Water extraction is one of challenging topics in studies on remote-sensing applications. Spectral profiles and experiments indicate that existing water indices often misclassified turbid water, small waterbodies, and some land features in a shadow area. In this study, a new water index called weighted normalized difference water index (WNDWI) was proposed to reduce those errors and improve the mapping accuracy of waterbodies by using Landsat imagery. To test the performance of the newly proposed water index, two test sites (Tampa Bay, FL, USA and Xiangshan Harbour, Zhejiang, China) were selected and the performances of three existing water indices including the normalized difference water index (NDWI), the modified NDWI (MNDWI), and the automated water extraction index (AWEI) were compared with that of the WNDWI. In addition, a default threshold 0 and automatically thresholding methods including Otsu threshold method and multiple thresholds identified by valley points in a histogram curve were tested to determine an optimal threshold that can be used to separate water and non-water features from grey images created by the four water indices. The experimental results indicate that the overall accuracies (OAs) created with WNDWI were all higher than those created with the three existing water indices: NDWI, MNDWI, and AWEI in both sites. Moreover, the results thresholded by 0 owned or shared the highest OAs with the results segmented by some of non-zero thresholds obtained from Otsu method and multiple thresholds method. Therefore, using an appropriate threshold, the proposed method could extract waterbodies from Landsat TM imagery with a high accuracy.

Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.

Improvement in remote sensing techniques in spatial/spectral resolution strengthens their applicability for urban environmental study. Unfortunately, high spatial resolution imagery also increases internal variability in land cover units and can cause a ‘salt-and-pepper’ effect, resulting in decreased accuracy using pixel-based classification results. Region-based classification techniques, using an image object (IO) rather than a pixel as a classification unit, appear to hold promise as a method for overcoming this problem. Using IKONOS high spatial resolution imagery, we examined whether the IO technique could significantly improve classification accuracy compared to the pixel-based method when applied to urban land cover mapping in Tampa Bay, FL, USA. We further compared the performance of an artificial neural network (ANN) and a minimum distance classifier (MDC) in urban detailed land cover classification and evaluated whether the classification accuracy was affected by the number of extracted IO features. Our analysis methods included IKONOS image data calibration, data fusion with the pansharpening (PS) process, Hue–Intensity–Saturation (HIS) transferred indices and textural feature extraction, and feature selection using a stepwise discriminant analysis (SDA). The classification results were evaluated with visually interpreted data from high-resolution (0.3 m) digital aerial photographs. Our results indicate a statistically significant difference in classification accuracy between pixel- and object-based techniques; ANN outperforms MDC as an object-based classifier; and the use of more features (27 vs. 9 features) increases the IO classification accuracy, although the increase is statistically significant for the MDC but not for the ANN.

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods.

A change detection experiment for an invasive species, saltcedar, near Lovelock, Nevada, was conducted with multidate Compact Airborne Spectrographic Imager (CASI) hyperspectral datasets. Classification and NDVI differencing change detection methods were tested. In the classification strategy, a principal component analysis (PCA) was performed on single‐date CASI imagery separately in the visible bands and NIR bands. Then the first five PCs from the visible bands and the first five PCs from the NIR bands were used to classify six to eight cover types with a maximum likelihood classifier. A complete matrix of change information and change/no‐change maps were produced by overlaying two single‐date classification maps. In the NDVI differencing strategy, a linear regression model was developed between two Normalized Difference Vegetation Index (NDVI) images to normalize the index differences caused by factors not related to land cover change. Then the actual time 2 NDVI image was subtracted by the predicted time 2 NDVI image to obtain the differencing image. The NDVI differencing image was further processed with a new threshold method into change/no‐change of saltcedar. By testing the single‐date classification results and validating the change/no‐change results, both change detection results indicated that CASI hyperspectral data have the potential to map and monitor the change of saltcedar. However, the accuracy assessment and change/no‐change validation results (overall accuracy 91.56% and kappa value 0.618 for the classification method against corresponding values of 93.04% and 0.684 for the NDVI differencing method) indicate that the NDVI differencing method outperformed the classification method in this particular study. In addition, use of the new method of determining thresholds for differentiating change pixels from no‐change pixels from the NDVI differencing image improved the change detection accuracy compared to a traditional method (kappa value increased from 0.813 to 0.888 from a test sample). Therefore, according to the criteria of higher accuracy of change/no‐change maps and fewer spectral bands, the NDVI differencing method is recommended for use if a suitable spectral normalization between multi‐temporal images can be carried out before performing image differencing.

Analyzing evolution characteristics of landscape ecological risk patterns would help establish ecological risk early warning mechanism, reduce the ecological risk probability and promote coastal landscape pattern optimization. In this study, landscape pattern indices were first calculated by using multitemporal Landsat TM images acquired in years 1990, 2000 and 2010, then landscape ecological risk pattern evolution models and the spatiotemporal evolutions of ecological risk patterns of coastal zones in Zhejiang province, China were constructed and analyzed. The results demonstrate that (1) greater changes have taken place in landscape structures during the two decades, and (2) the areas of cropland, sea, woodland and water surface have decreased significantly, while the areas of built-up land, unused land and aquaculture land have increased. In 1990, coastal landscapes were mainly occupied by extremely low and low ecological risk areas. However, by 2010, the low ecological risk area apparently decreased compared to that in 1990, while extremely high and high ecological risk areas increased significantly, occupying spatial regions where originally the extremely low and low ecological risk zones located. Transition rates of ecological risk grades in the second ten years (2000–2010) were significantly lower than in the first ten years (1990–2000). The result suggests that people who originally urged to accelerate coastal landscape exploitation and development have started to concern the ecological environment protection and to pursue a mode that economic development and ecological protection are synchronously conducted.