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4 - Global Forest Maps in Support of Conservation Monitoring

Published online by Cambridge University Press:  23 July 2018

By
Samuel M. Jantz ,
Lilian Pintea ,
Janet Nackoney  and
Matthew C. Hansen
Edited by
Allison K. Leidner  and
Graeme M. Buchanan
Allison K. Leidner
Affiliation:
National Aeronautics and Space Administration, Washington DC
Graeme M. Buchanan
Affiliation:
Royal Society for the Protection of Birds (RSPB), Edinburgh
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Summary

Forest habitat loss is a preeminent threat to numerous species, including all four chimpanzee (Pan troglodytes) sub-species. The chimpanzee range spans approximately 2.3 million km2, making ground surveys of habitat condition intractable. Remotely sensed data is well-suited to quantify rates and map the spatial patterns of forest cover loss over large areas. Open access to the Landsat data archive and the proliferation of high performance computing has enabled annual forest loss to be mapped at the 30-m resolution for the entire globe. Here, we discuss a partnership between the Jane Goodall Institute (JGI) and the University of Maryland Department of Geographical Sciences to leverage recent advances in remote sensing with the aim of monitoring chimpanzee habitats across their range to support conservation action. We present two case studies using two methods to monitor habitat change. First, we use a simple method that quantifies forest loss area within the chimpanzee range and in protected areas between 2001 and 2014. The second study describes the development of a model that incorporates several environmental variables derived from satellite imagery to produce a map of relative habitat suitability that can be updated as new imagery becomes available. Resulting data from both methods are used to create Key Ecological Attributes (KEAs) that are incorporated into Jane Goodall Institute’s Decision Support System (DSS). The DSS allows managers to aggregate multi-source spatial data into management units that enable them to better visualise the relative status of chimpanzee habitats and make better-informed decisions to increase the likely success of conservation actions over time.
Type
Chapter
Information
Satellite Remote Sensing for Conservation Action
Case Studies from Aquatic and Terrestrial Ecosystems
 , pp. 82 - 118
Publisher: Cambridge University Press
Print publication year: 2018

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References

Anteau, M. J., Wiltermuth, M. T., Sherfy, M. H., and Shaffer, T. L. (2014). Measuring and predicting abundance and dynamics of habitat for piping plovers on a large reservoir. Ecological Modelling, 272, 16–27.CrossRefGoogle Scholar
Ashcroft, M. B., King, D. H., Raymond, B., et al. (2017). Moving beyond presence and absence when examining changes in species distributions. Global Change Biology, 23, 2929–2940.CrossRefGoogle ScholarPubMed
Avissar, R. and Werth, D. (2005). Global hydroclimatological teleconnections resulting from tropical deforestation. Journal of Hydrometeorology, 6, 134–145.CrossRefGoogle Scholar
Barbet-Massin, M., Jiguet, F., Albert, C. H., and Thuiller, W. (2012). Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution, 3, 327–338.CrossRefGoogle Scholar
Bennett, A. F. (2003). Linkages in the Landscape: The Role of Corridors and Connectivity in Wildlife Conservation. Gland: IUCN.Google Scholar
Benson, B. J. and MacKenzie, M. D. (1995). Effects of sensor spatial resolution on landscape structure parameters. Landscape Ecology, 10, 113–120.CrossRefGoogle Scholar
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.CrossRefGoogle Scholar
Breiman, L., Friedman, J., Stone, C. J., and Olsen, R. A. (1984). Regression Trees. Washington DC: Chapman and Hall/CRC.Google Scholar
Brook, B., Bradshaw, C. J. A., Koh, L. P., and Sodhi, N. S. (2006). Momentum drives the crash: mass extinction in the tropics. Biotropica, 38, 302–305.CrossRefGoogle Scholar
Brooks, T. M., Pimm, S. L., and Oyugi, J. O. (1999). Time lag between deforestation and bird extinction in tropical forest fragments. Conservation Biology, 13, 1140–1150.CrossRefGoogle Scholar
