Open access peer-reviewed chapter

Use of Phytosociology and Remote Sensing to Classify and Map the Vegetation in Protected Areas, Botswana

Written By

Tsholofelo Lori

Submitted: 16 July 2021 Reviewed: 27 August 2021 Published: 16 March 2022

DOI: 10.5772/intechopen.100178

From the Edited Volume

Protected Area Management - Recent Advances

Edited by Mohd Nazip Suratman

Chapter metrics overview

151 Chapter Downloads

View Full Metrics


In a natural environment, the vegetation is organized into different plant communities. The vegetation maps produced through phytosociological and remote sensing techniques can be used in the conservation, management, and monitoring of wildlife habitats in protected areas. A desk study was conducted to review studies conducted by various peer-reviewed researchers that used phytosociology and remote sensing methods to classify and map the vegetation in Botswana’s protected areas from 2000 to 2021. Seven studies were carried out in the last two decades, and four out of these studies were conducted in Northern Botswana. Even though a variety of satellite imagery was used, Landsat was the most commonly used. Maximum-likelihood supervised classification and random forest were the most common classification methods used to classify and map the vegetation. Vegetation maps are crucial in knowing which plant species occur in which protected areas, and they are used to manage effectively the vegetation in protected areas. It is important to incorporate phytosociology and remote sensing technology with the management of protected areas to conserve effectively and monitor the vegetation in these areas.


  • phytosociology
  • remote sensing
  • protected areas
  • plant communities
  • classification
  • vegetation map
  • conservation
  • Botswana

1. Introduction

Vegetation is organized into different plant communities in a natural environment. According to Brown et al. [1], “vegetation is a collective term for all the plant communities.” Clements [2] describes a plant community as a discrete and natural organism, whereas Gleason [3] states that a plant community is a collection of individual plants. It is important to integrate phytosociology with remote sensing when mapping the vegetation in protected areas. Phytosociology is a subsection of vegetation science, that focuses on existing plant communities and emphasizes their classification [4]. It concentrates on classifying plant communities based on their species composition and how different plant species relate to each other [5]. During the era of climate change, phytosociological studies are more crucial and necessary in the conservation of plant communities as well as in understanding the past and future changes occurring to these plant communities since in most cases, only vegetation data are accessible for comparisons [1, 6]. Computer technology has allowed the improvement of new methods to semi-automatically classify big datasets of vegetation and this has removed vegetation classification from just assigning the vegetation types to more organized data analysis [7]. Plant ecologists had generally agreed that the vegetation consisted of natural plant communities, which can be recognized as distinct formations with real boundaries [8]. Modern remote sensing products are likely to offer much more thorough arrangements of plant diversity than maps drawn by experts that subjectively assigned vegetation types in the olden days [7].

The classification, description, and mapping of plant communities are the important initial steps in constructing a basis in understanding, protecting, conserving, and management of natural resources in protected areas [9]. The International Union for the Conservation of Nature (IUCN) defines a protected area as “a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values” [10]. Even though most of the protected areas are located in very remote areas, it is very crucial to manage and monitor the vegetation in these areas. Field-based sampling using phytosociological methods for monitoring the vegetation in these remote areas is logistically challenging, costly, labor-intensive, and time consuming [11, 12]. In contrast, remote sensing monitoring is cheap, requires less labor, and is more objective than field-based methods, and it allows mapping of the vegetation in the remote areas to be efficient, effective, and economical [7, 11, 12]. Remote sensing in savanna landscapes is complicated because the landscapes are heterogeneous and there is a likelihood of spectral confusion between a shrub and a tree [13].

