University of Nottingham — Publications

Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions

Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r =0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r =0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+).

Mark Cutler, Doreen S. Boyd, Giles Foody et al. 2012Article

Publications

Spatial–Temporal Super-Resolution Land Cover Mapping With a Local Spatial–Temporal Dependence Model

Using control data to determine the reliability of volunteered geographic information about land cover

Unmixing Aggregate Data: Estimating the Social Composition of Enumeration Districts

Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep neural network

Scrutinizing Relationships between Submarine Groundwater Discharge and Upstream Areas Using Thermal Remote Sensing: A Case Study in the Northern Persian Gulf

Sharpened Mapping of Tropical Forest Biophysical Properties from Coarse Spatial Resolution Satellite Sensor Data

Super‐resolution mapping of the waterline from remotely sensed data

Ordinal-level classification of sub-pixel tropical forest cover

Addressing multiple facets of bias and uncertainty in continental scale biodiversity databases

Remote sensing of terrestrial chlorophyll content

One-Class Classification for Mapping a Specific Land-Cover Class: SVDD Classification of Fenland

Thematic Map Comparison

Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing

Training pattern replication and weighted class allocation in artificial neural network classification

Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes

Impacts of ignorance on the accuracy of image classification and thematic mapping

Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions

The significance of border training patterns in classification by a feedforward neural network using back propagation learning

Estimation of land coverage from a land cover classification derived from remotely sensed data

ERS-2 SAR soil moisture and herbaceous biomass monitoring across a semi-arid transect in Israel