Abstract
Effective monitoring of changes in the geographic distribution of cryospheric vegetation requires high-resolution and accurate baseline maps. The rationale of the present study is to compare multiple feature extraction approaches to remotely mapping vegetation in Antarctica, assessing which give the greatest accuracy and reproducibility relative to those currently available. This study provides precise, high-resolution, and refined baseline information on vegetation distribution as is required to enable future spatiotemporal change analyses of the vegetation in Antarctica. We designed and implemented a semiautomated customized normalized difference vegetation index (NDVI) approach for extracting cryospheric vegetation by incorporating very high resolution (VHR) 8-band WorldView-2 (WV-2) satellite data. The viability of state-of-the-art target detection, spectral processing/matching, and pixel-wise supervised classification feature extraction techniques are compared with the customized NDVI approach devised in this study. An extensive quantitative and comparative assessment was made by evaluating four semiautomatic feature extraction approaches consisting of 16 feature extraction standalone methods (four customized NDVI plus 12 existing methods) for mapping vegetation on Fisher Island and Stornes Peninsula in the Larsemann Hills, situated on continental east Antarctica. The results indicated that the customized NDVI approach achieved superior performance (average bias error ranged from ~6.44 ± 1.34% to ~11.55 ± 1.34%) and highest statistical stability in terms of performance when compared with existing feature extraction approaches. Overall, the accuracy analysis of the vegetation mapping relative to manually digitized reference data (supplemented by validation with ground truthing) indicated that the 16 semi-automatic mapping methods representing four general feature extraction approaches extracted vegetated area from Fisher Island and Stornes Peninsula totalling between 2.38 and 3.72 km2 (2.85 ± 0.10 km2 on average) with bias values ranging from 3.49 to 31.39% (average 12.81 ± 1.88%) and average root mean square error (RMSE) of 0.41 km2 (14.73 ± 1.88%). Further, the robustness of the analyses and results were endorsed by a cross-validation experiment conducted to map vegetation from the Schirmacher Oasis, East Antarctica. Based on the robust comparative analysis of these 16 methods, vegetation maps of the Larsemann Hills and Schirmacher Oasis were derived by ensemble merging of the five top-performing methods (Mixture Tuned Matched Filtering, Matched Filtering, Matched Filtering/Spectral Angle Mapper Ratio, NDVI-2, and NDVI-4). This study is the first of its kind to detect and map sparse and isolated vegetated patches (with smallest area of 0.25 m2) in East Antarctica using VHR data and to use ensemble merging of feature extraction methods, and provides access to an important indicator for environmental change.
Highlights
Parts of Antarctica have experienced major changes in temperature, wind speed, and the impacts of stratospheric ozone depletion since the mid-Twentieth Century [1,2]
A geographical information system (GIS)-compatible shapefile of the vegetation class was generated using ArcGIS 10, and the surface area of vegetation for the two study regions was calculated. Assuming that this manual digitization supplemented by ground truthing was accurate, we evaluated the performance of various feature extraction methods [54]. a map showing manually digitized reference vegetation cover is presented in Figures 1 and 2
The performance of the customized Normalized Difference Vegetation Index (NDVI)-based semi-automatic extraction approach was compared with the supervised semi-automatic feature extraction approach by computing bias and root mean square error (RMSE) values of the extracted vegetation areas
Summary
Parts of Antarctica have experienced major changes in temperature, wind speed, and the impacts of stratospheric ozone depletion since the mid-Twentieth Century [1,2]. The climatic changes are predicted to continue, leading to higher summer temperatures and generally increased water availability in ice-free areas which, in turn, will encourage increased growth and extent of cryospheric vegetation and other biological groups and communities [3]. The distribution of vegetation is influenced by a range of environmental factors including soil moisture, permafrost depth, seasonal temperature, atmospheric CO2, geomorphology, and ice-free area [4,5,6], with these factors exerting influences at a range of spatial scales [7]. Regardless of Antarctica’s sensitivity to changing climate, few studies have yet addressed in detail the response of Antarctic vegetation to changing climatic conditions [8,9,10,11,12,13]. Baseline information documenting distribution of vegetation is required against which to spatio-temporally monitor changes in extent in response to changing climatic conditions. There is a pressing need for the development and application of digital mapping methods capable of generating detailed vegetation maps in this pristine, remote, and inaccessible environment
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