and “man- a commitment by nations, peoples, groups and indi- made” disturbances on natural LULC, such as drought, viduals to safeguard areas of land and sea from floods, landslides, urbanization, population migration, destruction and defined them as “Clearly defined geo- conversions of natural land into arable land, etc. are graphical spaces, recognized, dedicated and managed, identified as major drivers of LULC change. These through legal or other effective means, to achieve the changes take place temporally at different times (few long-term conservation of nature with associated eco- weeks or many years), and spatially at different area system services and cultural values” (Ángeles et al., extent (Small-area or large-scale) with different land use 2019). National Parks (NPs) are classified as the cate- intensity, but the long-term changes are the most useful gory II of the VI management categories of PAs made for evaluating the sustainability of natural resources by the International Union for Conservation of Nature (Matlhodi et al., 2019). Human pressures on the envir- (IUCN) and are the most emblematic type in Africa onment are activities undertaken by humans that can (Muhumuza & Balkwill, 2013), their mission implies likely damage nature (Venter et al., 2016a). Vegetation (a) the protection of the ecological integrity of ecosys- removal due to human activities commonly leaves the tems for present and future generations, and (b) the soil susceptible to massive undesirable effects on ecosys- provision of space for environmental education and tems: wildlife habitat destruction, piercing historical nature-based tourism activities (Ángeles et al., 2019) wildlife migration corridors, soil erosion by wind and (Miller-Rushing et al., 2017). African NPs were estab- water, soil and air pollution, increase water turbidity, lished on the continent currently with 1.3 billion eutrophication and coastal hypoxia, etc. (Anaba et al., (17%) of the world’s population and on which an 2017) (Allan et al., 2017) (Roques et al., 2001) (Mora, additional 2.2 billion people are expected by the year CONTACT Xin Cao email@example.com School of Landscape Architecture, Beijing Forestry University, Beijing, China © 2021 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. 2 O. C. GATWAZA AND X. CAO 2050 (UN, 2017). In addition, the number of people large-scale African NPs. High resolution satellite from all over the world seeking recreational opportu- images with pixel resolution of less than 30 m, are nities increases as the world gets more industrialized equipped with full potential to provide the required (Chongwa, 2012). Africa is characterized by precision to map LULC changes at landscape level a relatively small number of PA (3.3% of the total (Brink & Eva, 2009) (Chen et al., 2014). Landsat number) but its sites are generally much larger and images have been used the most for determining forest in total cover 13.8% of the global area protected cover and measuring forest cover change (Castillo (Deguignet et al., 2014). Given the current observa- et al., 2015) (Devaney et al., 2015). Remote sensing tions, global climate change is generally suspected to offers an alternative technic to the ground-based sur- have had a substantial impact on many natural eco- vey for LULC change analysis (Liu & Yang, 2015). The systems (Gao et al., 2016); however, many researchers satellite-based earth observation offers a modern tool have also attributed human activities such as agricul- and unparalleled technique to gather information on ture, over-grazing and tree cutting to the natural green physical characteristics of earth systems. Different cover depletion and even to desertification (Zhou bands of multispectral imagery are produced by ima- et al., 2014) (Lyu et al., 2016) (Eisner et al., 2016). ging sensors aboard satellites. When viewed sepa- Despite increasing evidence of prehistoric and histor- rately, a single band looks like a black and white ical human disturbance on natural environment, photograph, when illuminated successively by RGB major questions remain concerning the extent of the (Red, Green, Blue) color light, these bands can be current unprecedented wildlife crisis especially in PA combined to create color pictures used to identify and measures to permit its recovery (Foster et al., features of interest on the ground (Horning, 2004). 1999). The perceptual organization of observed on satellite Over the last decades Africa has been subjected to images is based on mathematical values commonly a series of major losses of its natural ecosystems caused called hexadecimal color values. Knowing that white by environmental change and, particularly, LULC light can be decomposed into a color spectrum, it changes related to urbanization and agriculture, two becomes logical and obvious that the reverse process major drivers of biodiversity loss globally (Akinyemi, can be realizable. (Bendito, 2005) (Jehlicka, 2012). 