The vegetation maps produced through phytosociological and remote sensing techniques can be used in conservation and monitoring of wildlife habitats in protected areas. Vegetation maps are crucial in knowing which plant species occur in which protected areas, and they are used to effectively manage the vegetation in protected areas. Furthermore, vegetation maps are important in defining seasonal habitat use of collared wild animals, which cannot easily be tracked in huge wilderness areas with little road access more especially in Northern Botswana [14]. Mosugelo et al. [15] performed 36-year study on vegetation changes in Chobe National Park and they found that the reduction of woodland cover near Chobe river could be due to heavy browsing by elephants and impala in dry seasons. Still in Chobe National Park, Herrero et al. [13] found that increased elephant population has increased the amount of degradation in the riverfront area. The aim of this chapter is to review the phytosociological and remote sensing methods used by various peer-reviewed researchers to produce vegetation maps in Botswana’s protected areas. The literature for these studies is from 2000 to 2020. It is important to conduct a local review because it can give details on the main concerns and monitoring methods of protected areas in different environments together with providing specific information on the management of each protected area [12]. The current review focuses on information concerning the location of the study area, the study aim, satellite imagery used, and the classification method used to map the vegetation in each protected area.


2. Protected areas in Botswana

Botswana is a landlocked country located in Southern Africa and shares borders with South Africa, Namibia, Zimbabwe, and Zambia. There are 22 protected areas in Botswana [16]. A total of 245, 244 km2 of Botswana (over 37%) is committed to the conservation of wildlife, with >17% of the country being designated as protected national parks and game reserves, and 20% is utilized as wildlife management areas [17]. “Protecting such large areas of pristine wilderness across a wide variety of habitats has ensured that much of the biodiversity within Botswana is intact” [17]. Out of the 22 protected areas, there are 3 national parks, 1 transfrontier park, 7 game reserves, 6 forests reserves (located in Chobe District), and 4 sanctuaries in Botswana. Table 1 gives the names of protected areas found in Botswana, their sizes, and the years in which they were declared protected areas. These areas comprise national parks, game reserves, forest reserves, and sanctuaries (Figure 1). The Botswana National Conservation Strategy was developed in 1990 because the Botswana government acknowledged the importance of its natural resources and the goal of the strategy is sustainable development and conservation of natural resources [17]. According to DWNP [22], there is a policy framework in place which guides the management of the national parks and game reserves and this is done through the Wildlife Conservation Policy of 1986, the Tourism Policy of 1990, and National Development Plan No. 9 of 2003, whereas the Wildlife Conservation and National Parks Act of 1992 and National Parks and Game Reserves Regulations of 2000 provide the legislation. The Ministry of Environment, Natural Resources and Tourism (MENT), through the Department of Wildlife and National Parks (DWNP), is responsible for the management of protected areas in Botswana. Its sister department, the Department of Forestry and Range Resources (DFRR) is responsible for the management and conservation of forests through Forest Act 1968, Forest Reserves and State Land, Herbage Preservation Act, and Forest Policy 2011 [18]. In addition to these acts and policies, there are management plans of the protected areas, which offer guidance in their management.

Protected areas in BotswanaSize (km2)Declaration Year
National ParksChobe National Park15,4001960
Kalahari Transfontier Park35,5512000
Nxai Pan National Park15001971
Makgadikgadi Pans National Park15001970
Game ReservesCentral Kalahari Game Reserve52,8001961
Khutse Game Reserve26001971
Moremi Game Reserve48711963
Gaborone Game Reserve31980
Nnywane Dam Game Reserve101969
Mannyelanong Game Reserve31985
Northern Tuli Game Reserve7801964
Forest ReservesChobe Forest Reserve14321976
Maikaelelo Forest Reserve5431981
Kasane Forest Reserve1491968
Kasane Forest Extension6411981
Kazuma Forest Reserve1951981
Sibuyu Forest Reserve11941981
SanctuariesMaun Game Sanctuary851975
Nata Bird Sanctuary9611993
Mogobane Bird Sanctuary91940
Bathoen Dam Bird Sanctuary51992
World Heritage and Ramsar SiteOkavango Delta System55,3741996

Table 1.

Protected areas in Botswana [18, 19, 20].

