Mangrove wetlands are evergreen tree and shrub communities mainly consisting of mangrove plants, usually growing on the upper part of tidal flat of tropical and subtropical coasts, and periodically inundated by tidal water. Mangroves are naturally distributed in Hainan, Guangxi, Guangdong, Taiwan and Fujian provinces, with an area of about 15,000 ha.There are 26 exclusive mangrove species and 11 semi exclusive mangrove species in China. The mangrove wetland in China has low direct economic values, but plays an important role in protecting coastline, keeping coastal biodiversity and fishery resources, clarifying sea water, beautifying environment, and so on. It is a coastal ecologically critical area that is specially easy to be undervalued.The mangrove areas have been sharply reduced because of humans unreasonable exploiting activities,such as des troying mangroves for agricultural and salt fields, for aquaculture ponds and for urban construction, and the mangrove wetland resources are in danger and need urgent efficient management and protection. Eighteen mangrove natural reserves have been established. It is essential to attract scientists to take an active part in scientific researches on mangrove wetlands, to spread the knowledge about mangrove environmental functions, to reverse its decline trend and to achieve the ecological restoration and sustainable deve lopment of mangrove wetland ecosystem.

Mangroves are salt tolerant woody plants that form highly productive intertidal ecosystems in tropical and subtropical regions. Despite the established importance of mangroves to the coastal environment, including fisheries, deforestation continues to be a major threat due to pressures for wood and forest products, land conversion to aquaculture, and coastal urban development. Over the past 15 years, remote sensing has played a crucial role in mapping and understanding changes in the areal extent and spatial pattern of mangrove forests related to natural disasters and anthropogenic forces. This paper reviews recent advancements in remote-sensed data and techniques and describes future opportunities for integration or fusion of these data and techniques for large-scale monitoring in mangroves as a consequence of anthropogenic and climatic forces. While traditional pixel-based classification of Landsat, SPOT, and ASTER imagery has been widely applied for mapping mangrove forest, more recent types of imagery such as very high resolution (VHR), Polarmetric Synthetic Aperture Radar (PolSAR), hyperspectral, and LiDAR systems and the development of techniques such as Object Based Image Analysis (OBIA), spatial image analysis (e.g. image texture), Synthetic Aperture Radar Interferometry (InSAR), and machine-learning algorithms have demonstrated the potential for reliable and detailed characterization of mangrove forests including species, leaf area, canopy height, and stand biomass. Future opportunities include the application of existing sensors such as the hyperspectral HYPERION, the application of existing methods from terrestrial forest remote sensing, investigation of new sensors such as ALOS PRISM and PALSAR, and overcoming challenges to the global monitoring of mangrove forests such as wide-scale data availability, robust and consistent methods, and capacity-building with scientists and organizations in developing countries.

Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827–2807 km2 for Senegal and 245–1271 km2 for The Gambia with one outlier for each country. We further report “Places of Agreement” (PoA) to identify areas where all iterations for both methods agree (506.6 km2 and 129.6 km2 for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making.

Mangrove forests are tropical trees and shrubs that grow in sheltered intertidal zones. Accurate mapping of mangrove forests is a great challenge for remote sensing because mangroves are periodically submerged by tidal floods. Traditionally, multi-tides images were needed to remove the influence of water; however, such images are often unavailable due to rainy climates and uncertain local tidal conditions. Therefore, extracting mangrove forests from a single-tide imagery is of great importance. In this study, reflectance of red-edge bands in Sentinel-2 imagery were utilized to establish a new vegetation index that is sensitive to submerged mangrove forests. Specifically, red and short-wave near infrared bands were used to build a linear baseline; the average reflectance value of four red-edge bands above the baseline is defined as the Mangrove Forest Index (MFI). To evaluate MFI, capabilities of detecting mangrove forests were quantitatively assessed between MFI and four widely used vegetation indices (VIs). Additionally, the practical roles of MFI were validated by applying it to three mangrove forest sites globally. Results showed that: (1) theoretically, Jensen–Shannon divergence demonstrated that a submerged mangrove forest and water pixels have the largest distance in MFI compared to other VIs. In addition, the boxplot showed that all submerged mangrove forests could be separated from the water background in the MFI image. Furthermore, in the MFI image, to separate mangrove forests and water, the threshold is a constant that is equal to zero. (2) Practically, after applying the MFI to three global sites, 99–102% of submerged mangrove forests were successfully extracted by MFI. Although there are still some uncertainties and limitations, the MFI offers great benefits in accurately mapping mangrove forests as well as other coastal and aquatic vegetation worldwide.