Bruner, A. G., Gullison, R. E., Rice, R. E., and da Fonseca, G. A. B. (2001). Effectiveness of parks in protecting tropical biodiversity. Science, 291, 125–128.CrossRefGoogle ScholarPubMed
Caldecott, J. and Miles, L. (2005). World Atlas of the Great Apes and Their Conservation. Cambridge: UNEP World Conservation Monitoring Centre.Google Scholar
Campbell, G., Kuehl, H., Kouamé, P. N. G., and Boesch, C. (2008). Alarming decline of West African chimpanzees in Côte d’Ivoire. Current Biology, 18, R903–R904.CrossRefGoogle ScholarPubMed
Carrere, R. (2013). Oil palm in Africa: past, present, and future scenarios. World Rainforest Movement. See http://wrm.org.uy/wp-content/uploads/2014/08/Oil_Palm_in_Africa_2013.pdf.Google Scholar
Conservation Measures Partnership (2013). Open standards for the practice of conservation; version 3.0. See http://cmp-openstandards.org/wp-content/uploads/2014/03/CMP-OS-V3-0-Final.pdf.Google Scholar
Cushman, S. A., Gutzweiler, K., Evans, J. S., and McGarigal, K. (2010). The gradient paradigm: a conceptual and analytical framework for landscape ecology. In Cushman, S. A. and Huettmann, F., eds., Spatial Complexity, Informatics, and Wildlife Conservation. Tokyo: Springer Verlag, pp. 83–108.CrossRefGoogle Scholar
Defries, R. (2012). Why forest monitoring matters for people and the planet. In Achard, F. and Hansen, M. C., eds., Global Forest Monitoring from Earth Observation. Boca Raton, FL: CRC Press, pp. 1–12.Google Scholar
De Souza, C. M., Hayashi, S., and Veríssimo, A. (2009). Near real-time deforestation detection for enforcement of forest reserves in Mato Grosso. See http://77.243.131.160/pub/fig_wb_2009/papers/trn/trn_2_souza.pdf.Google Scholar
Dornelas, M., Gotelli, N. J., McGill, B., et al. (2014). Assemblage time series reveal biodiversity change but not systematic loss. Science, 344, 296–299.CrossRefGoogle Scholar
Fitzpatrick, M. C., Gotelli, N. J., and Ellison, A. M. (2013). MaxEnt versus MaxLike: empirical comparisons with ant species distributions. Ecosphere, 4, article 55.CrossRefGoogle Scholar
Food and Agriculture Organization of the United Nations (2006). Global Forest Resources Assessment 2005, Main Report. Progress towards Sustainable Forest Management. Rome: Food and Agriculture Organization of the United Nations.Google Scholar
Gao, F., Masek, J., Schwaller, M., and Hall, F. (2006). On the blending of the MODIS and Landsat ETM+ surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44, 2207–2218.Google Scholar
Gaston, K. J. (1991). How large is a species’ geographic range? Oikos, 61, 434–438.CrossRefGoogle Scholar
Guisan, A. and Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147–186.CrossRefGoogle Scholar
Gunther, M. (2015). Google-powered map helps fight deforestation. The Guardian, 10 March. See www.theguardian.com/sustainable-business/2015/mar/10/google-earth-engine-maps-forest-watch-deforestation-environment.Google Scholar
Haddad, N. M., Brudvig, L. A., Clobert, J., et al. (2015). Habitat fragmentation and its lasting impact on Earth’s ecosystems. Science Advances, 1, 1–9.CrossRefGoogle ScholarPubMed
Hansen, M. C., DeFries, R. S., Townshend, J. R. G., et al. (2002). Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sensing of Environment, 83, 303–319.CrossRefGoogle Scholar
Hansen, M. C., Roy, D. P., Lindquist, E., et al. (2008). A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sensing of Environment, 112, 2495–2513.CrossRefGoogle Scholar
Hansen, M. C., Potapov, P. V, Moore, R., et al. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853.CrossRefGoogle ScholarPubMed
Hansen, M. C., Krylov, A., Tyukavina, A., et al. (2016). Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11, 34008.CrossRefGoogle Scholar
Harcourt, A. (1998). Ecological indicators of risk for primates, as judged by species’ susceptibility to logging. In Caro, T., ed. Behavioral Ecology and Conservation Biology. Oxford: Oxford University Press, pp. 56–79.CrossRefGoogle Scholar
Hastie, T. and Fithian, W. (2013). Inference from presence-only data: the ongoing controversy. Ecography, 36, 864–867.CrossRefGoogle ScholarPubMed
