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, 195–200, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-195-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 195–200, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-195-2020

  06 Nov 2020

06 Nov 2020

COVERAGE CHANGES DETECTION AT CIÉNAGA GRANDE, SANTA MARTA – COLOMBIA USING AUTOMATIC CLASSIFICATION

J. S. Vinasco, D. A. Rodríguez, S. Velásquez, D. F. Quintero, L. R. Livni, and F. L. Hernández J. S. Vinasco et al.
  • Remote Sensing Research Group, Universidad del Valle, Santiago de Cali, Colombia

Keywords: Automatic classification, Neural Network Analysis, Random Forest, Landsat 8, vegetation index, NDVI, EVI, NDWI

Abstract. The Ciénaga Grande, Santa Marta is the largest and most diverse ecosystem of its kind in Colombia. Its primary function is acting as a filter for the organic carbon cycle. Recently, this place has been suffering disruptions due to the anthropic activities taking place in its surroundings. The present study, the changes in the surface of Ciénaga Grande, Santa Marta, Magdalena, Colombia between 2013 and 2018 were determined using semiautomatic detection methods with high resolution data from remote sensors (Landsat 8). The zone of studies was classified in six kinds of surfaces: 1) artificial territories, 2) agricultural territories, 3) forests and semi-natural areas, 4) wet areas, 5) deep water surfaces & 6) wich is related to clouds as a masking method. Random Forest classifiers were utilized and the Feed For Ward multilayer perceptron neuronal network (ANN) was simultaneously assessed. The training stage for both methods was performed with 300 samples, distributed in equal quantities, over each coverage class. The semi-automatic classification was carried out with an annual frequency, but the monitoring was carried out throughout the analysis period through the performance of three indicators Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI). It was found from the confusion matrix that the Random Forest method more accurately classified four classes while Neural Networks Analysis (NNA) just three. Finally, taking the Random Forest results into account, it was found that the agricultural expansion increased from 7% to 9% and the urban zone increased from 20% to 30% of the total area. As well as a decrease of damp areas from 27% to 12% and forests from 4% to 3% of the total area of study.