Quantifying the sensitivity of L-Band SAR to a decade of vegetation structure changes in savannas

被引:5
|
作者
Wessels, Konrad [1 ]
Li, Xiaoxuan [1 ]
Bouvet, Alexandre [2 ]
Mathieu, Renaud [3 ]
Main, Russell [4 ]
Naidoo, Laven [5 ]
Erasmus, Barend [6 ]
Asner, Gregory P. [7 ]
机构
[1] George Mason Univ, Dept Geog & GeoInformat Sci, Fairfax, VA 22030 USA
[2] Univ Toulouse, CNRS, CNES, INRAE,IRD,UPS,Ctr Etud Spatiales Biosphere CESBIO, Toulouse, France
[3] Int Rice Res Inst, South Asia Res Ctr, Varanasi, India
[4] Council Sci & Ind Res CSIR, Pretoria, South Africa
[5] Gauteng City Reg Observ GCRO, Johannesburg, South Africa
[6] Univ Pretoria, Fac Nat & Agr Sci, Pretoria, South Africa
[7] Arizona State Univ, Ctr Global Discovery & Conservat Sci, Tempe, AZ USA
基金
美国安德鲁·梅隆基金会;
关键词
SAR; Savannas; L; -Band; LiDAR; Vegetation structure; ALOS PALSAR; South Africa; FOREST ABOVEGROUND BIOMASS; ALOS PALSAR DATA; AFRICAN SAVANNAS; WOODY COVER; RADAR BACKSCATTER; SOUTH-AFRICA; LARGE TREES; ENCROACHMENT; WOODLANDS; LIDAR;
D O I
10.1016/j.rse.2022.113369
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Global savannas are the third largest carbon sink with large human populations being highly dependent on their ecosystem services. However, savannas are changing rapidly due to climate change, fire, animal management, and intense fuelwood harvesting. In southern Africa, large trees (>5 m in height) are under threat while shrub cover (<3 m) is increasing. The collection of multi-date airborne LiDAR (ALS) data, initiated over a decade ago in the Lowveld of South Africa, provided a rare opportunity to quantify the ability of L-band SAR to track changes in savanna vegetation structure and this study is the first to do so, to our knowledge. The objective was to test the ability of ALOS PALSAR 1&2, dual-pol (HH, HV) data to quantify woody cover and volume change in savannas over 2-, 8-and 10-year periods through comparison to ALS. For each epoch (2008, 2010, 2018), multiple PALSAR images were processed to Gamma0 (gamma 0) at 15 m resolution with multi-temporal speckle filtering. ALS data were processed to fractional canopy cover and volume, and then compared to 5 x 5 aggregated (75 m) SAR mean gamma 0. The ALS cover change (Delta CALS) and volume change between pairs of years were highly correlated, with (R2 > 0.8), thus results for cover change applied equally to volume change. Cover change was predicted using (i) direct backscatter change or (ii) the difference between annual cover map product derived using the Bayesian Water Cloud Model (BWCM) and logarithmic models. The linear relationship between Delta gamma 0 and Delta CALS varied between year pairs but reached a maximum R2 of 0.7 for 2018-2010 and a moderate R2 of 0.4 for 2018-2008. Overall, 1 dB Delta gamma 0 corresponded to approximately 0.1 cover change. The three cover change models had very similar uncertainties with mean RMSE = 0.15, which is 13% of the observed cover change range (-0.6 to +0.6). The direct backscatter change approach had less underestimation of positive and negative cover change. The L -band backscatter had a higher sensitivity than suggested by previous studies, as it was able to reliably distinguish cover change at 0.25 increments. The SAR-derived cover change maps detected the loss of stands of big trees, and widespread increases in cover of 0.35-0.65 in communal rangelands due to shrub encroachment. In contrast, the maps suggest that cover generally decreased in conservation areas, forming distinct fence-line effects, potentially caused by significant increases in elephant numbers and frequent, intense wildfires in reserves.
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