Summary Quantitative measurements of above‐ground vegetation biomass are vital to a range of ecological and natural resource management applications. Remote‐sensing techniques, such as terrestrial laser scanning (TLS) and image‐based point clouds, are potentially revolutionary techniques for measuring vegetation biomass and deriving other related, structural metrics for these purposes. Surface vegetation biomass (up to 25 cm) in pasture, forest, and woodland environments is estimated from a 3D point cloud derived from a small number of digital images. Volume is calculated, using the 3D cloud and regressed against dry weight to provide an estimate of biomass. Assessment of the method is made through comparison to 3D point clouds collected through TLS surveys. High correlation between destructively sampled biomass and vegetation volume derived from TLS and image‐based point clouds in the pasture (TLS r2=0·75, image based r2=0·78), dry grassy forest (TLS r2=0·73, image based r2=0·87) and lowland forest (TLS r2=0·74, image based r2=0·63) environments was found. Occlusion caused by standing vegetation in the woodland environment resulted in moderate correlation between TLS derived volume and biomass (r2=0·50). The effects of surrounding vegetation on the image‐based technique resulted in 3D point clouds being resolved for only 40% of the samples in this environment. The results of this study demonstrate that image‐based point cloud techniques are highly viable for the measurement of surface biomass. In contrast to TLS, volume and biomass data can be captured using low‐cost equipment and relatively little expertise.

Satellite remote sensing is regularly used for wildfire detection, fire severity mapping and burnt area mapping. Applications in the surveillance of wildfire using geostationary-based sensors have been limited by low spatial resolutions. With the launch in 2015 of the AHI (Advanced Himawari Imaginer) sensor on board Himawari-8, ten-minute interval imagery is available covering an entire earth hemisphere across East Asia and Australasia. Existing active fire detection algorithms depend on middle infrared (MIR) and thermal infrared (TIR) channels to detect fire. Even though sub-pixel fire detection algorithms can detect much smaller fires, the location of the fire within the AHI 2 × 2 km (400 ha) MIR/TIR pixel is unknown. This limits the application of AHI as a wildfire surveillance and tracking sensor. A new multi-spatial resolution approach is presented in this paper that utilizes the available medium resolution channels in AHI. The proposed algorithm is able to map firelines at a 500 m resolution. This is achieved using near infrared (NIR) (1 km) and RED (500 m) data to detect burnt area and smoke within the flagged MIR (2 km) pixel. Initial results based on three case studies carried out in Western Australia shows that the algorithm was able to continuously track fires during the day at 500 m resolution. The results also demonstrate the utility for wildfire management activities.

Abstract:Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.