The challenges of using satellite data sets to assess historical land use change and associated greenhouse gas emissions: a case study of three Indonesian provinces

Advances in satellite remote sensing and the wealth of earth observation (EO) data now available have improved efforts toward determining and quantifying historical land use and land cover (LULC) change. Satellite imagery can overcome the absence of accurate records of historical land use; however, the variability observed in the case study regions demonstrates a number of current challenges. Differences in spatial coverage, resolution and land cover classification can lead to challenges in analyzing historical data sets to estimate LULC change and associated GHG emissions. This paper demonstrates the calculation of LULC change from three existing, open-source data sets to show how this can lead to significant variation in estimates of GHG emissions related to differences in land classification methodologies, EO input data and period of investigation. This article focuses on selected regions of Indonesia, where quantifying land use change is important for GHG assessments of agricultural commodities and for evidencing progress against corporate and government deforestation commitments. Given the significance of GHG emissions arising from LULC change and the increasing need for emissions monitoring, this research highlights a need for consensus building to develop consistency in historical and future LULC change estimates. This paper concludes with a set of recommendations for improvements to ensure consistent LULC mapping

Airborne S-Band SAR for forest biophysical retrieval in temperate mixed forests of the UK

Radar backscatter from forest canopies is related to forest cover, canopy structure and aboveground biomass (AGB). The S-band frequency (3.1–3.3 GHz) lies between the longer L-band (1–2 GHz) and the shorter C-band (5–6 GHz) and has been insufficiently studied for forest applications due to limited data availability. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest biophysical properties. To understand the scattering mechanisms in forest canopies at S-band the Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model was used. S-band backscatter was found to have high sensitivity to the forest canopy characteristics across all polarisations and incidence angles. This sensitivity originates from ground/trunk interaction as the dominant scattering mechanism related to broadleaved species for co-polarised mode and specific incidence angles. The study was carried out in the temperate mixed forest at Savernake Forest and Wytham Woods in southern England, where airborne S-band SAR imagery and field data are available from the recent AirSAR campaign. Field data from the test sites revealed wide ranges of forest parameters, including average canopy height (6–23 m), diameter at breast-height (7–42 cm), basal area (0.2–56 m2/ha), stem density (20–350 trees/ha) and woody biomass density (31–520 t/ha). S-band backscatter-biomass relationships suggest increasing backscatter sensitivity to forest AGB with least error between 90.63 and 99.39 t/ha and coefficient of determination (r2) between 0.42 and 0.47 for the co-polarised channel at 0.25 ha resolution. The conclusion is that S-band SAR data such as from NovaSAR-S is suitable for monitoring forest aboveground biomass less than 100 t/ha at 25 m resolution in low to medium incidence angle rang

Remote sensing-based mapping and modelling of salt marsh habitats based on optical, lidar and sar data

There is much interest in the ability of Remote Sensing (RS) technologies for mapping natural environments. Meanwhile, coastal zones need monitoring in order to find a balance between human use and sustainable functioning of coastal zone ecosystems. This research explores methods for characterising coastal salt marsh zone habitats using multi-source RS data, focussing on under-exploited Synthetic Aperture Radar (SAR) remote sensing data, thereby providing additional information in support of the mapping of natural habitats in coastal zones. This research examined the use of quad-polarimetric airborne S-band and X-band SAR data, in conjunction with optical and LiDAR RS data variables, for assessment of environmental parameters, mapping and modelling of salt marsh habitats in a research area set in the Llanrhidian salt marshes in Wales. In the first analysis it was researched how SAR descriptors (backscatter intensity and polarimetric decomposition variables) were affected by salt marsh environmental and botanical factors. It was found that SAR backscatter from the most seaward pioneer zone of the salt marsh was most affected by soil moisture variations. Differences in botanical structure caused variations in SAR backscatter mechanisms active in different habitats. In the second analysis habitat mapping was carried out with optical, LiDAR and SAR variables, with the supervised classifiers Support Vector Machine (SVM) and Random Forest (RF). With these classifiers accurate salt marsh habitat maps were produced, the most accurate classification achieved was 78.20% with RF based on all available RS variables. The last research experiment involved multivariate regression analysis of correlations between RS variables and biophysical parameters vegetation cover, height and volume and showed that multivariate SVM regression was the most accurate technique for all three biophysical parameters. This research indicated that SAR is complementary to optical and LiDAR data for ecological mapping and therefore recommended to be included in similar ecological studies