为探究高分六号（GF6）宽幅遥感影像红边波段在春季作物识别中的应用，以河南省杞县为研究区，通过分析2019年3月25日单时相影像及其光谱特征，利用随机森林算法完成4种不同红边波段方案下冬小麦、大蒜和其他作物（油菜、蔬菜等）的分类提取，并基于地面采样数据实现不同方案分类精度评价、样本间可分性测度以及光谱反射率计算分析。结果表明，有红边波段参与下，较无红边波段参与时作物总体分类精度和不同作物可分性测度值均有所提高；单红边波段参与下，红边波段2作物总体分类精度较红边波段1提高了1.98个百分点；引入全部红边波段较无红边参与方案的作物总体分类精度由81.56%提高到86.19%，提高了4.63个百分点，Kappa系数由0.72提高到0.79，冬小麦-大蒜、冬小麦-其他作物、大蒜-其他作物的J-M（Jeffries-Matusita）可分性测度也分别增加了0.085 6、0.076 1和0.025 1。研究表明，红边波段的引入不仅增加了作物间的可分性测度，降低了分类结果中作物误分、漏分情况，也在一定程度上降低了结果中的“椒盐现象”，为国产红边卫星数据在农业上的应用提供参考。

高分六号卫星具有覆盖广、多种分辨率、波段多的优势,能为遥感解译提供更丰富的信息。为探究高分六号卫星新增波段在森林树种识别上的应用,本文以覆盖根河市阿龙山林业局的一期高分六号宽幅影像为数据源,基于特征优化空间算法（Feature Space Optimization,FSO）和最大似然分类法,分别利用高分六号的前4个波段和所有波段（8波段）的光谱、纹理等特征进行了森林树种分类,并逐一添加新增波段特征确定了各波段的贡献率排名。结果表明：在加入了优选出的均匀性纹理、均值纹理和角二阶矩纹理3种纹理特征后,前4波段和8波段的分类精度比只基于光谱特征时的精度分别高出13.23%和24.63%;利用8波段信息比只利用前4波段在基于光谱特征上的精度高11.88%,在基于光谱+纹理特征上则高23.24%;基于8波段光谱+纹理特征的树种分类精度最高,达到68.74%,新增4波段的贡献率排名为B6>B5>B8>B7,说明新增红边波段对于本次树种分类试验的贡献率最高,能为北方树种识别提供有效帮助。

Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery.

Through the analysis of hyperspectral imaging data collected over water surfaces covered by floating vegetation, such as Sargassum and algae, we observed that the spectra commonly contain a reflectance peak centered near 1.07 μm. This peak results from the competing effects between the well-known vegetation reflectance plateau in the 0.81–1.3 μm spectral range and the absorption effects above 0.75 μm by liquid water within the vegetation and in the surrounding water bodies. In this article, we propose a new index, namely the floating vegetation index (FVI), for the hyperspectral remote sensing of vegetation over surface layers of oceans and inland lakes. In the formulation of the FVI, one channel centered near 1.0 μm and another 1.24 μm are used to form a linear baseline. The reflectance value of the third channel centered at the 1.07-μm reflectance peak above the baseline is defined as the FVI. Hyperspectral imaging data acquired with the AVIRIS (Airborne Visible Infrared Imaging Spectrometer) instrument over the Gulf of Mexico and over salt ponds near Moffett Field in southern portions of the San Francisco Bay were used to demonstrate the success in detecting Sargassum and floating algae with this index. It is expected that the use of this index for the global detection of floating vegetation from hyperspectral imaging data to be acquired with future satellite sensors will result in improved detection and therefore enhanced capability in estimating primary production, a measure of how much carbon is fixed per unit area per day by oceans and inland lakes.