Deep learning for land use and land cover classification from the Ecuadorian Paramo.

Abstract

The paramo, plays an important role in our ecosystems as They balance the water resources and can retain substantial quantities of carbon. This research was carried out in the province of Tungurahua, specifically the Quero district. The aim is to develop a classification of the land use land cover (LULC) in the paramo using satellite imagery using several classifiers and determine which one obtains the best performance, for which three different approaches were applied: Pixel-Based Image Analysis (PBIA), Geographic Object-Based Image Analysis (GEOBIA), and a Deep Neural Network (DNN). Various parameters were used, such as the Normalized Difference Vegetation Index (NDVI), the Bare Soil Index (BSI), texture, altitude, and slope. Seven classes were used: paramo, pasture, crops, herbaceous vegetation, urban, shrubrainland, and forestry plantations. The data was obtained with the help of onsite technical experts, using geo-referencing and reference maps. Among the models used the highest-ranked was DNN with an overall precision of 87.43%, while for the paramo class specifically, GEOBIA reached a precision of 95%.

Keywords

• classification
• land use and land cover
• pixel-based image analysis
• geographic object-based image analysis
• deep neural network