2017). An increased exposure to hydro-climatic This work attempts to quantify and scrutinize habi- extremes that have affected different parts of the con- tat loss via climatic change and anthropogenic intru- tinent is an earlier reminder of the effects of LULC sions into African NP already known to be important change on water resources and social-ecological sys- drivers of biodiversity loss (Jacobson et al., 2015) tem (Näschen et al., 2019). Several studies investigated (Harris et al., 2014) (Chown et al., 2003). The specific and quantified LULC change to better understand objectives of this study are: (a). Identify and map key many of the continent’s land surface and climatic LULC classes, (b). Analyze LULC changes over a 30- processes but most of these efforts have been limited year period, (c). Identify the drivers of the observed to local-scale investigation due to the computational changes, (d). Make recommendations minimize the requirements of analyzing large volumes of data, even negative impacts of LULC change. Since the situation then rarely dared to break through the boundaries of of this study is replicated in several other similar and African PAs to monitor the dynamics of their LULC, mostly smaller locations on the continent, the meth- yet such studies are highly required in the regions of ods and conclusions can be relevant to state govern- ecological importance and biodiversity hubs as they ments, conservation organizations, and future are supposed to serve as the last line of defense in the researchers who need to base their policies and deci- fight to slow down species extinctions (Akinyemi, sions on sufficient and reliable information to ensure 2017). So far, growing concerns ranging from ecolo- the efficiency and sustainability of natural resources gical and economical ineffectiveness of NPs, to NPs use (Sader & Winne, 1992). Changes on NP landscape fragmentation, isolation, and degazettement have been have generally the characteristics of large-scale and reported in different parts of Africa (Santini et al., long-term space-time evolution and are apparently 2016). In addition, the lack of high spatial resolution difficult to quantify; fortunately, today’s available data has discouraged many initiatives that have earth observation technology has the characteristics attempted to generate global to continental scale of macroscopic, dynamic, fast and accurate detection LULC maps, especially if spanned over a long period ability which makes it the most convenient tool to of time (Midekisa et al., 2017). Thankfully, the USGS reveal the mysteries of land-based changes that have release of Landsat data archive for public use since slowly, silently and even in the dark altered the entire 2008 coupled with the availability of new high- or part of the landscape over a long time period. performance computing platforms enabled this study Remote sensing has many advantages when mapping to gather large volumes of data and process them at and monitoring the Earth’s surface, such as providing continental level to assess at periodical intervals, spa- up to date, cheaper and more dynamic information for tial and temporal LULC dynamics with the focus on many applications including physical environmental GEOLOGY, ECOLOGY, AND LANDSCAPES 3 Table 1. List of BSANP their establishment date, area and geographic position. No Country Name of BSANP Establishment date Area (Km ) Latitude/longitude of centroid 1 Central African Republic Manovo-Gounda-Saint Floris National Park 1933 18,909 9°31 N/21°21 E 2 Mauritania Banc d’Arguin National Park 1978 12,075 20°10 N/16°24 E 3 Namibia Namib-Naukluft National Park 1986 50,766 24°40’S/15°16 E 4 South Sudan Southern National Park 1939 22,800 6°32 N/28°40 E 5 Sudan Radom National Park 1980 12,500 9°50 N/24°45 E data (DEM, slope, aspect), biological factor data (plant challenge of handling the large amount of data species distribution, distance to drainage), human dis- required to cover all the BSANP, a sample of five turbance data (road, hydroelectric stations, mines, BSANP with each one being the largest representing scenic spots, etc.). Remote sensing assessment metho- a specific part of the continent (Central, North, South, dology is widely used for vegetation mapping, and East and West) was chosen. The Table 1 introduces the natural resource assessment. five BSANP on which the research