- Remote sensing, which is a common method to examine land-use/land-cover (LULC) changes, could be useful in the analysis of livestock ecosystem transformations. In the last two decades, before Landsat images were free, developing countries could not afford monitoring through remote sensing because of the high cost of acquiring satellite imagery and commercial software. However, Landsat time series nowadays allows the characterization of changes in vegetation across large areas over time. The aim of this study is to analyse the LULC changes affecting forest frontiers and traditional silvopastoral systems (TSPS) in a representative livestock area of Nicaragua. Nearly cloud-free Landsat scenes - a Landsat 5 Thematic Mapper (TM) scene from 1986 and a Landsat 8 Operational Land Imager (OLI) scene from 2015 - have been the data sets used in the study. A process chain following a four-step definition of the remote-sensing process was conceptually developed and implemented based onfree open source software components and by applying the randomRemote sensing, which is a common method to examine land-use/land-cover (LULC) changes, could be useful in the analysis of livestock ecosystem transformations. In the last two decades, before Landsat images were free, developing countries could not afford monitoring through remote sensing because of the high cost of acquiring satellite imagery and commercial software. However, Landsat time series nowadays allows the characterization of changes in vegetation across large areas over time. The aim of this study is to analyse the LULC changes affecting forest frontiers and traditional silvopastoral systems (TSPS) in a representative livestock area of Nicaragua. Nearly cloud-free Landsat scenes - a Landsat 5 Thematic Mapper (TM) scene from 1986 and a Landsat 8 Operational Land Imager (OLI) scene from 2015 - have been the data sets used in the study. A process chain following a four-step definition of the remote-sensing process was conceptually developed and implemented based onfree open source software components and by applying the random forest (RF) algorithm. A conceptual LULC classification scheme representing TSPS was developed. Although the imagery shows a heterogeneous surface cover and mixed pixels, it is possible to achieve promising classification results with the RF algorithm with out-of-the-bag (OOB) errors below 13% for both images along with an overall accuracy level of 85.9% for the 2015 subset and 85.2% for the 1986 subset. The classification shows that from 1986 to 2015 (29years) the intervened secondary forest (ISF) increased 2.6 times, whereas the degraded pastures decreased by 34.5%. The livestock landscape in Matiguas is in a state of constant transformation, but the main changes head towards the positive direction of tree-cover recovery and an increased number of areas of natural regeneration.…
