Using geographic information systems and remote sensing technique to classify land cover types and predict grassland bird abundance and distribution in Nairobi National Park, Kenya

Abstract

Accurate and high-resolution mapping of land cover is essential for modeling species response, guiding habitat management practices, and prioritizing conservation efforts, especially in restricted and remote areas. Geographic information systems (GIS) and remote sensing (RS) techniques offer potential solutions. This study assessed the utility of GIS and RS techniques to map and predict grassland bird species in Nairobi National Park (NNP), Kenya. We utilized Sentinel-2B median imagery, which was accessible through Google Earth Engine (GEE), for January 2022 to classify six land cover classes: forest, shrubland, woodland, grassland, water, and bare soil. Grassland bird data were extracted from Kenya Bird Map (KBM) website for the period between 2015 and 2022, using full protocol card records. We hypothesized that grassland and shrubland would cover a larger portion of NNP and that grassland birds would respond positively to grassland, shrubland and woodland. We tested the second hypothesis using KBM data. Training samples for various land cover types were collected and used to train a Random Forest (RF) classifier on Sentinel-2B imagery. Model accuracy was evaluated with a confusion matrix, showing an overall accuracy of 99.93% and a Kappa statistic of 0.9989. Land cover composition indicated that grassland had the highest composition (44.9%), while water had the least (0.003%). Woodland, shrubland, forest and bare soil comprised 33.7%, 15.4%, 5.9%, and 0.2%, respectively. Logistic regression results showed that grassland birds responded positively to grassland and shrubland but tended to avoid woodland and bare soil. These findings demonstrate that land cover maps derived from GIS and RS techniques are fundamental tools for studying the abundance and distribution of grassland bird species, especially in remote areas. These tools are also essential for conservation and habitat management.

Keywords

Nairobi National Park / Kenya Bird Map / land cover types / bird / conservation / remote area

References

[1] M. Aboelnour, B.A. Engel. Application of remote sensing techniques and geographic information systems to analyze land surface temperature in response to land use/land cover change in greater Cairo region, Egypt. Journal of Geographic Information System, 10 (1) (2018), pp. 57-88, 10.4236/jgis.2018.101003

[2] J. Alroy. The shape of terrestrial abundance distributions. Science Advances, 1 (8) (2015), Article 1500082, 10.1126/sciadv.1500082

[3] M.M. Ambani, G.C. Mulaku. GIS assessment of environmental footprints of the standard gauge railway (SGR) on Nairobi National Park, Kenya. Journal of Environmental Protection, 12 (10) (2021), pp. 694-716, 10.4236/jep.2021.1210042

[4] A.A. Amboka, T.G. Ngigi. Mapping and monitoring spatial-temporal cover change of prosopis species colonization in Baringo Central, Kenya. International Journal of Engineering Science Invention, 4 (3) (2015), pp. 50-55

[5] A.B. Azpiroz, J.G. Blake. Associations of grassland birds with vegetation structure in the Northern Campos of Uruguay. The Condor, 118 (1) (2016), pp. 12-23, 10.1650/CONDOR-15-49.1

[6] R.D. Bardgett, J.M. Bullock, S. Lavorel, P. Manning, U. Schaffner, N. Ostle, … H. Shi. Combating global grassland degradation. Nature Reviews Earth & Environment, 2 (10) (2021), pp. 720-735, 10.1038/s43017-021-00207-2

[7] M. Basile, I. Storch, G. Mikusiński.Abundance, species richness and diversity of forest bird assemblages—The relative importance of habitat structures and landscape context. Ecological Indicators, 133 (2021), Article 108402, 10.1016/j.ecolind.2021.108402

[8] S. Beery, E. Cole, J. Parker, P. Perona, K. Winner. Species distribution modeling for machine learning practitioners: A review. Paper Presented at the 4th ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS), Virtual Event, Australia (2021, June), 10.1145/3460112.3471966

[9] A.G. Besnard, A. Davranche, S. Maugenest, J.B. Bouzillé, A. Vian, J. Secondi. Vegetation maps based on remote sensing are informative predictors of habitat selection of grassland birds across a wetness gradient. Ecological Indicators, 58 (2015), pp. 47-54, 10.1016/j.ecolind.2015.05.033