Figure 1.

Map of main national parks and game reserves in Botswana [21].

Chobe National Park is considered one of the most important national parks in Africa [23] and it hosts the largest elephant (Loxodonta africana) population in Africa. Makgadikgadi Pans National Park is located in northeastern Botswana and it contains pans that host one of the most important breeding sites for flamingos. Nxai Pan National is found on the northern side of Makgadikgadi Pans National Park. Central Kalahari Game Reserve (CKGR) is the largest game reserve in Botswana which is located in Ghanzi District and it shares the border with Khutse Game Reserve that is in Kweneng District. Moremi Game Reserve is the second largest game reserve and it is found in Ngamiland District. The Kgalagadi Transfrontier Park (KTP) is the first transboundary park in Africa and is located between Botswana and South Africa. It was formed by the amalgamation of the former Kalahari Gemsbok National Park (proclaimed in 1931) in South Africa and the Gemsbok National Park (proclaimed in 1971) in Botswana [24]. In addition to the protected areas, there are wildlife management areas surrounding the protected areas and private game reserves around the country. Non-consumptive utilization of wildlife is permitted in the protected areas, whereas both sustainable consumptive and non-consumptive utilization of wildlife are allowed in the wildlife management areas.


3. Study approach

This chapter presents a desk study that was conducted to review studies that used phytosociological and remote sensing methods to classify and map the vegetation in the protected areas in Botswana. Phytosociological methods include going to the field to study and collect vegetation data, whereas remote sensing methods involve using satellite imagery to study and map the vegetation. The current study used Google, ScienceDirect, and Web of Science to search for keywords such as phytosociology, remote sensing, national park, game reserve, plant community, classification, mapping, conservation, protected areas, Botswana. English literature published from 2000 to 2020 from peer-reviewed journal articles, books, edited book chapters, electronic academic thesis, and technical reports were selected for review. The full texts of the studies were downloaded, and the information on the study area, study objective, satellite imagery used, and classification type used to map the vegetation was extracted.


4. Vegetation description, classification, and mapping

According to this review, seven vegetation description, classification, and mapping studies have been conducted in Botswana’s protected areas in the last two decades. Most of the studies were carried out in Northern Botswana. The results of the review on the phytosociological and remote sensing methods used by researchers to produce vegetation maps in Botswana’s protected are summarized in Table 2. The table provides information on the study area, satellite imagery used classification method, and the reference of the researchers who conducted the studies. Van Rooyen [25] used Landsat ETM+ to classify and map the entire Kgalagadi Transfrontier Park (KTP). This produced a vegetation map consisting of 13 major plant communities that were found on Botswana side of the KTP (Figure 2). The study found that the vegetation varies from open to dense tree savanna.

Study areaSatellite imageryClassification methodSource
Kalahari Transfontier ParkLandsat ETM+[25]
Chobe National ParkLandsat TM & AVHRRRandom Forest[13]
Savuti-Mababe-LinyantiRapidEye & LandsatMaximum Likelihood[14]
Northern BotswanaLandsat 5TM, 7ETM+, 8OLIISO Clustering[26]
Kasane Forest ReserveLandsat 5TMSupport Vector Machine[27]
Central Kalahari Game ReserveMODISRandom Forest[28]
Khutse Game ReserveSentinel-2AMaximum Likelihood[30]

Table 2.

Satellite imagery and classification methods used to map the vegetation in different protected areas in Botswana.

Notes: AVHRR, Advanced Very High Resolution Radiometer; ETM, Enhanced Thematic Mapper; OLI, Operational Land Imager; ISO, Interactive Self-Organizing; MODIS, Moderate Resolution Imaging Spectroradiometer; TM, Thematic Mapper.

Figure 2.

Vegetation map of Kgalagadi Transfrontier Park [25].

