Mapping forest canopy height globally with spaceborne lidar
TLDR
In this article, a wall-to-wall, global map of canopy height at 1-km spatial resolution, using 2005 data from the Geoscience Laser Altimeter System (GLAS) aboard ICESat (Ice, Cloud, and land Elevation Satellite).Abstract:
[1] Data from spaceborne light detection and ranging (lidar) opens the possibility to map forest vertical structure globally. We present a wall-to-wall, global map of canopy height at 1-km spatial resolution, using 2005 data from the Geoscience Laser Altimeter System (GLAS) aboard ICESat (Ice, Cloud, and land Elevation Satellite). A challenge in the use of GLAS data for global vegetation studies is the sparse coverage of lidar shots (mean = 121 data points/degree2 for the L3C campaign). However, GLAS-derived canopy height (RH100) values were highly correlated with other, more spatially dense, ancillary variables available globally, which allowed us to model global RH100 from forest type, tree cover, elevation, and climatology maps. The difference between the model predicted RH100 and footprint level lidar-derived RH100 values showed that error increased in closed broadleaved forests such as the Amazon, underscoring the challenges in mapping tall (>40 m) canopies. The resulting map was validated with field measurements from 66 FLUXNET sites. The modeled RH100 versus in situ canopy height error (RMSE = 6.1 m, R2 = 0.5; or, RMSE = 4.4 m, R2 = 0.7 without 7 outliers) is conservative as it also includes measurement uncertainty and sub pixel variability within the 1-km pixels. Our results were compared against a recently published canopy height map. We found our values to be in general taller and more strongly correlated with FLUXNET data. Our map reveals a global latitudinal gradient in canopy height, increasing towards the equator, as well as coarse forest disturbance patterns.read more
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Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
Peng Gong,Jie Wang,Le Yu,Yongchao Zhao,Yuanyuan Zhao,Lu Liang,Zhenguo Niu,Xiaomeng Huang,Haohuan Fu,Shuang Liu,Congcong Li,Xueyan Li,Wei Fu,Caixia Liu,Yue Xu,Xiaoyi Wang,Qu Cheng,Luanyun Hu,Wenbo Yao,Han Zhang,Peng Zhu,Ziying Zhao,Haiying Zhang,Yaomin Zheng,Luyan Ji,Yawen Zhang,Han Chen,An Yan,JianHong Guo,Liang Yu,Lei Wang,Xiaojun Liu,Tingting Shi,Menghua Zhu,Yanlei Chen,Guangwen Yang,Ping Tang,Bing Xu,Chandra Giri,Nicholas Clinton,Zhiliang Zhu,Jin Chen,Jun Chen +42 more
TL;DR: In this article, the first 30 m resolution global land cover maps using Landsat Thematic Mapper TM and enhanced thematic mapper plus ETM+ data were produced. And the authors used four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier.
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Positive biodiversity-productivity relationship predominant in global forests
Jingjing Liang,Thomas W. Crowther,Nicolas Picard,Susan K. Wiser,Mo Zhou,Giorgio Alberti,Ernst Detlef Schulze,A. David McGuire,Fabio Bozzato,Hans Pretzsch,Sergio de-Miguel,Alain Paquette,Bruno Hérault,Michael Scherer-Lorenzen,Christopher B. Barrett,Henry B. Glick,Geerten M. Hengeveld,Gert-Jan Nabuurs,Sebastian Pfautsch,Helder Viana,Helder Viana,Alexander Christian Vibrans,Christian Ammer,Peter Schall,David David Verbyla,N. M. Tchebakova,Markus Fischer,James V. Watson,Han Y. H. Chen,Xiangdong Lei,Mart-Jan Schelhaas,Huicui Lu,Damiano Gianelle,Elena I. Parfenova,Christian Salas,Eungul Lee,Boknam Lee,Hyun-Seok Kim,Helge Bruelheide,David A. Coomes,Daniel Piotto,Terry Sunderland,Terry Sunderland,Bernhard Schmid,Sylvie Gourlet-Fleury,Bonaventure Sonké,Rebecca Tavani,Jun Zhu,Susanne Brandl,Jordi Vayreda,Fumiaki Kitahara,Eric B. Searle,Victor J. Neldner,Michael R. Ngugi,Christopher Baraloto,Christopher Baraloto,Lorenzo Frizzera,Radomir Bałazy,Jacek Oleksyn,Jacek Oleksyn,Tomasz Zawiła-Niedźwiecki,Olivier Bouriaud,Filippo Bussotti,Leena Finér,Bogdan Jaroszewicz,Tommaso Jucker,Fernando Valladares,Fernando Valladares,Andrzej M. Jagodziński,Pablo Luis Peri,Pablo Luis Peri,Pablo Luis Peri,Christelle Gonmadje,William Marthy,Timothy G. O'Brien,Emanuel H. Martin,Andrew R. Marshall,Francesco Rovero,Robert Bitariho,Pascal A. Niklaus,Patricia Alvarez-Loayza,Nurdin Chamuya,Renato Valencia,Frédéric Mortier,Verginia Wortel,Nestor L. Engone-Obiang,Leandro Valle Ferreira,David E. Odeke,R. Vásquez,Simon L. Lewis,Simon L. Lewis,Peter B. Reich,Peter B. Reich +92 more
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A high-accuracy map of global terrain elevations
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The Community Land Model version 5 : description of new features, benchmarking, and impact of forcing uncertainty
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TL;DR: A global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer using circa- 2000 and 2005 Landsat images, incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas.
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