Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland

Abstract

Abstract:Pasture biomass is an important quantity globally in livestock industries, carbon balances, and bushfire management. Quantitative estimates of pasture biomass or total standing dry matter (TSDM) at the field scale are much desired by land managers for land-resource management, forage budgeting, and conservation purposes. Estimates from optical satellite imagery alone tend to saturate in the cover-to-mass relationship and fail to differentiate standing dry matter from litter. X-band radar imagery was added to complement optical imagery with a structural component to improve TSDM estimates in rangelands. High quality paddock-scale field data from a northeastern Australian cattle grazing trial were used to establish a statistical TSDM model by integrating optical satellite image data from the Landsat sensor with observations from the TerraSAR-X (TSX) radar satellite. Data from the dry season of 2014 and the wet season of 2015 resulted in models with adjusted r 2 of 0.81 in the dry season and 0.74 in the wet season. The respective models had a mean standard error of 332 kg/ha and 240 kg/ha. The wet and dry season conditions were different, largely due to changed overstorey vegetation conditions, but not greatly in a pasture 'growth' sense. A more robust combined-season model was established with an adjusted r 2 of 0.76 and a mean standard error of 358 kg/ha. A clear improvement in the model performance could be demonstrated when integrating HH polarised TSX imagery with optical satellite image products.

Evaluation of Normalized Difference Water Index as a Tool for Monitoring Pasture Seasonal and Inter-Annual Variability in a Mediterranean Agro-Silvo-Pastoral System

“…Figure 12 shows the relationship between NDWI and biomass. The resulting curve has a tendency for over-saturation, and consequently a decline in the accuracy/sensitivity of this index at high values of biomass (above 20000 kg•ha −1 ), an aspect documented in various works related to NDVI ( [10,22,[27][28][29][30]. These results show that NDWI based on remote sensing can be an interesting tool to estimate pasture biomass (R 2 = 0.7556; p < 0.01).…”

Monitoring Seasonal Pasture Quality Degradation in the Mediterranean Montado Ecosystem: Proximal versus Remote Sensing

“…In this grassland ecosystem, NDVI saturated at biomass values of approximately 15,000 kg•ha −1 (Figure 10). Schmidt et al [4], Lumbierres et al [6], Trotter et al [36], Schaefer and Lamb [37], and Serrano et al [38] also referenced the saturation of NDVI for pasture biomass. To Several studies, based on advances in RS technology, have used higher-resolution images to estimate grassland productivity and biomass [33,34].…”

“…In this grassland ecosystem, NDVI saturated at biomass values of approximately 15,000 kg•ha −1 (Figure 10). Schmidt et al [4], Lumbierres et al [6], Trotter et al [36], Schaefer and Lamb [37], and Serrano et al [38] also referenced the saturation of NDVI for pasture biomass. To overcome this problem, Schaefer and Lamb [37] suggested the use of an index that associates spectral reflectance measurements (NDVI) with a vegetation property such as plant height, thus mitigating the saturation issue and increasing the biomass range that can be estimated from NDVI.…”

“…rotundifolia Lam.). These low maintenance pastures fulfill an important function in rangeland ecology and are fundamental contributors to the productivity and biodiversity of these ecosystems, while sustaining livestock in soils that otherwise would have been considered unproductive [3,4]. Given the fragility of these soils, continued efforts are needed to improve their quality and thus achieve sustained pasture production.…”

Remote Sensing of Above-Ground Biomass

“…The integrated SAR and NDVI improved estimation of biomass, while only a slight saturation was experienced at higher forest biomass levels. Pasture biomass was also modelled by integrating optical and X-Band radar data in an open savannah woodland [62]. The objective was to minimise the saturation problem experienced when using optical data as well as difficulties in separating dry matter from litter.…”