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Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

While the protected area (PA) covers >15% of the planet's terrestrial land area and continues to expand, factors determining its effectiveness in conserving endangered species are being debated. We investigated the links between direct anthropogenic pressures, socioeconomic settings, and the coverage of vertebrate taxa by China's PA network, and indicated that high socioeconomic status and low levels of human pressure correlate with high species coverage, with threatened mammals more effectively conserved than reptiles or amphibians. Positive links between conservation outcomes and socioeconomic progress appear linked to local livelihood improvements triggering positive perceptions of local PAs—aided further by ecological compensation and tourism schemes introduced in wealthy areas and reinforced by continued positive conservation outcomes. Socioeconomic development of China's less developed regions might assist regional PA efficiency and achievement of the Kunming‐Montreal Global Biodiversity Framework, while also addressing potential shortcomings from an insufficient past focus on socioeconomic impacts for biodiversity conservation. [ABSTRACT FROM AUTHOR]

Hyperspectral/multispectral and panchromatic of optical remote sensing images are commonly used for multisensor image fusion, which has been applied in various applications of Earth observation. However, the utilization of optical remote sensing data suffers from the limitation of bad weather and cloud contamination. To address aforementioned issue and enhance spatial details of polarimetric synthetic aperture radar (PolSAR) image, a novel dual-domain super-resolution image fusion method is proposed by combining improved spherically invariant random vector (ISIRV) model with generalized adaptive linear combination approximation (GALCA) technology in this study. The proposed method decomposes the task of image fusion into polarimetric and texture domain fusion by integrating polarimetric components of PolSAR image and texture detail component of panchromatic image, which can significantly improve spatial resolutions of the PolSAR image while preserving polarimetric information. The data fusion experiment is implemented with three data sets including panchromatic images of Gaofen-1 (GF-1) and Gaofen-2 (GF-2) and the quad-pol SAR data of Gaofen-3 (GF-3) and Radarsat-2. Results show that the proposed dual-domain image fusion method provides a better performance compared with state-of-the-art multisensor fusion methods (BT, PCA, GS, indusion, and PRACS) regarding qualitative and quantitative evaluations. In addition, results of image fusion are applied to image classification over agricultural and urban areas of China, which shows that classification accuracy is significantly improved when compared with the result using only the original image. [ABSTRACT FROM AUTHOR]

Additive noise results from the thermal noise of the radar receiver or antenna, and often has an adverse effect on the subsequent applications. To weaken the influence of additive noise, it is crucial to know the actual additive noise level. However, the system noise level is not always provided in some single- or full-polarimetric synthetic aperture radar (PolSAR) products. As one of the typical low backscattering objects (LBOs) in PolSAR images, shadow occurs as soon as the depression angle of the synthetic aperture radar (SAR) system reaches a more value than the slope away from the radar illumination, and thus provides a feasible way to measure additive noise. This paper proposes a framework to automatically extract shadow regions by a trained model using the bag of words (BoW) model with data from different scenes. The additive noise can then be estimated across a large area based on the extracted shadow. The experiments based on single- and full-polarimetric Radarsat-2 data suggest that the shadow-based framework is an effective way to estimate additive noise. [ABSTRACT FROM AUTHOR]