[10] A.L. Boesing, E. Nichols, J.P. Metzger. Land use type, forest cover and forest edges modulate avian cross-habitat spillover. Journal of Applied Ecology, 55 (3) (2018), pp. 1252-1264, 10.1111/1365-2664.13032

[11] M. Brambilla, L. Ilahiane, G. Assandri, S. Ronchi, G. Bogliani. Combining habitat requirements of endemic bird species and other ecosystem services may synergistically enhance conservation efforts. Science of the Total Environment, 586 (2017), pp. 206-214, 10.1016/j.scitotenv.2017.01.203

[12] S.J. Brodie, J.T. Thorson, G. Carroll, E.L. Hazen, S. Bograd, M.A. Haltuch, … R.L. Selden. Trade-offs in covariate selection for species distribution models: A methodological comparison. Ecography, 43 (1) (2020), pp. 11-24, 10.1111/ecog.04707

[13] V.L. Buxton, T.J. Benson. Conservation-priority grassland bird response to urban landcover and habitat fragmentation. Urban Ecosystems, 19 (2) (2016), pp. 599-613, 10.1007/s11252-016-0527-3

[14] L.N. Céspedes Arias, S. Wilson, N.J. Bayly. Community modeling reveals the importance of elevation and land cover in shaping migratory bird abundance in the Andes. Ecological Applications, 32 (1) (2022), Article e02481, 10.1002/eap.2481

[15] M.A. Cunningham, D.H. Johnson. Proximate and landscape factors influence grassland bird distributions. Ecological Applications, 16 (3) (2006), pp. 1062-1075, 10.1890/1051-0761(2006)016[1062:PALFIG]2.0.CO;2

[16] D.J.T. Douglas, J. Waldinger, Z. Buckmire, K. Gibb, J.P. Medina, L. Sutcliffe, … N. Koper. A global review identifies agriculture as the main threat to declining grassland birds. Ibis, 165 (4) (2023), pp. 1107-1128, 10.1111/ibi.13223

[17] R.G. Drum, C.A. Ribic, K. Koch, E. Lonsdorf, E. Grant, M. Ahlering, … D. Sample. Strategic grassland bird conservation throughout the annual cycle: Linking policy alternatives, landowner decisions, and biological population outcomes. PLoS One, 10 (11) (2015), Article e0142525, 10.1371/journal.pone.0142525

[18] M. Drusch, U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, … P. Bargellini.Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120 (2012), pp. 25-36, 10.1016/j.rse.2011.11.026

[19] S. Feyers. Nairobi National Park situation analysis. African Conservation Center, Kenya Wildlife Service, Nairobi (2015)

[20] T.L. Hamer, C.H. Flather, B.R. Noon. Factors associated with grassland bird species richness: The relative roles of grassland area, landscape structure, and prey. Landscape Ecology, 21 (4) (2006), pp. 569-583, 10.1007/s10980-005-2167-5

[21] M.H.K. Hesselbarth, M. Sciaini, K.A. With, K. Wiegand, J. Nowosad. Landscapemetrics: An open-source R tool to calculate landscape metrics. Ecography, 42 (10) (2019), pp. 1648-1657, 10.1111/ecog.04617

[22] N.J.B. Isaac, M.A. Jarzyna, P. Keil, L.I. Dambly, P.H. Boersch-Supan, E. Browning, … R.B. O’Hara. Data integration for large-scale models of species distributions. Trends in Ecology & Evolution, 35 (1) (2020), pp. 56-67, 10.1016/j.tree.2019.08.006

[23] K. Karra, C. Kontgis, Z. Statman-Weil, J.C. Mazzariello, M. Mathis, S.P. Brumby. Global land use/land cover with Sentinel 2 and deep learning. Paper Presented at the 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium (2021, July)

[24] R. Kibet, C. Olatubara, C. Ikporukpo, A. Jebiwott. Land use land cover changes and encroachment issues in Kapkatet wetland, Kenya. Open Journal of Ecology, 11 (2021), pp. 493-506, 10.4236/oje.2021.117032

[25] W.D. Kissling, J.A. Ahumada, A. Bowser, M. Fernandez, N. Fernández, E.A. García, … J. Elith. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biological Reviews, 93 (1) (2018), pp. 600-625, 10.1111/brv.12359