In Chobe National Park, Herrero et al. [13] mapped vegetation changes in Chobe riverfront using Landsat TM and AVHRR. The study used random forest because it is a good classification method in spatially and temporally complex heterogeneous savanna landscapes [13]. The overall classification accuracy was 79.8% for 1989–1990 and 78.5% for 2008–2009 Fox et al. [26] used Landsat 5TM, 7ETM+, and 8OLI to study land cover change (LCC) in Northern Botswana which included Chobe National Park and the six forest reserves. The study found that LCC processes in semi-arid savannas in Southern Africa are influenced by environmental and anthropogenic factors. Interactive self-organizing (ISO) clustering was the classification method used resulting in 86.7% overall accuracy and a Kappa coefficient of 0.832, with the highest confusion coming from woodland and shrubland [26]. In Northern Botswana, Sianga and Fynn [14] conducted a study in Savuti-Mababe-Linyanti ecosystem, which also covers Chobe National Park and the forest reserves. The authors used RapidEye &and Landsat to classify and map 15 plant communities in this ecosystem. The study used maximum-likelihood supervised classification and concluded that vegetation map will provide an important database for research in wildlife habitat selection and monitoring of plant communities [14]. Basalumi et al. [27] classified four carbon classes with Landsat 5TM and produced above ground carbon stock map of Kasane Forest Reserve (Figure 3). The supervised classification method used was Support Vector Machine and it yielded 97.8% overall classification accuracy. The study suggested that in miombo woodlands, the use of Landsat was ideal for monitoring biomass and carbon stock [27].

Figure 3.

Above ground carbon stock map of Kasane Forest reserve [27].

Mishra et al. [28] used MODIS to broadly and physiognomically map six vegetation morphology types in Central Kalahari Game Reserve and Khutse Game Reserve. The random forest classification method was used for this study and overall accuracy was 91.9% and Kappa coefficient was 0.88. Lori et al. [29] classified and described nine plant communities in Khutse Game Reserve. Lori [30] has the details of this study which include the mapping of these plant communities using Sentinel-2A imagery (Figures 4 and 5). Figure 6 shows one of these nine plant communities, that is, Heliotropium lineare-Enneapogon desvauxii community. Maximum-likelihood supervised classification method resulted in overall classification accuracy of 61.67% and overall Kappa coefficient of 58.18%. The heterogeneous savanna vegetation in the study area might have contributed to the optimal overall accuracy and medium Kappa value [30]. This study differs from the one by Mishra et al. [28] because it used Sentinel-2A imagery that has a high spatial scale (i.e., 10 m) to indicate fine-scale spatial heterogeneity of the area, as compared to MODIS with a low spatial resolution (i.e., 232 m) [28].

Figure 4.

Sentinel-2A natural color RGB (red, green, and blue) imagery with red squares representing the sampling plots in Khutse Game Reserve [30].

Figure 5.

Plant community map of Khutse game reserve [30].

Figure 6.

A pan habitat consisting of Heliotropium lineare-Enneapogon desvauxii plant community in Khutse Game Reserve. Photo credit: Tsholofelo Lori.

In this review, Landsat satellite imagery was the most commonly used. This might be due to the fact that Landsat is the most advanced, free, and easy to access online. The results indicate that maximum-likelihood supervised classification and random forest were the most common classification methods used to classify and map the vegetation and each of the seven studies used different satellite imagery. The results show that there is still a lot that needs to be done in terms of mapping and monitoring vegetation in Botswana’s protected areas using remote sensing. Even though different researchers use different satellites with different spatial resolutions, there is a general agreement in methods used between different studies in remote sensing of protected areas in Botswana.