was conducted: 2 Methods 2.2 Operational classification of BSANP’s LULC 2.1 Big Scale African National Parks The use of multiple-date imagery for LULC mapping is becoming more common but remains a complex The present study was conducted on Big Scale African task which requires a substantive level of organization National Parks (BSANP) data of which were obtained and coordination as computer storage and processing from the World Database on Protected Areas capacities are often challenged by spectral large data (WDPA) the most comprehensive global database on associated with increased number of bands (Prince terrestrial and marine protected areas internationally et al., 2012). Fortunately, petabyte-scale archives of recognized as the major tool that keeps up-to-date remote sensing data have become freely available for information on PA (UNEP-WCMC, 2017). The data the public from multiple U.S. Government agencies can be accessed from Protected Area Profile for Africa (Gorelick et al., 2017). We downloaded 60 satellite from the World Database of Protected Areas, available images from the U.S. Geological Survey Earth at: www.protectedplanet.net Explorer (EE), tool that gives users the ability to As of January 2021, the WDPA keeps a record of query, search, and order satellite images, aerial photo- 8562 PAs on African continent; given the complexity graphs, and cartographic products from several and abundance of PAs in Africa, a systematically sources. 2/3 of the used images were taken by selected sample was chosen to represent the BSANP Landsat 5 and 1/3 of images was taken by Landsat 8. on which this research was conducted. The selection Our date range was from 01/01/1985 up to 31/12/ criteria included: 1985; from 01/01/2000 up to 31/12/2000 and from 01/01/2015 up to 31/12/2015. For each site we care- (1) Belonging to the 2015 United Nations List of fully selected images taken within the same month so Protected Areas, data based on the WDPA as to compare situations of the same season of October release the year. Images with less than 10% of cloud cover (2) Region: Africa were preferred. All the images were subtracted from (3) Designation: Either of Forest Reserve, Nature the following address: Reserve, National Park, Game Reserve, Marine Protected Area (1) Manovo-Gounda-Saint Floris NP: Path 179 (4) IUCN Category: II Row 53; Path 179 Row 54; Path 180 Row 53, (5) Status: Designated Path 180 Row 54. (6) Area: Greater or equal to (≥) 5000 km (2) Banc d’Arguin NP: Path 205 Row 046; Path 205 Row 047; Path 206 Row 046; Path 206 Row 047. Note, however, that the names of the categories (3) Namib-Naukluft NP: Path 178 Row 076; Path used by IUCN do not necessarily reflect the names 178 Row 077; Path 178 Row 078; Path 179 Row used at national or sub-national levels. For consis- 076; Path 179 Row 077; Path 179 Row 078. tency, the term “National Park” was used in this (4) Southern NP: Path 174 Row 55; Path 174 Row research to denote all PA as stipulated by IUCN to 56; Path 175 Row 55; Path 175 Row 56. denote its Category II. (5) Radom NP: Path 178 Row 53; Path 178 Row 54. The selection yielded 45 BSANP fulfilling the above criteria, of which the name, country, area [km2], Landsat 5 was equipped with Multispectral Scanner establishment date and geographical coordinates [lati- System (MSS) sensor for 1985 images and Thematic tudeand longitude] were clarified. Considering the 4 O. C. GATWAZA AND X. CAO Mapper (TM) sensor for 2000 images, Landsat 8 was vegetation, though not very apparent as the data did equipped with Operational Land Imager and Thermal not have a mid-infrared band. For TM images, the Infrared Sensor (OLI TIRS) for 2015 images. The combination Red = band4, Green = band3, reason for choosing Landsat is its longest history as Blue = band2. This combination includes the near it was launched in 1972. The TM sensor, have then infrared channel (band 4: 0.76–0.90 m) which makes been added from Landsat 4 (1982–1993), Landsat 5 vegetation monitoring easy and land water boundaries (1984–2013), Landsat 6 (1993, launch failed), Landsat more apparent. For OLI-TIRS images: Red = band5, 7 (1999-Still active) and the most recent Landsat 8 Green = band4; Blue = band3; this combination (2013-Still active) (Kennedy et al., 2007) (Xie et al., includes the mid-infrared (band 5: 1.55–1.75 m) 2008). which is very sensitive to moisture and is therefore used to monitor vegetation. In this type of classification, the image processing 2.3 Radiometric and Atmospheric calibration software is given guidance by the operator to specify the LULC classes of interest. The number of classes Satellite images are contaminated by various radiative was