Optimisation of Savannah Land Cover Characterisation with Optical and SAR Data

Abstract

Accurately mapping savannah land cover at the regional scale can provide useful input to policy decision making efforts regarding, for example, bush control or overgrazing, as well as to global carbon emissions models. Recent attempts have employed Earth observation data, either from optical or radar sensors, and most commonly from the dry season when the spectral difference between woody vegetation, crops and grasses is maximised. By far the most common practice has been the use of Landsat optical bands, but some studies have also used vegetation indices or SAR data. However, conflicting reports with regards to the effectiveness of the different approaches have emerged, leaving the respective land cover mapping community with unclear methodological pathways to follow. We address this issue by employing Landsat and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data to assess the accuracy of mapping the main savannah land cover types of woody vegetation, grassland, cropland and non-vegetated land. The study area is in southern Africa, covering approximately 44,000 km 2 . We test the performance of 15 different models comprised of combinations of optical and radar data from the dry and wet seasons. Our results show that a number of models perform well and very similarly. The highest overall accuracy is achieved by the model that incorporates both optical and synthetic-aperture radar (SAR) data from both dry and wet seasons with an overall accuracy of 91.1% (±1.7%): this is almost a 10% improvement from using only the dry season Landsat data (81.7 ± 2.3%). The SAR-only models were capable of mapping woody cover effectively, achieving similar or lower omission and commission errors than the optical models, but other classes were detected with lower accuracies. Our main conclusion is that the combination of metrics from different sensors and seasons improves results and should be the preferred methodological pathway for accurate savannah land cover mapping, especially now with the availability of Sentinel-1 and Sentinel-2 data. Our findings can provide much needed assistance to land cover monitoring efforts to savannahs in general, and in particular to southern African savannahs, where a number of land cover change processes have been related with the observed land degradation in the region.

Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics

“…The L-band is particularly suitable for observations in tropical areas characterized by a dense vegetation cover and the frequent presence of dense clouds, which can lead to strong attenuation even in C-band radar data [45]. Various studies have analyzed the potential of airborne or spaceborne L-band radar for the observation of agricultural surfaces, as well as for the estimation of land cover and vegetation properties [46][47][48][49][50]. Several studies have proposed the use of polarimetric airborne SAR measurements to analyze soil moisture [51][52][53][54][55][56][57][58][59][60][61][62][63], and have demonstrated the potential of L-band data for the high accuracy retrieval of soil moisture.…”

A Novel Approach to Unsupervised Change Detection Based on Hybrid Spectral Difference

“…However, some change types are difficult to detect by only the spectral features of individual pixels. To solve this problem, texture features like grey-level co-occurrence matrix (GLCM) [46] and histogram of oriented gradients (HOG) [47], which use context information, can be used in our future investigations.…”

“…Another limitation of HSD is that it could not help identify changes if they were missed by both methods no matter how to calculate the weights. In this situation, spectral characteristics were not enough to find changes, and more features like texture, e.g., grey-level co-occurrence matrix (GLCM) [46] and histogram of oriented gradients (HOG) [47], should be used.…”

Combining optical and radar satellite image time series to map natural vegetation: savannas as an example

“…; Symeonakis et al. ). To our knowledge, the only work that classified savanna following a vegetation height gradient was the contribution of Schwieder et al.…”

“…; Symeonakis et al. ), we expect classifications based on data combination to perform better than classifications based on optical or radar data alone, because data combination takes advantage of the sensitivity of both sensors (H1). Based on previous studies (Archibald and Scholes ; Mathieu et al.…”