5. Conclusion

A review of the literature on the phytosociological and remote sensing methods used by researchers to produce vegetation maps in Botswana’s protected areas was performed and found that there is still a lot that needs to be done in terms of producing up-to-date vegetation maps for the protected areas in Botswana. There is currently a limited number of published works on the use of remote sensing data to map the vegetation in the protected areas. Due to their remoteness, some protected areas in the country are still understudied and there is a lack of in situ vegetation data for these areas. Vegetation classification and mapping are crucial because the vegetation maps can be used to detect vegetation change over time caused by climate change. Researchers used similar methods in remote sensing of the protected areas in Botswana. It is recommended that remote sensing technology should be incorporated with the management of protected areas to effectively conserve and monitor the vegetation in these areas. Research institutions with resources and capacity should work closely with the Ministry of Environment, Natural Resources and Tourism on remote sensing of vegetation in the protected areas.



The author appreciates and thanks an anonymous person for lending her an Internet modem which enabled her to write this book chapter. The author is also grateful to an anonymous reviewer for his valuable encouragement and comments.


Conflict of interest

The author declares no conflict of interest.


  1. 1. Brown LR, du Preez PJ, Bezuidenhout H, Bredenkamp GJ, THC M, Collins NC. Guidelines for phytosociological classifications and descriptions of vegetation in southern Africa. Koedoe. 2013;55(1) #1103;10 p. DOI: 10.4102/koedoe.v55i1.1103
  2. 2. Clements FE. Plant Succession: An Analysis of the Development of Vegetation. Washington, DC, US: Carnegie Institute; 1916 Publication #242
  3. 3. Gleason HA. The individualistic concept of plant association. Bulletin of the Torrey Botanical Club. 1926;53:7-26
  4. 4. Dengler J, Chytrý M, Ewald J. Phytosociology. In: Jorgensen SE, Fath BD, editors. Encyclopedia of Ecology. 2nd ed. Oxford: Elsevier; 2008. p. 516-527. DOI: 10.1016/B978-0-444- 63768-0.00533-3
  5. 5. Ismail IM, Elawad A. Phytosociological analysis and species diversity of herbaceous layer in Rashad and Alabassia localities, South Kordofan State, Sudan. Jordan Journal of Biological Sciences. 2015;8(2):151-157
  6. 6. Qose A, Proko A. Remote Sensing techniques and phytosociological methods on the inventory of medicinal plants (Case study on the Skrapari’s Municipality). Albanian Journal of Agricultural Sciences. 2018:604-620
  7. 7. Revermann R, Finckh M. Vegetation Survey, Classification and Mapping in Angola. In: Huntley BJ, Russo V, Lages F, Ferrand N, editors. Biodiversity of Angola Science & Conservation: A Modern Synthesis. Vol. 2019. Switzerland AG: Springer Nature; 2019. pp. 97-108. DOI: 10.1007/978-3-030-03083
  8. 8. Nicolson M. Community concepts in plant ecology: from Humboldtian plant geography to the super organism and beyond. Web Ecology. 2013;13:95-102
  9. 9. Van Rooyen MW, van Rooyen N, Jacobus du P, van den Berg HM. Landscapes in the Kalahari Gemsbok National Park, South Africa. Koedoe. 2008;50(1):99-112. DOI: 10.4102/koedoe.v50i1.154
  10. 10. Dudley N, Stolton S (editors). Defining protected areas: In: Proceedings of the International Conference in Almeria, Spain. Gland, Switzerland: IUCN; 2008. 220 p. ISBN: 978-2-8317-1132-4
  11. 11. Oldeland J, Dorigo W, Lieckfeld L, Lucieer A, Jürgens N. Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery. Remote Sensing of Environment. 2010;114:1155-1166