previously set as approximately twice as the processes, hence their obvious correction process. required number of classes. The obtained classes in During this process, raw data images are either excess were later combined to obtain the real repre- restored, rectified or their quality is enhanced for sentation of BSANP’s state of LULC at a given time. better interpretation. Distortions caused by the This research integrated different types of LULC into 9 Earth’s rotation and camera angles are eliminated. classes: Forest, Shrub, Grass, Water, Bedrock, Sand We used a processing software to correct all the down- strips, Sand dunes, Burned area, and Bare land. By loaded images that used the following formula to per- confronting three produced images with the image form the atmospheric corrections. analysis tool, the change between the two layers was ρET L ¼ þ L computed at three different periods by apply pixel over t t p pixel comparison, then for each BSANP, produced two different maps displaying the level of occurred L L � π t� t p changes over equal periods of time (15 years), and ρ ¼ ET a third map displaying the level of occurred changes L = radiance measured by sensor over 30 years period. The former two images provide tot ρ = reflectance of the target a comparison between changes over equal period of E = irradiance on the target time, while the latter elucidates the level of occurred T = transmissivity of the atmosphere changes over the 30 years period. The the cumulative Lp = path radiance (radiance due to atmosphere) impact of human activities on BSANP natural envir- onment that refers to the sum up of isolated impacts that has created scars on natural landscape through 2.4 Analysis of LULC change on BSANP human intrusion into BSANP was measured, and used The LULC change in BSANP natural environment was the globally-standardized measure commonly known tracked in one 30 years interval, and two 15 years sub- as human footprint (Allan et al., 2017) (Sanderson intervals. LULC state on BSANP was studied at three et al., 2002) (Venter et al., 2016a) (M.C. Hansen different dates 1985, 2000 and 2015. To assess the state et al., 2013). of LULC on the single BSANP at a given period, satellite images of the same year and covering the same NP were combined (mosaic) and a subset of the study area was 3 Results retrieved according to the official NP boundary shape- 3.1 LULC maps for each BSANP file accessed from the WDPA database. The subset image was taken to the unsupervised classification. The LULC classification was conducted on basis of This stage requires a level of familiarity with remote subset images covering Manovo-Gounda-Saint Floris sensing as primary color lights are used to illuminate NP, Banc d’Arguin NP, Namib Naukluft NP, Southern different bands to enable detection of features hardly NP and Radom NP. On one hand, the classification recognizable without appropriate band combination grouped LULC into approximatively 6 main classes of adjustments. Here are some of the frequently used LULC (Forest/Dense vegetation, Shrub, Grass, Bare band combinations throughout this research: For land, Burned land and water) in Manovo-Gounda- Landsat MSS images, the combination Red = band3, Saint Floris NP, Southern NP and Radom NP. On Green = band2, Blue = band1. This combination the other hand, the LULC classification for Banc includes the visible red band channel (band 3: d’Arguin and Namib Naukluft was grouped into 5 0.63–0.69 m), was used to detect different types of main classes (water, bed rock, sand strips and sand GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Table 2. The LULC class numerical value and portion for Manovo-Gounda-Saint Floris NP. Manovo-Gounda-Saint Floris NP (Central African Republic) Covered area [sq.km] % Covered area [sq.km] % Covered area [sq.km] % 1985 2000 2015 Forest/dense vegetation 2753.78 14.70 1927.76 10.29 4394.47 23.45 Shrub 5136.62 27.41 6469.80 34.53 3141.10 16.76 Grass 4929.17 26.31 6905.85 36.86 4273.49 22.81 Water 883.92 4.72 813.89 4.34 0.00 0.00 Burned land 0.00 0.00 0.00 0.00 3536.62 18.87 Bare land 5034.46 26.87 2620.44 13.98 3391.95 18.10 Total 18,737.95 18,737.73 18,737.63 dunes). The following maps represent the 30 years content behind the visual information contains evolution of LULC over the 5 BSANP. The LULC numerical values that can be used to compute the maps contain visual information about the extent of amount and portion of each LULC class at a given each LULC class over each BSANP. The table of time period. The Figure 1, Figure 2, Figure 3, Figure 4 Table 3. The LULC class numerical value and portion for Banc d’Arguin NP. Banc d’Arguin NP (Mauritania) Covered area [sq.km] % Covered area [sq.km] % Covered area [sq.km] % 1985 2000 2015 Bedrock 535.88 4.49 535.54 4.48 872.74 7.32 Sand strips 722.43 6.06 2250.33 18.83 2397.41 20.10 Sand dunes 4774.13 40.02 3334.31 27.90 3149.70 26.41 Water 5896.40 49.43 5832.25 48.80 5504.82 46.16 Total 11,928.83 11,952.43 11,924.67 Table 4. The LULC class numerical value and portion for Namib Naukluft NP. Namib Naukluft NP (Namibia) Covered area [sq.km] % Covered area [sq.km] % Covered area [sq.km] % 1985 2000 2015 Forest/dense vegetation 3628.21 7.15 3106.95 6.12 6340.44 12.50 Shrub 19,840.85 39.12 19,250.20 37.95 15,006.13 29.58 Grass 11,352.09 22.38 13,240.17 26.10 20,901.90 41.20 Water 15,899.01 31.35 15,132.65 29.83 8483.26 16.72 Total 50,720.16 50,729.97 50,731.73 Table 5. The LULC class numerical value and portion for Southern NP. Southern NP (South Sudan) Covered area [sq.km] % Covered area [sq.km] % Covered area [sq.km] % 1985 2000 2015 Forest/dense vegetation 4041.65 21.00 4696.13 24.40 5422.98 28.18 Shrub 5305.95 27.57 1874.01 9.74 0.00 0.00 Grass 5444.22 28.29 5999.44 31.17 5112.52 26.57 Burned land 1641.83 8.53 3339.68 17.35 3240.64 16.84 Bare land 2811.68 14.61 3335.19 17.33 5469.08 28.42 Total 19,245.33 19,244.45 19,245.22 Table 6. The LULC class numerical value and portion for Radom NP. Radom NP (Sudan) Covered area [sq.km] % Covered area [sq.km] % Covered area [sq.km] % 1985 2000 2015 Burned land 2782.89 19.00 1154.80 7.87 1517.07 10.34 Dense vegetation 3058.27 20.89 1552.18 10.58 2842.54 19.37 Shrub 2712.74 18.53 6003.58 40.92 3785.00 25.80 Grass 2989.11 20.41 4740.80 32.31 5511.27 37.56 Bare land 3099.97 21.17 1221.12 8.32 1016.60 6.93 Total 14,642.97 14,672.48 14,672.47 6 O. C. GATWAZA AND X. CAO Figure 1. The LULC classes for Manovo-Gounda-Saint Floris NP based on 30 mx 30 m imagery. GEOLOGY, ECOLOGY, AND LANDSCAPES 7 Figure 2. The LULC classes for Banc d’Arguin NP based on 30 mx 30 m imagery. Figure 3. The LULC classes for Namib-Naukluft NP based on 30mx 30 m imagery. and Figure 5 contain the visual information about the 4 Discussions state of various BSANP, while Table 2, Table 3, Table 4.1 Pixel over pixel comparison 4, Table 5, and Table 6 in section 3.2 indicate the quantitative value of each class and its share (in per- The fluctuations that have occurred on BSANP’S centage) compared to the total area of the BSANP. LULC between 2000 and 1985, 2015 and 2000 as well as the ones that occurred over the last 30 years (between 2015 and 1985) can be appreciated by 3.2 LULC class numerical value and portion for looking at LULC change maps that were produced BSANP by confronting two selected layers and computing the variations using pixel over pixel comparison. The following tables contain the LULC class covered The Figure 6, Figure 7, Figure 8, Figure 9 and area (in square kilometers) and portion (in percentages) Figure 10 indicate with slightly different scores, for the 5 BSANP at three different time period, which regional level of change from “Not affected”, can also be translated into the trend of each LULC class “Mildly affected”, “Moderately affected”, to change over a 30 years period (1985–2015). 8 O. C. GATWAZA AND X. CAO Figure 4. The LULC classes for Southern NP based on 30mx 30 m imagery. “Highly affected”. Note however that the occurred the nature of the occurred change and the drivers change can either be destructive, restorative, or of the change. constructive to BSANP’s natural landscape. It The fluctuations maps show a generalized hence requires a further interpretation to know Moderate change with in Manovo-Gounda-Saint GEOLOGY, ECOLOGY, AND LANDSCAPES 9 Figure 5. The LULC classes for Random NP based on 30mx 30 m imagery. Floris NP LULC between 2000 and 1985, localized Banc d’Arguin NP LULC shows no remarkable High change between 2015 and 2000, the combination changes at the sea side during all the three phases, of which has resulted in localized High change Mild to Moderate changes on land but excessive between 2015 and 1985. changes seem to have occurred on isolated 10 O. C. GATWAZA AND X. CAO Figure 6. Pixel over pixel comparison over Manovo-Gounda-Saint Floris NP. locations along the separation line between water The fluctuations maps show a generalized No to and land. Mild change in Namib Naukluft NP LULC for all the three time intervals. GEOLOGY, ECOLOGY, AND LANDSCAPES 11 Figure 7. Pixel over pixel comparison over Banc d’Arguin NP. Figure 8. Pixel over pixel comparison over Namib Naukluft NP. The fluctuations maps show general Moderate to to as an additional tool to help the interpretation of High LULC changes over isolated and scattered loca- what happened to the studied BSANP during the last tions of Southern NP. 30 years (2015–1985) and predict the trend of each The fluctuation maps apparently show the same LULC class, as shown on Figure 11, Figure 12, Figure LULC changes over the two-time intervals (2000–1985 13, Figure 14 and Figure 15. and 2015–1985) in Random NP created only 5 years before the beginning of the first interval. 