Integration of ALOS PALSAR and Landsat Data for Land Cover and Forest Mapping in Northern Tanzania

Dorothea Deus
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Dorothea Deus: Department of Geospatial Sciences and Technology, School of Earth Sciences, Real Estate, Business Studies and Informatics (SERBI), Ardhi University, P.O. Box 35176, Dar es Salaam, Tanzania

Land, 2016, vol. 5, issue 4, 1-19

Abstract: Land cover and forest mapping supports decision makers in the course of making informed decisions for implementation of sustainable conservation and management plans of the forest resources and environmental monitoring. This research examines the value of integrating of ALOS PALSAR and Landsat data for improved forest and land cover mapping in Northern Tanzania. A separate and joint processing of surface reflectance, backscattering and derivatives (i.e., Normalized Different Vegetation Index (NDVI), Principal Component Analysis (PCA), Radar Forest Deforestation Index (RFDI), quotient bands, polarimetric features and Grey Level Co-Occurrence Matrix (GLCM) textures) were executed using Support Vector Machine (SVM) classifier. The classification accuracy was assessed using a confusion matrix, where Overall classification Accuracy (OA), Kappa Coefficient (KC), Producer’s Accuracy (PA), User’s Accuracy (UA) and F 1 score index were computed. A two sample t-statistics was utilized to evaluate the influence of different data categories on the classification accuracy. Landsat surface reflectance and derivatives show an overall classification accuracy (OA = 86%). ALOS PALSAR backscattering could not differentiate the land cover classes efficiently (OA = 59%). However, combination of backscattering, and derivatives could differentiate the land cover classes properly (OA = 71%). The attained results suggest that integration of backscattering and derivative has potential of utilization for mapping of land cover in tropical environment. Integration of backscattering, surface reflectance and their derivative increase the accuracy (OA = 97%). Therefore it can be concluded that integration of ALOS PALSAR and optical data improve the accuracies of land cover and forest mapping and hence suitable for environmental monitoring.

Keywords: integration; Landsat; ALOS PALSAR; support vector machine; land cover classification; forest (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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