Abstract
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.
Highlights
In Argentina, the information related to land use and land cover is characterized by its scarcity, diversity of scales, and poor data quality
The National Institute of Agricultural Technology (INTA) published the first map on land cover in Argentina using exploratory scale (Volante et al, 2010), being the first national map of this type. This cartography information was produced from field data and visual interpretation of Landsat and MODIS images, which involved great effort and time of work
We present some results that MapBiomas Chaco Project for the Collection 1 that complete a time series cartography of land use and land cover between 2010 and 2017 using Earth Engine cloud computing technology to process Landsat data archive
Summary
In Argentina, the information related to land use and land cover is characterized by its scarcity, diversity of scales, and poor data quality. Agricultural production data (acreage and yield) comes from subjective surveys with unknown accuracy level This information is reported at department level and hampers detailed and updated territorial synopsis on land use dynamics. The National Institute of Agricultural Technology (INTA) published the first map on land cover in Argentina using exploratory scale (Volante et al, 2010), being the first national map of this type This cartography information was produced from field data and visual interpretation of Landsat and MODIS images, which involved great effort and time of work. The LULC maps were based on the random forest algorithm
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