Hickey, J. R., Nackoney, J., Nibbelink, N. P., et al. (2013). Human proximity and habitat fragmentation are key drivers of the rangewide bonobo distribution. Biodiversity and Conservation, 22, 3085–3104.CrossRefGoogle Scholar
Hilton-Taylor, C., Pollock, C. M., Chanson, J. S., et al. (2008). State of the world’s species. In Vie, J.-C., Hilton-Taylor, C., and Stuart, S. N., eds. Wildlife in a Changing World: An analysis of the 2008 IUCN Red List of Threatened Species. Gland: IUCN, pp. 15–41.Google Scholar
Humle, T., Maisels, F., Oates, J. F., Plumptre, A. J., and Williamson, E. A. (2016). Pan troglodytes. The IUCN Red List of Threatened Species 2016. See www.iucnredlist.org/details/15933/0.Google Scholar
Hutchinson, G. (1957). Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22, 415–427.CrossRefGoogle Scholar
Jantz, P., Goetz, S., and Laporte, N. (2014). Carbon stock corridors to mitigate climate change and promote biodiversity in the tropics. Nature Climate Change, 4, 138–142.CrossRefGoogle Scholar
Jantz, S. M., Pintea, L., Nackoney, J., and Hansen, M. C. (2016). Landsat ETM+ and SRTM data provide near real-time monitoring of Chimpanzee (Pan troglodytes) habitats in Africa. Remote Sensing, 8, 1–16.CrossRefGoogle Scholar
Johns, A. D. and Skorupa, J. P. (1987). Responses of rain-forest primates to habitat disturbance: a review. International Journal of Primatology, 8, 157–191.CrossRefGoogle Scholar
Joshi, A. R., Dinerstein, E., Wikramanayake, E., et al. (2016). Tracking changes and preventing loss in critical tiger habitat. Science Advances, 2, 1–8.CrossRefGoogle ScholarPubMed
Junker, J., Blake, S., Boesch, C., et al. (2012). Recent decline in suitable environmental conditions for African great ape. Diversity and Distributions, 18, 1077–1091.CrossRefGoogle Scholar
Kim, D., Sexton, J. O., and Townshend, J. R. (2015). Accelerated deforestation in the humid tropics from the 1990s to the 2000s. Geophysical Research Letters, 42, 3495–3501.CrossRefGoogle Scholar
Kormos, R., Boesch, C., Bakarr, M. I., and Butynski, T. M. (2003). West African Chimpanzees. Status Survey and Conservation Action Plan. Gland: IUCN.Google Scholar
Manel, S., Williams, H. C., and Ormerod, S. J. (2001). Evaluating presence-absence models in ecology: the need to count for prevalence. Journal of Appied Ecology, 38, 921–931.CrossRefGoogle Scholar
McLennan, M. R. (2008). Beleaguered chimpanzees in the agricultural district of Hoima, western Uganda. Primate Conservation, 23, 45–54.CrossRefGoogle Scholar
Merow, C., Smith, M. J., and Silander, J. A. (2013). A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography, 36, 1058–1069.CrossRefGoogle Scholar
Mittermeier, R. A., Gil, P. R., Hoffman, M., et al. (2005). Hotspots Revisited: Earth’s Biologically Richest and Most Endangered Terrerestrial Ecoregions. Mexico City: Cemex.Google Scholar
Myers, N., Mittermeier, R. A, Mittermeier, C. G., da Fonseca, G. A., and Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403, 853–858.CrossRefGoogle ScholarPubMed
Nellemann, C. and Newton, A. (2002). The great apes: the road ahead. A Globio perspective on the impacts of infrastructural development on the great apes. See: www.globio.info/downloads/249/Great+Apes+-+The+Road+Ahead.pdf.Google Scholar
Palumbo, I., Rose, R. A., Headley, R. M. K., et al. (2017). Building capacity in remote sensing for conservation: present and future challenges. Remote Sensing in Ecology and Conservation, 3, 21–29.CrossRefGoogle Scholar
Petersen, R. and Pintea, L. (2017). Forest watcher brings data straight to environmental defenders. See www.wri.org/blog/2017/09/forest-watcher-brings-data-straight-environmental-defenders.Google Scholar
Pimm, S. and Askins, R. (1995). Forest losses predict bird extinctions in eastern North America. Proceedings of the National Academy of Sciences of the United States of America, 92, 9343–9347.CrossRefGoogle ScholarPubMed
Pimm, S. L. and Brooks, T. (2013). Conservation: forest fragments, facts, and fallacies. Current Biology, 23, R1098–R1101.CrossRefGoogle ScholarPubMed
Pimm, S. L., Jenkins, C. N., Abell, R., et al. (2014). The biodiversity of species and their rates of extinction, distribution, and protection. Science, 344, (6187).CrossRefGoogle ScholarPubMed