[26] D.J. Klein, M.W. McKown, B.R. Tershy. Deep learning for large scale biodiversity monitoring. Paper Presented at the Bloomberg Data for Good Exchange Conference, New York City, NY, USA (2015, September)

[27] B.K. Kogo, L. Kumar, R. Koech. Forest cover dynamics and underlying driving forces affecting ecosystem services in western Kenya. Remote Sensing Applications: Society and Environment, 14 (2019), pp. 75-83, 10.1016/j.rsase.2019.02.007

[28] P. Kowe, O. Mutanga, T. Dube. Advancements in the remote sensing of landscape pattern of urban green spaces and vegetation fragmentation. International Journal of Remote Sensing, 42 (10) (2021), pp. 3797-3832, 10.1080/01431161.2021.1881185

[29] P.J. Leitão, M.J. Santos. Improving models of species ecological niches: A remote sensing overview. Frontiers in Ecology and Evolution, 7 (2019), p. 009, 10.3389/fevo.2019.00009

[30] J. Lockhart, N. Koper. Northern prairie songbirds are more strongly influenced by grassland configuration than grassland amount. Landscape Ecology, 33 (9) (2018), pp. 1543-1558, 10.1007/s10980-018-0681-5

[31] C.K. Maria.An assessment of the management strategies and wildlife population trends in Nairobi National Park. Retrieved from University of Nairobi Digital Repository (2013)

[32] E.M. Marzinelli, S.B. Williams, R.C. Babcock, N.S. Barrett, C.R. Johnson, A. Jordan, … P.D. Steinberg. Large-scale geographic variation in distribution and abundance of Australian deep-water Kelp forests. PLoS One, 10 (2) (2015), Article e0118390, 10.1371/journal.pone.0118390

[33] L.C. Maskell, M. Botham, P. Henrys, S. Jarvis, D. Maxwell, D.A. Robinson, … B.A. Emmett. Exploring relationships between land use intensity, habitat heterogeneity and biodiversity to identify and monitor areas of High Nature Value farming. Biological Conservation, 231 (2019), pp. 30-38, 10.1016/j.biocon.2018.12.033

[34] M. Muchai, L. Bennun, L. Lens, M. Rayment, G. Pisano. Land-use and the conservation of Sharpe’s Longclaw Macronyx sharpei in Central Kenya. Bird Conservation International, 12 (2) (2001), pp. 107-121, 10.1017/S0959270902002071

[35] M. Muchai, L. Lens, L. Bennun. Habitat selection and conservation of Sharpe’s longclaw (Macronyx sharpei), a threatened Kenyan grassland endemic. Biological Conservation, 105 (3) (2002), pp. 271-277, 10.1016/S0006-3207(01)00118-5

[36] F. Mwangi, Q. Zhang, H. Wang. Development challenges and management strategies on the Kenyan National Park system: A case of Nairobi National Park. International Journal of Geoheritage and Parks, 10 (1) (2022), pp. 16-26, 10.1016/j.ijgeop.2022.02.003

[37] H.K. Ndithia, S.N. Bakari, K.D. Matson, M. Muchai, B.I. Tieleman. Geographical and temporal variation in environmental conditions affects nestling growth but not immune function in a year-round breeding equatorial lark. Frontiers in Zoology, 14 (1) (2017), p. 28, 10.1186/s12983-017-0213-1

[38] W. Neumann, S. Martinuzzi, A.B. Estes, A.M. Pidgeon, H. Dettki, G. Ericsson, V.C. Radeloff. Opportunities for the application of advanced remotely-sensed data in ecological studies of terrestrial animal movement. Movement Ecology, 3 (1) (2015), p. 8, 10.1186/s40462-015-0036-7

[39] D.T. Nugent, D.J. Baker-Gabb, S.W.J. Leonard, J.W. Morgan. Livestock grazing to maintain habitat of a critically endangered grassland bird: Is grazer species important?. Ecological Applications, 32 (5) (2022), Article e2587, 10.1002/eap.2587

[40] G. Obunga, M. Siljander, M. Maghenda, P.K.E. Pellikka. Habitat suitability modelling to improve conservation status of two critically endangered endemic Afromontane forest bird species in Taita Hills, Kenya. Journal for Nature Conservation, 65 (2022), Article 126111, 10.1016/j.jnc.2021.126111

