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Accurately mapping and monitoring the urban impervious surface area (ISA) is crucial for understanding the impact of urbanization on heat islands and sustainable development. However, less is known about ISA spectra heterogeneity and their similarity to bare land, wetland, and high-rise-building shadows. This study proposes a feature-based approach using decision tree classification (FDTC) to map ISAs and their spatio-temporal changes in a coastal city in southeast China using Landsat 5 TM, Landsat 8 OLI/TIRS, and Sentinel-2 images from 2009 to 2021. Atmospheric correction using simplified dark object subtraction (DOS) was applied to Landsat imagery, which enabled faster computation. FDTC's performance was evaluated with three sensors with different spectral and spatial resolutions, with parameter thresholds held constant across remote-sensing images. FDTC produces a high average overall accuracy (OA) of 94.53%, a kappa coefficient (KC) of 0.855, and a map-level image classification efficacy (MICE) of 0.851 for ISA mapping over the studied period. In comparison with other indices such as BCI (biophysical composition index), PISI (automated built-up extraction index), and ABEI (perpendicular impervious surface index), the FDTC demonstrated higher accuracy and separability for extracting ISA and bare land as well as wetland and high-rise buildings. The results of FDTC were also consistent with those of two open-source ISA products and other remote sensing indices. The study found that the ISA in Xiamen City increased from 16.33% to 26.17% over the past 13 years due to vegetation occupation, encroachment onto bare land, and reclamation of coastal areas. While the expansion significantly reduced urban vegetation in rapidly urbanizing areas of Xiamen, ambitious park greening programs and massive redevelopment of urban villages resulted in a modest but continuous increase in urban green space. [ABSTRACT FROM AUTHOR]

Context: Image classification is routine in a variety of disciplines, and analysts rely on accuracy metrics to evaluate the resulting maps. The most frequently used accuracy metric in Earth resource remote sensing is overall accuracy. However, the inherent properties of this accuracy metric make it inappropriate as the single metric for map assessment, particularly when a map contains imbalanced categories. Objectives: We discuss four noteworthy problems with overall accuracy. Under circumstances frequently encountered, overall accuracy is misleading or misinterpreted. Methods: Literature review, hypothetical examples, and mathematic equations are used to prove overall accuracy is a poor general indicator of map quality. Conclusions: Any research that involves classification techniques or a map product that is evaluated only with overall accuracy may be unreliable. It is necessary for map providers to publish the error matrix and its development procedure so that map users can computer whatever metrics as they wish. [ABSTRACT FROM AUTHOR]

The global phenomenon of urbanization increases the importance of compact-city development. China’s rapid urban development has resulted in unprecedented urban population growth and built-up area expansion, but its effects on urban morphology and mobility are only partly understood. City compactness can be measured simply using urban spatial form or morphology: the more concentrated the built-up area, the more compact the city is. Here we show that 35 major cities in China are not compact in spatial form and that their compactness is not improving over time. Our results reveal close correlations between changes in urbanization rate and changes in city compactness as well as between city compactness and commuting time (CT), indicating that the high rate of urbanization without adequate planning has contributed to the poor compactness of Chinese cities, which has further increased CT. We suggest that continuing urban sprawl with low land use efficiency and low urban form compactness will make cities in China more congested and threaten China’s sustainable urbanization. [ABSTRACT FROM AUTHOR]

Abstract: Forest degradation in protected areas has been monitored around the world with remote sensing data, but degradation processes undetectable by widely used satellite sensors have been largely overlooked. Increased human pressures and socioeconomic development make forest protection more challenging, particularly for forest ecosystems that lie across national borders because of the differences in national socioeconomic policies and conditions within them. Here with Landsat data, Google Earth images, and field observations, we show that, in two adjacent biosphere reserves across the border of China and North Korea, over one half of primary forest landscapes have been deteriorated by exploitive uses, including seed harvesting and systematic logging. The combined effects of detectable and hidden degradation processes have further damaged forest ecosystems in the core areas in the two biosphere reserves, threatening sustainable biodiversity conservation in the region. It is urgent to develop cross-border collaborative conservation strategies that can help combat both detectable and hidden degradation processes at a regional scale. [Copyright &y& Elsevier]

