%0 Journal Article
%T Genetic algorithm-based method for forest type classification using multi-temporal NDVI from Landsat TM imagery
%A Guonian L¨¹
%A Hong Tao
%A Manqi Li
%A Ming Wang
%J Annals of GIS
%D 2019
%R https://doi.org/10.1080/19475683.2018.1552621
%X ABSTRACT Remote-sensing technology has been a useful tool for mapping and characterizing forest cover types and species composition, providing valuable information for effective forest management. This study investigates the application of a genetic algorithm (GA)-based approach on Normalized Difference Vegetation Index (NDVI) to separate local forest communities at Huntington Wildlife Forest (HWF), located in New York State of the United States, into deciduous, mixed/coniferous and nonforests using Landsat TM imagery. Overall accuracy, producer¡¯s accuracy, user¡¯s accuracy and kappa coefficient of agreement are employed to assess the performance of the proposed method. Its overall effectiveness is supported by the accuracy of 80.41% and kappa coefficient of 0.56, and its capability of separating the forest cover types is endorsed by the class-wise accuracy measures. This method shows advantages in its limited demands for input features, that only multi-temporal NDVI indices are required; and in its simple and efficient mechanism, which refers to threshold optimization and feature selection
%U https://www.tandfonline.com/doi/full/10.1080/19475683.2018.1552621