[41] F.J. Ong’ondo, F.A. Fogarty III, P. Njoroge, M.D. Johnson.Bird abundance and diversity in shade coffee and natural forest Kenya. Global Ecology and Conservation, 39 (2022), Article e02296, 10.1016/j.gecco.2022.e02296

[42] D.O. Onyango, C.O. Ikporukpo, J.O. Taiwo, S.B. Opiyo, K.O. Otieno. Comparative analysis of land use/land cover change and watershed urbanization in the lakeside counties of the Kenyan Lake Victoria basin using remote sensing and GIS techniques. Advances in Science, Technology and Engineering Systems Journal, 6 (2) (2021), pp. 671-688, 10.25046/aj060278

[43] D.J. Perović, S. Gámez-Virués, D.A. Landis, F. Wäckers, G.M. Gurr, S.D. Wratten, … N. Desneux. Managing biological control services through multi-trophic trait interactions: Review and guidelines for implementation at local and landscape scales. Biological Reviews, 93 (1) (2018), pp. 306-321, 10.1111/brv.12346

[44] J.A. Pithon, R. Duflot, V. Beaujouan, M. Jagaille, G. Pain, H. Daniel. Grasslands provide diverse opportunities for bird species along an urban-rural gradient. Urban Ecosystems, 24 (6) (2021), pp. 1281-1294, 10.1007/s11252-021-01114-6

[45] G.N. Pricope, K.L. Mapes, K.D. Woodward. Remote sensing of human-environment interactions in global change research: A review of advances, challenges and future directions. Remote Sensing, 11 (23) (2019), p. 2783, 10.3390/rs11232783

[46] L. Reino, M. Porto, R. Morgado, F. Carvalho, A. Mira, P. Beja. Does afforestation increase bird nest predation risk in surrounding farmland?. Forest Ecology and Management, 260 (8) (2010), pp. 1359-1366, 10.1016/j.foreco.2010.07.032

[47] C.A. Ribic, D.W. Sample. Associations of grassland birds with landscape factors in southern Wisconsin. The American Midland Naturalist, 146 (1) (2001), pp. 105-121, 10.1674/0003-0031(2001)146[0105:AOGBWL]2.0.CO;2

[48] D. Rocchini, V. Andreo, M. Förster, C.X. Garzon-Lopez, A.P. Gutierrez, T.W. Gillespie, H.C. Hauffe, K.S. He, B. Kleinschmit, P. Mairota, M. Marcantonio, M. Metz, H. Nagendra, S. Pareeth, L. Ponti, C. Ricotta, A. Rizzoli, G. Schaab, M. Zebisch, … M. Neteler. Potential of remote sensing to predict species invasions: A modelling perspective. Progress in Physical Geography, 39 (3) (2015), pp. 283-309, 10.1177/0309133315574659

[49] S. Sh. Application of geographic information system (GIS) in forest management. Journal of Geography & Natural Disasters, 5 (3) (2015), p. 145, 10.4172/2167-0587.1000145

[50] J.-M. Thiollay. Large bird declines with increasing human pressure in savanna woodlands (Burkina Faso). Biodiversity and Conservation, 15 (7) (2006), pp. 2085-2108, 10.1007/s10531-004-6684-3

[51] R.H. Topaloğlu, E. Sertel, N. Musaoǧlu. Assessment of classification accuracies of Sentinel-2 and Landsat-8 data for land cover/use mapping. Paper Presented at the 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, Prague; Czech Republic (2016, July), 10.5194/isprsarchives-XLI-B8-1055-2016

[52] M. Winter, D.H. Johnson, J.A. Shaffer, T.M. Donovan, W.D. Svedarsky. Patch size and landscape effects on density and nesting success of grassland birds. Journal of Wildlife Management, 70 (1) (2006), pp. 158-172, 10.2193/0022-541X(2006)70[158:PSALEO]2.0.CO;2

[53] A. Zlinszky, H. Heilmeier, H. Balzter, B. Czúcz, N. Pfeifer. Remote sensing and GIS for habitat quality monitoring: New approaches and future research. Remote Sensing, 7 (6) (2015), pp. 7987-7994, 10.3390/rs70607987