Pintea, L. (2005). Satellite analysis of threats to Gombe chimpanzees. In Caldecott, J. and Miles, L., eds., World Atlas of Great Apes and Their Conservation. Cambridge: UNEP World Conservation Monitoring Centre, pp. 223–224.Google Scholar
Pintea, L. (2007). Applying satellite imagery and GIS for chimpanzee habitat change detection and conservation. PhD Thesis, University of Minnesota, MN.Google Scholar
Pintea, L. (2016). Geodesign restores chimpanzee habitats in Tanzania. ArcNews, Summer newsletter. See www.esri.com/esri-news/arcnews/summer16articles/geodesign-restores-chimpanzee-habitats-in-tanzania.Google Scholar
Pintea, L., Bauer, M. E., Bolstad, P. V, and Pusey, A. (2002). Matching multiscale remote sensing data to inter-disciplinary conservation needs: the case of chimpanzees in Western Tanzania. In Pecora 15/Land Satellite Information IV/ISPRS Commission I/FIEOS 2002 Conference Proceedings, p. 12.Google Scholar
Plumptre, A. J., Nixon, S., Vieilledent, G., et al. (2015). Status of Grauer’s Gorilla and Chimpanzees in Eastern Democratic Republic of Congo: Historical and Current Distribution and Abundance. New York, NY: Wildlife Conservation Society.Google Scholar
Pusey, A. E., Pintea, L., Wilson, M. L., Kamenya, S., and Goodall, J. (2007). The contribution of long-term research at Gombe National Park to chimpanzee conservation. Conservation Biology, 21, 623–634.CrossRefGoogle ScholarPubMed
Reymondin, L., Jarvis, A., Perez-Uribe, A., et al. (2012). A methodology for near real-time monitoring of habitat change at continental scales using MODIS-NDVI and TRMM. See www.terra-i.org/dam/jcr:508a0e27-3c91-4022-93dd-81cf3fe31f42/Terra-i Method.pdf.Google Scholar
Saura, S. (2004). Effects of remote sensor spatial resolution and data aggregation on selected fragmentation indices. Landscape Ecology, 19, 197–209.CrossRefGoogle Scholar
Sexton, J. O., Song, X.-P., Feng, M., et al. (2013). Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. International Journal of Digital Earth, 6, 427–448.CrossRefGoogle Scholar
Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 677–680.CrossRefGoogle ScholarPubMed
Stickler, C. M. and Southworth, J. (2008). Application of multi-scale spatial and spectral analysis for predicting primate occurrence and habitat associations in Kibale National Park, Uganda. Remote Sensing of Environment, 112, 2170–2186.CrossRefGoogle Scholar
Strassburg, B. B. N., Rodrigues, A. S. L., Gusti, M., et al. (2012). Impacts of incentives to reduce emissions from deforestation on global species extinctions. Nature Climate Change, 2, 350–355.CrossRefGoogle Scholar
Tracewski, Ł., Butchart, S. H. M., Donald, P. F., et al. (2016a). Patterns of twenty-first century forest loss across a global network of important sites for biodiversity. Remote Sensing in Ecology and Conservation, 2, 37–44.CrossRefGoogle Scholar
Tracewski, Ł., Butchart, S. H. M., Di Marco, M., et al. (2016b). Toward quantification of the impact of 21st-century deforestation on the extinction risk of terrestrial vertebrates. Conservation Biology, 30, 1070–1079.CrossRefGoogle ScholarPubMed
UNESCO (1973). International Classification and Mapping of Vegetation, Series 6, Ecology and Conservation. Paris: United Nations Educational, Scientific and Cultural Organization.Google Scholar
Vitousek, P. M. (1997). Human domination of Earth’s ecosystems. Science, 277, 494–499.CrossRefGoogle Scholar
Wich, S. A., Garcia-Ulloa, J., Kühl, H. S., et al. (2014). Will oil palm’s homecoming spell doom for Africa’s great apes? Current Biology, 24, 1659–1663.CrossRefGoogle Scholar
Woodcock, C. E., Allen, R., Anderson, M., et al. (2008). Free access to Landsat imagery. Science, 320, 1011.CrossRefGoogle ScholarPubMed
Wrangham, R. W., Hagel, G., Leighton, M., et al. (2008). The Great Ape World Heritage Species Project. In Stoinski, T. S., Steklis, H. D., and Mehlman, P. T., eds, Conservation in the 21st Century: Gorillas as a Case Study. New York, NY: Springer Science and Business Media, pp. 282–296.CrossRefGoogle Scholar

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