4.3 Reasons of LULC changes over the BSANP For Manovo-Gounda-Saint Floris NP, excessive 4.2 LULC class changes over the BSANP changes in LULC that have been mentioned were mainly due to the burned area that have expanded Although climate change counts among the important on 19% of the NP entire area at the expense of shrub factors influencing the spatial variation in the green and grass which moreover seemed to expand more LULC (Jing Wang et al., 2015), the analysis of Landsat and more on larger areas. The fire is set by human images in different times offer a strong tool that not activities willingly or accidentally. Most of the time only include climatic change but also embrace other individuals are attracted by the short-term beneficial destructive factors such as the human impact into the effects of fire on flora since it causes the early and computation of environmental degradation. The abundant production of young leaves for fauna, thus visual information supplied by pixel over pixel com- completing the food supply, on medium and long parison maps is enriched by LULC class proportions term however fires have depressive effects on 12 O. C. GATWAZA AND X. CAO Figure 9. Pixel over pixel comparison over Southern NP. protected species in general (Yameogo, 2005). nearly the same period 1986–1995) deforestation was Compared to similar studies, our results seem promis- recorded, a reforestation has regained the field and ing for Manovo-Gounda-Saint Floris NP though extended on 8.75% beyond the 1985 situation during a 4.41% (>1.2% measured by Sandoval et al. during the following 30 years (6.8% deforestation in the case GEOLOGY, ECOLOGY, AND LANDSCAPES 13 Figure 10. Pixel over pixel comparison over Random NP. of Sandoval between 1995–2000). (Sandoval et al., mostly caused by the effects of wind and water that 2007). from time to time moved sand from a place to A generalized Mild to No change situation in another. For Banc d’Arguin NP new human settle- Banc d’Arguin NP and Namib Naukluft NP is ments were detected near the southern border and explained by the fact that they are parks in desert other isolated regions along the separation line regions, the slight changes that have occurred were between land and water which explains the High- 14 O. C. GATWAZA AND X. CAO Figure 11. LULC class proportions and trend over Manovo-Gounda-Saint Floris NP. Figure 12. LULC class proportions and trend over Banc d’Arguin NP. Figure 13. LULC class proportions and trend over Namib Naukluft NP. level change in small and isolated locations of its degradation of natural landcover (Temudo & landscape. Contrary to conventional wisdom, the Silva, 2011) (Eisner et al., 2016) (Ouedraogo et al., human settlements in that desert regions seem to 2010). have sustained the rare but growing green cover Excessive changes that have mostly invaded the through agricultural activities around them. west and south east of the Southern NP are mainly Paradoxically, our results still get along with pre- explained by the increased bush fire and bare land. vious researches that agricultural practices and Figure 14 makes it clear that the regions covered with urbanization coverage are the main cause of the shrub were the highly affected especially during the GEOLOGY, ECOLOGY, AND LANDSCAPES 15 Figure 14. LULC class proportions and trend over Southern NP. Figure 15. LULC class proportions and trend over Random NP. 2015–2000 time period. The park has lost 22.11% forms of human use (King, 2007), the park seems to (0.7% annually) of its green cover during the last have succeeded to drive away people responsible of 30 years. Nevertheless, the park gained 7.18% in forest setting fire on the park, and indeed NP plays an cover during the last 30 years, this value is slightly important role in countering green cover decline lower than an annual increase of 0.66% of forest (Sanchez-Azofeifa et al., 2002). However, further increase within protected areas in Africa that was negative causes can also better explain the green reported for 2015 (FAO, 2016). Let’s again note that cover prosperity, as an example, animal populations African tropical region was reported to have the lowest are declining not only in disturbed habitat but also in Gross Forest Cover Loss (0.4%) due to the lower use of NP (Chapman et al., 