Abstract
This paper aims to (i) optimize the application of multiple bands of satellite images for land cover classification by using random forest algorithms and (ii) assess correlations and regression of vegetation indices of a better-performed land cover classification image with vertical and horizontal structures of tropical lowland forests in Central Vietnam. In this study, we used Sentinel-2 and Landsat-8 to classify seven land cover classes of which three forest types were substratified as undisturbed, low disturbed, and disturbed forests where forest inventory of 90 plots, as ground-truth, was randomly sampled to measure forest tree parameters. A total of 3226 training points were sampled on seven land cover types. The performance of Landsat-8 showed out-of-bag error of 31.6%, overall accuracy of 68%, kappa of 67.5%, while Sentinel-2 showed out-of-bag error of 14.3% and overall accuracy of 85.7% and kappa of 83%. Ten vegetation indices of the better-performed image were extracted to find out (i) the correlation and regression of horizontal and vertical structures of trees and (ii) assess the variation values between ground-truthing plots and training sample plots in three forest types. The result of thettest on vegetation indices showed that six out of ten vegetation indices were significant atp<0.05. Seven vegetation indices had a correlation with the horizontal structure, but four vegetation indices, namely, Enhanced Vegetation Index, Perpendicular Vegetation Index, Difference Vegetation Index, and Transformed Normalized Difference Vegetation Index, had better correlationsr = 0.66, 0.65, 0.65, 0.63 and regression results were ofR2 = 0.44, 0.43, 0.43, and 0.40, respectively. The correlations of tree height werer = 0.46, 0.43, 0.43, and 0.49 and its regressions were ofR2 = 0.21, 0.19, 0.18, and 0.24, respectively. The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application.
Highlights
Forests, at a nationwide scale, need a monitoring system as fundamental tools to support the management of landscapes, land use, ecosystem, and biodiversity for multiple production purposes including national forest inventory (NFI) and international conventions [1,2,3,4]
Remote sensing images provide potential information on tropical landscape forests and land use types [18] where the landscape is defined as a heterogeneous land area from a set of a cluster of interacting ecosystems that repeat in similar shape throughout and as an area that is spatially heterogeneous in at least one factor of interests [19, 20]. e structure of forest has a relation to spatial distribution and as an important factor of forest ecological processes which supports to give more patterns of some taxa and even the disturbance status [21, 22]
Vegetation indices (VIs) are essential for vegetation cover classification captured from the radiometric biophysical derivation and vegetation structure. ese indices contribute to land use planning and manage natural resources management and provide to policy making [28,29,30,31]
Summary
At a nationwide scale, need a monitoring system as fundamental tools to support the management of landscapes, land use, ecosystem, and biodiversity for multiple production purposes including national forest inventory (NFI) and international conventions [1,2,3,4]. Remote sensing images provide potential information on tropical landscape forests and land use types [18] where the landscape is defined as a heterogeneous land area from a set of a cluster of interacting ecosystems that repeat in similar shape throughout and as an area that is spatially heterogeneous in at least one factor of interests [19, 20]. No studies have been conducted applying RF for land cover classification in combination with extracted vegetation indices to stratify forest structures and to compare the values of vegetation indices between the ground-truth and training sample plots of VIs from the best-performed satellite images in the tropical lowland forests in Vietnam. Is paper aims to (i) compare the application of multiple band Sentinel-2 and Landsat-8 images for land cover classification by applying RF and (ii) evaluate the correlation and regression of vegetation indices forest vertical and horizontal structures with the abovementioned two sensors in the tropical lowland forests in Central Vietnam. Four forest types of the lowland forest consist of evergreen closed, semideciduous closed, deciduous closed and closed, and hard leaved that echoed the development of lowland tropical forests in Vietnam since 1960s. e four-forest-type classification method of Germany introduced by Loeschau to Vietnam in 1959 has been widely applied [46, 47]. e rich forest distributes in the very remote and high terrain area where forests are protected and forest structure is well-preserved, called undisturbed forest (UF), which had the basal area of about 30 m2·ha−1 [48]. e UF is dominated by Fagaceae, Lauraceae, Dipterocarpaceae, Leguminosae, and Meliaceae. e second forest type is classified as a medium forest where the forest has been somehow slightly logged and disturbed, canopy fragmentation exists, and its structure is somehow maintained with the basal area ranging from 21–26 m2·ha−1 [49]. is forest type is classified as less disturbed forest (LF), which is dominated by Fagaceae, Lauraceae, Euphorbiaceae, and Sapidaceae. e third type is secondary forest, where forest is heavily disturbed (DF), which is dominated with Myristicaceae, Clusiaceae, Annonaceae, Euphorbiaceae, and Myrtaceae [45, 50] with the basal area from 10–21 m2·ha−1 [49]
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