Search

Remotely sensing the spatial extent of artificial surfaces is essential for understanding human footprints and evaluating anthropogenic impacts on the global environment and climate. However, since the spectral signatures of artificial surfaces are similar to other bright natural surfaces, it is still a difficult task to separate artificial surfaces from other natural surfaces. Therefore, this study proposed a new Artificial Surface Index (ASI) to extract artificial surfaces based on the multispectral Landsat 8 imagery. We examined the performance of ASI in eight study areas with a variety of landscapes, including desert, coastal, inland urban and rural areas dominated by different climates and land cover categories. We also compared ASI with seven state-of-the-art built-up area or impervious surface indices that are developed for similar purposes. The qualitative and quantitative evaluations demonstrate that ASI has advantages in suppressing non-artificial surfaces with similar spectral signatures to artificial surfaces, such as deserts, bare soil, and harvested croplands. Furthermore, ASI improves the separability between artificial and non-artificial surfaces by 2% to 75% (measured by Jeffries–Matusita Distance), 13% to 164% (measured by Transformed Divergence), and 3% to 131% (measured by Spectral Discrimination Index) compared to the other seven indices across the eight study areas. The newly proposed ASI index has the potential to support a variety of human-activity-related studies, such as change detection of artificial surfaces, urban planning, land resource management at local, continental, and even global scales.

Toxic Cyanobacteria-rich Harmful Algal Blooms (CyanoHABs) are severe environmental issues impacting water environment, aquatic life populations, and surrounding wild and human lives. Given their fast-changing variations in space and time, frequent monitoring of CyanoHABs with sufficient spatial details and coverage is needed to understand their impacts. However, current monitoring activities based on in situ or satellite data do not meet this requirement due to the limited spatial coverages of buoy measurements and the compromise between spatial resolution and temporal frequency of satellite observations. In this study, we develop a Spatial-Temporal Image Fusion (STIF) approach to enable high spatial-temporal resolution monitoring of CyanoHABs. The proposed approach consists of two steps: (1) a new CyanoHAB spectral index called Broad Wavelength Algae Index (BWAI) is developed for fine-but-sparse (Landsat) and coarse-but-frequent (MODIS) satellite images, and (2) the Landsat and MODIS derived BWAI images are fused by the Robust Adaptive Spatial and Temporal Fusion Model (RASTFM) to generate fine-and-frequent Landsat-like BWAI images. Our results show that the proposed BWAI index is with higher similarity and correlation with the reference Cyanobacteria Index (CI) images and in situ observations than the comparative algae indices devised for broad wavelength sensors. Moreover, the 30-m Landsat-like BWAI image series provide more accurate and detailed results than their 500-m MODIS counterparts and greatly improve the temporal frequency over Landsat-based algae indices, demonstrating the contribution of the improved spatial-temporal resolution achieved by the proposed STIF approach. Consequently, this research fills the gap of high spatiotemporal resolution monitoring of CyanoHABs and paves a new way of assessing water environment in a timely and detailed manner.