2010). forests for commercial development compared to other tropical regions (Matthew C. Hansen et al., 4.4 Evidences of human causes of BSANP’s LULC 2010). changes Scars of excessive changes that appear on Random NP maps are mainly due to the scars of burned land While the previous sections of this study focused on that were still visible into its landscape even some identifying and quantifying habitat loss caused by the years after its establishment as a NP in 1980. The combined effect of climatic change and anthropogenic gains in all green cover types reached 22.9% (0.76% intrusions into the BSANP, it is important to highlight annually) and a reduction in bare land and burned that most of the drivers of LULC change such as area infer a great achievement of conservation espe- farmlands, human settlements, linear infrastructures cially during the time in which many other African are exclusively human related. Though lightning can reserves are increasingly being affected by habitat loss also cause bush fire, such cases are extremely rare due to human activities (Newmark, 2008). (Trollope & Trollope, 2004); hence, we consider Conservation through the developing word recognizes human to also be responsible for igniting fire on that natural areas need to be protected from certain BSANP. Note, however, that the combination of 16 O. C. GATWAZA AND X. CAO warmer and drier conditions makes the forest much et al., 2017), (FAO, 2016), (Estes, Kuemmerle, more susceptible to fire (Kilungu et al., 2019), which Kushnir, Radeloff, & Shugart, 2012), (Gimmi, et al., shows how much human and climatic changes are 2011)] and stressed that BSANP are indeed threatened intertwined and amalgamated. To make things by human pressure and forest loss. The study supplied worse, the socio-political environment around necessary metrics to analyze not only temporal human BSANP has played a vital role in shaping the past, pressure trends but also the temporal trends of land- present and future of the parks (Apio et al., 2017). scape elements. This study supplied data and offers the The war also had implications on law enforcement in opportunity to act at restoring damaged PA as some Manovo-Gounda-Saint Floris NP and Southern NP, BSANP head to dangerous thresholds where the NP leading to the lack of protection activities and loss of will be difficult to recover. wildlife which better explain the catastrophic situation of these two PAs, respectively, in Central African Acknowledgements Republic and South Sudan both countries ravaged by long wars. The presented results contain another cate- The authors would like to thank scientists and practitioners gory of features whose change can be caused by either whose expertise advice and critique inspired the develop- type of drivers. Forest loss can either result from ment of this work. Special thanks to Professor Robert Melnick from Oregon University and Dr. Hassan human activity, desertification, global warming, etc. Mohammed Abdelmanan for your assistance. In the same way, bare land increase can result from many and unspecific factors such as human activity, desertification, global warming, etc. Nevertheless, Disclosure statement whatever the cause, the precarious green cover further No potential conflict of interest was reported by the deteriorates as climatic change continues. In our ana- author(s). lysis, we have observed that human intrusions have the characteristics of brutal, isolated, random, and limited in time while climatic change causes are rather silent, Funding large scale, continuous, and rather long-term process. This research did not receive any specific grant from fund- We have also observed that the threat to BSANP ing agencies in the public, commercial, or not-for-profit natural landscape is generally brought by a small sectors. group of selfish people but there is a shared responsi- bility to indirectly contribute to its devastation by turning a blind eye to the PA in danger. Even coun- ORCID tries that have an obligation to monitor the changes in Olivier Clement Gatwaza http://orcid.org/0000-0003- PAs to meet international reporting commitments fail 4977-0818 to do it and the change quantification is rarely done (Devaney et al., 2015). This study comes as a reminder of humankind’s responsibility to live in harmony with References nature. 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Geology Ecology and Landscapes
Taylor & Francis
USING RGB COLOR LIGHTS TO DETERMINE THE EFFECTS OF HUMAN PRESSURE ON BIG SCALE AFRICAN NATIONAL PARKS
Gatwaza, Olivier Clement
Geology Ecology and Landscapes
, Volume 7 (1): 18 –
Jan 2, 2023
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