Browsing by Author "Shinde, Nilesh"

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    Mapping Land use and Land Cover change on Brazil's land tenure categories using Google Earth Engine
    Shinde, Nilesh (Virginia Tech, 2021-04-30)
    Brazilian territory expands up to 851.6 million ha, of which 44.2% land is privately owned, and 36.1% land is public owned.The privately owned land are registed under the nationwide tenure registries such as Cadastro Ambiental Rural (CAR), Land Management System (SIGEF), Terra Legal, Quilombola territory. The public land comes under Indigenous Reserves, Conservation Units, Communitary Territory, Military Land and Rural Settlement. In this paper, we employ 4.5 million property-level locations to understand the trajectory of land use and land cover change across Brazil’s Land tenure categories. Using Google Earth Engine (GEE), we employ MapBiomas collection 5 landuse data from 1985-2019. The paper provides the first quantitative and spatially explicit assessment of the coverage, gaps, and uncertainties in the land use categories across variety of land tenure status of the entire Brazilian territory. Data is organized in the most detailed property level, but it allows integration in the various jurisdiction levels where land policy and decision occur, from the municipal to the federal scale.
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    Non-industrial private forest expansion in Andhra Pradesh
    Wynne, Randolph H.; Thomas, Valerie A.; Schons Do Valle, Stella Zucchetti; Gundimeda, Haripriya; Cobourn, Kelly M.; Amacher, Gregory S.; Köhlin, Gunnar; Williams, Paige; More, Snehal; Shinde, Nilesh (2019-04-09)
    Outside forests, Andhra Pradesh is greening, and we are learning by how much— and why. Our objectives are to (1) map smallholder forest plantations in Andhra Pradesh using multitemporal HLS S10 and/or very-high spatial resolution commercial satellite data, and (2) determine the drivers of plantation forest establishment. We have (1) developed a land use model that integrates land quality and spatial aspects of the farm with market variables and farmer production decisions, (2) implemented a household-level socio-economic survey, and (3) completed a Sentinel 2-era classification that separates natural from planted forest with 94% accuracy. Plantation forestry is rapidly expanding in Asia, and understanding the extent, drivers, and ramifications of these new trees outside forests is vital.