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Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest's Successional Stages

文献类型： 外文期刊

作者： Zhao, Genping 1 ; Sanchez-Azofeifa, Arturo 2 ; Laakso, Kati 2 ; Sun, Chuanliang 3 ; Fei, Lunke 1 ;

作者机构： 1.Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, Peoples R China

2.Univ Alberta, Dept Earth & Atmospher Sci, Ctr Earth Observat Sci, Edmonton, AB T6G 2E3, Canada

3.Jiangsu Acad Agr Sci, Inst Agr Informat, Nanjing 210014, Peoples R China

关键词： tropical dry forests; succession mapping; hyperspectral; full-waveform LiDAR; randomized nonlinear archetypal analysis (RNAA); relative attribute learning (RAL)

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2021 年 13 卷 19 期

页码：

收录情况： SCI

摘要： Accurate estimation of the degree of regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing and light detection and ranging (LiDAR) have been used to characterize the deterministic successional stages in a TDF. These successional stages, classified as early, intermediate, and late, are considered a proxy for mapping the age since the abandonment of a given forest area. Expanding on the need for more accurate successional forest mapping, our study considers the age attributes of a TDF study area as a continuous expression of relative attribute scores/levels that vary along the process of ecological succession. Specifically, two remote-sensing data sets: HyMap (hyperspectral) and LVIS (waveform LiDAR), were acquired at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS) in Costa Rica, were used to generate age-attribute metrics. These metrics were then used as entry-level variables on a randomized nonlinear archetypal analysis (RNAA) model to select the most informative metrics from both data sets. Next, a relative attribute learning (RAL) algorithm was adapted for both independent and fused metrics to comparatively learn the relative attribute levels of the forest ages of the study area. In this study, four HyMap indices and five LVIS metrics were found to have the potential to map the forest ages of the study area, and compared with these results, a significant improvement was found through the fusion of the metrics on the accuracy of the generated forest age maps. By linking the age group mapping and the relative attribute mapping results, a dynamic gradient of the age-attribute transition patterns emerged.

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