The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 369–372, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-369-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 369–372, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-369-2020

  06 Nov 2020

06 Nov 2020

RECENT LAND USE AND LAND COVER CHANGE DYNAMICS IN THE GRAN CHACO AMERICANO

S. Banchero1, D. de Abelleyra1, S. R. Veron1, M. J. Mosciaro2, F. Arévalos3, and J. N. Volante2 S. Banchero et al.
  • 1Instituto de Clima y Agua, Instituto Nacional de Tecnología Agropecuaria (INTA) Hurlingham, Argentina
  • 2Estación Experimental Salta, Instituto Nacional de Tecnología Agropecuaria (INTA) Salta, Argentina
  • 3Asociación Guyra Paraguay, Asunción, Paraguay

Keywords: LULC, change dynamics, visual interpretation, machine learning

Abstract. Land transformation is one of the most significant human changes on the Earth’s surface processes. Therefore, land use land cover time series are a key input for environmental monitoring, natural resources management, territorial planning enforcement at national scale. We here capitalize from the MapBiomas initiative to characterize land use land cover (LULC) change in the Gran Chaco between 2010 and 2017. Specifically we sought to a) quantify annual changes in the main LULC classes; b) identify the main LULC transitions and c) relate these transitions to current land use policies. Within the MapBiomas project, Landsat based annual maps depicting natural woody vegetation, natural herbaceous vegetation, dispersed natural vegetation, cropland, pastures, bare areas and water. We used Random Forest machine learning algorithms trained by samples produced by visual interpretation of high resolution images. Annual overall accuracy ranged from 0,73 to 0,74. Our results showed that, between 2010 and 2017, agriculture and pasture lands increased ca. 3.7 Mha while natural forestry decreased by 2.3 Mha. Transitions from forests to agriculture accounted for 1.14% of the overall deforestation while 86% was associated to pastures and natural herbaceous vegetation. In Argentina, forest loss occurred primarily (39%) on areas non considered by the territorial planning Law, followed by medium (33%), high (19%) and low (9%) conservation priority classes. These results illustrate the potential contribution of remote sensing to characterize complex human environmental interactions occurring over extended areas and timeframes.