  12. 12. Mao L, Li M, Shen W. Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade. Sustainability. 2020;12:5016. DOI: 10.3390/su12125016
  13. 13. Herrero HV, Southworth J, Bunting E. Utilizing Multiple Lines of Evidence to Determine Landscape Degradation within Protected Area Landscapes: A Case Study of Chobe National Park, Botswana from 1982 to 2011. Remote Sensing. 2016;8(623):1-17. DOI: 10.3390/rs8080623
  14. 14. Sianga K, Fynn R. The vegetation and wildlife habitats of the Savuti-Mababe-Linyanti ecosystem, northern Botswana. Koedoe. 2017;59(2):1-16. DOI: 10.4102/koedoe. v59i2.1406
  15. 15. Mosugelo DK, Moe SR, Ringrose S, Nellemann C. Vegetation changes during a 36-year period in Northern Chobe National Park, Botswana. African Journal of Ecology. 2002;40:232-240
  16. 16. IUCN ESARO. The state of protected and conserved areas in Eastern and Southern Africa. In: , State of Protected and Conserved Areas Report Series No. 1. Nairobi, Kenya: IUCN ESARO; 2020
  17. 17. Maude G, Reading R. The role of ecotourism in biodiversity and grassland conservation in Botswana Great Plains Research: Paper 1077. A Journal of Natural and Social Sciences. 2010;20(1):109-119
  18. 18. Centre for Applied Research. Forest management and use in Botswana: brief situation analysis and options for the Forest Conservation Strategy. Forest Conservation Botswana; Gaborone, Botswana; 2013. p. 36
  19. 19. Campbell A. Establishment of Botswana’s National Park and Game Reserve System. Botswana Notes and Records. 2004;36:55-66
  20. 20. UNEP-WCMC Protected Area Profile for Botswana from the World Database of Protected Areas [Internet]. June 2021. Available from: [Accessed: 23-06-2021]
  21. 21. Expert Africa [Internet]. 2018. Available from: [Accessed: 23-06-2021]
  22. 22. DWNP. Central Kalahari Game Reserve and Khutse Game Reserve Management Plan. Gaborone, Botswana: Department of Wildlife and National Parks; 2003 132 p
  23. 23. Nellis MD, Bussing CE. Spatial variation in elephant impact on the Zambezi Teak Forest in the Chobe National Park, Botswana. Geocarto International. 1990;2:55-57
  24. 24. Thondhlana G, Shackleton S, Muchapondwa E. Kgalagadi Transfrontier Park and its land claimants: a pre- and post-land claim conservation and development history. Environmental Research Letters. 2011;6;024009.12p. DOI: 10.1088/1748-9326/6/2/024009
  25. 25. Van Rooyen N. Vegetation survey of the Gemsbok National Park, Botswana and mapping of the Kgalagadi Transfrontier Park. Stellenbosch: Peace Parks Foundation; 2000 Final Project Report. Project number PPF/P/23
  26. 26. Fox JT, Vandewalle ME, Alexander KA. Land Cover Change in Northern Botswana: The Influence of Climate, Fire, and Elephants on Semi-Arid Savanna Woodlands. Land. 2017;6(73):1-23. DOI: 10.3390/land6040073
  27. 27. Basalumi L, Kilawe CJ, Mauya EW. Linking Ground Forest Inventory and NDVI in Mapping above Ground Carbon Stock in Kasane Forest Reserve, Botswana. Open Journal of Forestry. 2018;8:429-438. DOI: 10.4236/ojf.2018.83027
  28. 28. Mishra NB, Crews K, Miller J, Meyer T. Mapping vegetation morphology types in southern Africa savanna using MODIS time-series metrics: a case study of central Kalahari, Botswana. Land. 2015;4(1):197-215
  29. 29. Lori T, Ditlhogo MK, Setshogo MP, Koosaletse-Mswela P. Classification, description and mapping of the vegetation in Khutse Game Reserve, Botswana. Botswana Journal of Agriculture and Applied Sciences. 2019;13(2):8-23. DOI: 10.37106/bojaas.2019.45
  30. 30. Lori T. Classification, description and mapping of the vegetation in Khutse Game Reserve, Botswana [PhD Thesis]. Gaborone: University of Botswana; 2019

Written By

Tsholofelo Lori

Submitted: 16 July 2021 Reviewed: 27 August 2021 Published: 16 March 2022