Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data

Saeedeh Eskandari*, Mohammad Reza Jaafari, Patricia Oliva, Omid Ghorbanzadeh, Thomas Blaschke

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in FachzeitschriftArtikel

Abstract

The Zagros forests in Western Iran are valuable ecosystems that have been seriouslydamaged by human interference (harvesting the wood and forest sub-products, converting the foreststo the agricultural lands, and grazing) and natural events (drought events and fire). In this study, wegenerated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests ofShirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field datafor protective management. First, we assessed the accuracy of Google Earth data using 300 randomfield plots in 10 different land cover types. For land cover mapping, we evaluated the performance offour supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD),neural network (NN), and support vector machine (SVM)). The accuracy of the land cover mapswas assessed using a set of 150 stratified random plots in Google Earth. We mapped the forestcanopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. Wecalculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots).The linear regression between the NDVI values and the TCC% was used to obtain the predictivemodel of TCC% based on the NDVI. The results showed that Google Earth data yielded an overallaccuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopycover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, whichis highly significant. The results also showed that the linear regression model is a good predictivemodel for TCC% estimation based on the NDVI (r2=0.864). The results can be used as a baseline fordecision-makers to monitor land cover change in the region, whether produced by human activitiesor natural events and to establish measures for protective management of forests.
OriginalspracheEnglisch
Aufsatznummer1912
FachzeitschriftRemote Sensing
Jahrgang12
Ausgabenummer12
DOIs
PublikationsstatusVeröffentlicht - 12 Jun 2020

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