Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 41, Issue 22, Pages 8682-8692Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/2150704X.2020.1802529
Keywords
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Funding
- National Science Foundation [1825046]
- Division Of Behavioral and Cognitive Sci
- Direct For Social, Behav & Economic Scie [1825046] Funding Source: National Science Foundation
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Amazonian tropical rainforest is being converted to other land cover types including crops and pasture. In deforested areas, secondary forest grows after pastures are abandoned, and 'dirty pasture' that has trees and shrubs but is actively used for grazing are also regionally important land cover types following forest conversion. This study describes a multistage process land cover classification method to map primary forest, secondary forest, pasture, pasture with trees, built and water in the Brazilian state of Rondonia. A recently developed Spectral Variability Vegetation Index (SVVI) is tested to discriminate land cover types with differing tree cover amounts. Random Forest classifier (RF) is applied to inputs from a) spectral mixture analysis (SMA), and b) tasselled cap (TC) transformation, both with and without SSVI as an additional input feature. SVVI improved the classification accuracy from 73% (TC) to 85% (TC-SVVI), and TC-SVVI yielded a land cover map with higher accuracy than that from SMA-SVVI (82%). Pasture-with-trees, secondary forest and primary forest were all distinguishable with the SVVI. Pasture-with-trees accounted for 67% of all pastures, demonstrating its importance for regional land cover. This land cover classification workflow with the SVVI index improves the accuracy of mapping heterogeneous tropical land cover types.
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