The availability of high spatial and spectral resolution remote sensing systems may be accompanied by changes in techniques for applying the data if appropriate data processing methodologies can be demonstrated. Landslide monitoring, which requires large areas to be surveyed at a detailed level, has previously been unsatisfactory due to its reliance on air photograph interpretation. This study demonstrates the synergistic use of medium resolution, multitemporal Satellite pour l'Observation de la Terre (SPOT) XS, and fine resolution IKONOS images for landslide inventories. The post‐classification comparison method of change detection using the Maximum Likelihood classifier with SPOT XS images was able to detect approximately 70% of landslides, the main omissions being those smaller than approximately half a pixel wide. The visual quality of images obtained from Pan‐sharpening of IKONOS images was comparable to that obtainable from 1 : 10 000 scale air photographs, enabling detailed interpretation of landslides and associated environmental features. A methodology combining the two levels of survey is proposed for regional scale landslide monitoring.

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

High-resolution thermal data derived from Landsat's thematic mapper are evaluated for their correspondence to building geometry and landscape features in Singapore's high-rise housing estates. The image data are sufficiently detailed to reveal that differences in solar azimuth on images taken at different times of year create different thermal patterns due to building geometry and surface materials. Field measurements of surface and adjacent air temperatures in urban canyons at different orientations and at different elevations above ground demonstrate that for Singapore conditions satellite-derived surface temperature patterns are a good indicator of the daytime urban heat island. Abstract High-resolution thermal data derived from Landsat's thematic mapper are evaluated for their correspondence to building geometry and landscape features in Singapore's high-rise housing estates. The image data are sufficiently detailed to reveal that differences in solar azimuth on images taken at different times of year create different thermal patterns due to building geometry and surface materials. Field measurements of surface and adjacent air temperatures in urban canyons at different orientations and at different elevations above ground demonstrate that for Singapore conditions satellite-derived surface temperature patterns are a good indicator of the daytime urban heat island.