The timely and accurate recognition of damage to buildings after destructive disasters is one of the most important post-event responses. Due to the complex and dangerous situations in affected areas, field surveys of post-disaster conditions are not always feasible. The use of satellite imagery for disaster assessment can overcome this problem. However, the textural and contextual features of post-event satellite images vary with disaster types, which makes it difficult to use models that have been developed for a specific disaster type to detect damaged buildings following other types of disasters. Therefore, it is hard to use a single model to effectively and automatically recognize post-disaster building damage for a broad range of disaster types. Therefore, in this paper, we introduce a building damage detection network (BDD-Net) composed of a novel end-to-end remote sensing pixel-classification deep convolutional neural network. BDD-Net was developed to automatically classify every pixel of a post-disaster image into one of non-damaged building, damaged building, or background classes. Pre- and post-disaster images were provided as input for the network to increase semantic information, and a hybrid loss function that combines dice loss and focal loss was used to optimize the network. Publicly available data were utilized to train and test the model, which makes the presented method readily repeatable and comparable. The protocol was tested on images for five disaster types, namely flood, earthquake, volcanic eruption, hurricane, and wildfire. The results show that the proposed method is consistently effective for recognizing buildings damaged by different disasters and in different areas. [ABSTRACT FROM AUTHOR]

The effect of the expansion of urban impervious surfaces on surface urban heat islands (UHIs) has attracted research attention due to its relevance for studies of local climatic change and habitat comfort. In this study, using five satellite images of Xiamen city, Southeast China (four images from the Landsat 5 Thematic Mapper (TM) and one from the Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS)) acquired in summer between 1989 and 2016, together with spatial statistical methods, the changes in impervious surface area (ISA) were investigated, the spatiotemporal variation of the intensity of urban heat islands (UHIs) was explored, and the relationships between land surface temperature (LST) and the percentage of impervious surface area (ISA%), the normalized difference vegetation index (NDVI), and fractional vegetation coverage (Fv) were investigated. The results showed the following: (1) According to the biophysical composition index (BCI) combined with an ISA post-processing method, Xiamen has witnessed a substantial increase in ISA, showing a 6.1-fold increase from 1989 to 2016. The direction of ISA expansion was consistent throughout the study period in each of the five districts of Xiamen; (2) a bay-like UHI form is observed in the study area, which is remarkably distinct from the central-radial UHI form observed in previous studies of other cities; (3) the extent of UHIs in Xiamen greatly increased between 1989 and 2016, experiencing a 4.7-fold increase in UHI areas during this time. However, during the same period, the urban heat island ratio index (URI)—that is, the ratio of UHI area to ISA—decreased slightly. The UHI area decreased in some urban parts of Xiamen due to a significant increase in vegetation coverage, urban village redevelopment, and the construction of new parks; (4) sea ports and heavy industrial zones are the greatest contributor to surface UHI, followed by urban villages; and (5) LST is strongly positively correlated with ISA%. Each 10% increase in ISA was associated with an increase in summer LST of 0.41 to 0.91 K, which compares well with the results of related studies. This study presents valuable information for the development of regional urban planning strategies to mitigate the effects of UHIs during rapid urbanization. [ABSTRACT FROM AUTHOR]

Rapid assessment of natural disasters is essential for disaster analysis and spatially explicit strategic decisions of post-disaster reconstruction but requires timely available data. The recent daily data of the National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provide new opportunities to detect and evaluate natural disasters. Here, we introduce an application of NPP-VIIRS DNB daily data for rapidly assessing the damage of a severe typhoon that struck the urban agglomerations along the western Taiwan Straits in China. Our research explored the methods of rapid identification and extraction of the areas based on changes in nighttime light (NTL) after the typhoon disaster by using a statistical radiation-normalization method. We analyzed the correlations of NTL image derivatives with human population, population density, and gross domestic product (GDP). The strong correlations were found between NTL image light density and population density (R2 = 0.83) and between the total nighttime light intensity and GDP (R2 = 0.96) at the prefecture level. In addition, we examined the interrelationships between changes in NTL images and the areas affected by the typhoon and proposed a method to predict the affected population. Finally, the affected area and the affected population in the study area could be rapidly retrieved based on the proposed remote sensing method. The overall accuracy was 83.2% for the detection of the affected population after disaster and the recovery rate of the affected area was 86.9% in the third week after the typhoon. This research demonstrates that the NTL image-based change detection method is simple and effective, and further explains that the NPP-VIIRS DNB daily data are useful for rapidly assessing affected areas and affected populations after typhoon disasters, and for timely quantifying the degree of recovery at a large spatial scale. [ABSTRACT FROM AUTHOR]