Modeling forest biomass using Very-High-Resolution data - Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images

被引：42

作者：

Maack, Joachim ^{[1
]}

Kattenborn, Teja ^{[2
]}

Fassnacht, Fabian Ewald ^{[2
]}

Enssle, Fabian ^{[1
]}

Hernandez, Jaime ^{[3
]}

Corvalan, Patricio ^{[3
]}

Koch, Barbara ^{[1
]}

机构：

[1] Univ Freiburg, Chair Remote Sensing & Landscape Informat Syst Fe, Freiburg, Germany

[2] Karlsruhe Inst Technol, Inst Geog & Geoecol IfGG, D-76021 Karlsruhe, Germany

[3] Univ Chile, Lab Geomat Ecol & Paisaje, Santiago, Chile

来源：

EUROPEAN JOURNAL OF REMOTE SENSING | 2015年 / 48卷

关键词：

Biomass modelling; WordView-2; Pleiades; random forest; photogrammetry; canopy height models; ABOVEGROUND BIOMASS; CANOPY HEIGHT; CROSS-VALIDATION; LIDAR; BOOTSTRAP;

D O I：

10.5721/EuJRS20154814

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

We used spectral, textural and photogrammetric information from very-high resolution (VHR) stereo satellite data (Pleiades and WorldView-2) to estimate forest biomass across two test sites located in Chile and Germany. We compared Random Forest model performances of different predictor sets (spectral, textural, and photogrammetric), forest inventory designs and filter sizes (texture information). Best model performances were obtained with photogrammetric combined with either textural or spectral information and smaller, but more field plots. Stereo-VHR images showed a great potential for canopy height model (CHM) generation and could be an adequate alternative to LiDAR and InSAR techniques.

引用

页码：245 / 261

页数：17

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