Integrating remote sensing and local ecological knowledge to monitor rangeland dynamics |
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Institution: | 1. Department of Forest & Conservation Sciences, University of British Columbia, 2424 Main Mall, Vancouver, V6T 1Z4, Canada;2. Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, V6T 1Z4, Canada;3. Geospatial Sciences Center of Excellence and Department of Natural Resource Management, South Dakota State University, 1021 Medary Avenue, Wecota Hall 506B, Brookings, SD, 57007-3510, USA;4. Liu Institute for Global Issues, University of British Columbia, 6476 NW Marine Dr., Vancouver, V6T 1Z2, Canada;5. Mountain Societies Research Institute, University of Central Asia,138 Toktogul Street, Bishkek, 720001, Kyrgyzstan;1. Sistema de Información Espacial para el Soporte de Decisiones sobre, Impactos a la Biodiversidad (SIESDIB), Comisión Nacional para el Uso y Conservación de la Biodiversidad (CONABIO), Mexico;2. Liga Periférico – Insurgentes Sur, Núm. 4903, Col. Parques del Pedregal, Delegación Tlalpan, 14010, Mexico;1. School of Natural Resources and the Environment, University of Arizona, United States;2. Southwest Watershed Research Center, USDA-ARS, United States;1. Department of Geography, Philipps-University of Marburg, Deutschhausstr. 10, 35037 Marburg, Germany;2. Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115 Bonn, Germany;1. Faculty of Geography, Philipps-University Marburg, Deutschhausstr. 10, 35037 Marburg, Germany;2. Senckenberg Museum of Natural History, Am Museum 1, 02826 Görlitz, Germany;1. Farming Systems Ecology, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands;2. Instituto De Investigaciones Forestales y Agropecuarias Bariloche (IFAB), INTA-CONICET, Av. Modesta Victoria 4450 (8400), Bariloche, Río Negro, Argentina;3. Instituto Nacional de Tecnología Agropecuaria (INTA), Agencia de Extensión Rural Zapala, Av. Avellaneda 1165 (8340), Zapala, Neuquén, Argentina;4. Groningen Institute of Evolutionary Life Sciences, Groningen University, PO Box 11103, 9700 CC Groningen, the Netherlands;5. Agroécologie et Intensification Durable (AIDA), Centre de coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Université de Montpellier, 34000 Montpellier, France |
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Abstract: | Rangelands are among the most extensive anthropogenic landscapes on earth, supporting nearly 500 million people. Disagreements over the extent and severity of rangeland degradation affect pastoralist livelihoods, especially when impacts of drought and over-grazing are confounded. While vegetation indices (such as NDVI, or Normalized Difference Vegetation Index) derived from remotely sensed imagery are often used to monitor rangelands, their strategic integration with local ecological knowledge (LEK) is under-appreciated. Here, we explore these complementary approaches in Kyrgyzstan’s pasture-rich province of Naryn, where disagreements regarding pasture degradation could greatly benefit from additional information. We examine a time series of MODIS satellite imagery (2000–2015) to characterize browning trends in vegetation as well as to distinguish between climate- and grazing-induced trends. We also compare and contrast measured trends with LEK perceptions of pasture degradation. To do so, we first examine statistical trends in NDVI as well as in NDVI residuals after de-trending with meteorological data. Second, we use participatory mapping to identify areas local pasture managers believe are overgrazed, a particularly useful approach in lieu of reliable historical stocking rates for livestock in this region. Lastly, we compare the strengths and weaknesses of LEK and remote sensing for landscape monitoring.Browning trends were widespread as declining trends in NDVI (and NDVI residuals) covered 24% (and 9%) of the landscape, respectively. Local managers’ perceptions of pasture degradation better reflected trends seen in NDVI than in climate-controlled NDVI residuals, suggesting patterns in the latter are less apparent to managers. Our approach demonstrated great potential for the integration of two inexpensive and effective methods of rangeland monitoring well-suited to the country’s needs. Despite limitations due to terrain, our approach was most successful within the semi-arid steppe where pasture degradation is believed to be most severe. In many parts of the world, sources of long-term spatially extensive data are rare or even non-existent. Thus, paired LEK and remote sensing can contribute to comprehensive and informative assessments of land degradation, especially where contentious management issues intersect with sparse data availability. LEK is a valuable source of complementary information to remote sensing and should be integrated more routinely and formally into landscape monitoring. To aid this endeavor, we synthesize advice for linking LEK and remote sensing across diverse landscape situations. |
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Keywords: | NDVI Satellite imagery Vegetation indices Kyrgyzstan Grasslands Pasture Productivity Land degradation MODIS Central Asia Pastoralism Traditional ecological knowledge (TEK) Participatory mapping Participatory GIS Landscape change Montane landscapes Long-term monitoring Times series analysis Post-Soviet |
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