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The Middle Route of the South-to-North Water Diversion Project (MR-SNWDP) in China, with construction beginning in 2003, diverts water from Danjiangkou Reservoir to North China for residential, agriculture and industrial use. The water source area of the MR-SNWDP is the region that is most sensitive to and most affected by the construction of this water diversion project. In this study, we used Landsat Thematic Mapper (TM) and HJ-1A/B images from 2000 to 2015 by an object-based approach with a hierarchical classification method for mapping land cover in the water source area. The changes in land cover were illuminated by transfer matrixes, single dynamic degree, slope zones and fractional vegetation cover (FVC). The results indicated that the area of cropland decreased by 31% and was replaced mainly by shrub over the past 15 years, whereas forest and settlements showed continuous increases of 29.2% and 77.7%, respectively. The changes in cropland were obvious in all slope zones and decreased most remarkably (−43.8%) in the slope zone above 25°. Compared to the FVC of forest and shrub, significant improvement was exhibited in the FVC of grassland, with a growth rate of 16.6%. We concluded that local policies, including economic development, water conservation and immigration resulting from the construction of the MR-SNWDP, were the main drivers of land cover changes; notably, they stimulated the substantial and rapid expansion of settlements, doubled the wetlands and drove the transformation from cropland to settlements in immigration areas. [ABSTRACT FROM AUTHOR]

Remote sensing can be the most effective means of scaling up grassland aboveground biomass (AGB) from the sample scale to the regional scale. Among the remote-sensing approaches, statistical models based on the vegetation index (VI) are frequently used to retrieve grassland AGB because of their simplicity and reliability. However, these types of models have never been comprehensively optimized to overcome VI insensitivity and soil effects. Because grassland AGB is related to grassland type, in our research the integrated orderly classification system for grassland (IOCSG) was used to differentiate grassland types. The study area, located in Inner Mongolia, China, included desert steppe, typical steppe and meadow steppe. A pure VI (PVI) was extracted from the normal VI using spectral mixture analysis (SMA). Using a proportional relationship, PVI models were then constructed based on grassland type. The results demonstrated that the PVI models can have clear advantages over the more commonly used VI models. They simplify the parameterization of VI models and thus enhance models constructed for different regions with different remote sensing data sources. Notably, detailed differentiation of grassland types can improve the accuracy of AGB estimates. The methodology proposed in this study is particularly beneficial for AGB estimates at a national scale, especially for countries such as China with many grassland types. [ABSTRACT FROM AUTHOR]

Terrestrial vegetation is one of the most important components of the Earth's land surface. Variations in terrestrial vegetation directly impact the Earth system's balance of material and energy. This paper describes detected variations in vegetation activity at a national scale for China based on nearly 30 years of remote sensing data derived from NOAA/AVHRR (1982-2006) and MODIS (2001-2009). Vegetation activity is analyzed for four regions covering agriculture, forests, grasslands, and China's Northwest region with sparse vegetation cover (including regions without vegetation). Relationships between variations in vegetation activity and climate change as well as agricultural production are also explored. The results show that vegetation activity has generally increased across large areas, especially during the most recent decade. The variations in vegetation activity have been driven primarily by human factors, especially in the southern forest region and the Northwest region with sparse vegetation cover. The results further show that the variations in vegetation activity have influenced agricultural production, but with a certain time lag. [ABSTRACT FROM AUTHOR]

There has been a great deal of interest in studying the crown of trees using remote sensing data. In this study, crown width was extracted from high-resolution QuickBird images for open Populus xiaohei plantations. Regression models for predicting the individual stem volumes of Populus xiaohei were established using extracted crown width, as well as estimated tree parameters (i.e. diameter at breast height [DBH] and tree height) as predictors. Our results indicated that crown width could be accurately extracted from QuickBird images using a multi-scale segmentation approach with a mean relative error of 5.74%, especially for wide-spacing stands. Using either extracted crown width alone or with estimated DBH and tree height can successfully estimate individual stem volume of Populus xiaohei with the R2 value ranging from 0.87 to 0.92 depending on different model forms. In particular, the two second-order polynomial models (model 2 and model 6), based on QuickBird image-derived crown widths and estimated DBH and tree heights, respectively, were the best at describing the relationship between stem volume and tree characteristics. [ABSTRACT FROM AUTHOR]

Functional diversity (FD) provides a link between biodiversity and ecosystem functioning, summarizing inter- and intra-specific variation of functional traits. However, quantifying plant traits and FD consistently and cost-effectively across large and heterogeneous forest areas is challenging with traditional field sampling. Airborne light detection and ranging (LiDAR) and imaging spectroscopy provide spatially explicit data, which allow mapping of selected forest traits and FD at different spatial scales. We develop an individual tree-based method to measure forest FD from tree neighborhoods to whole forests, and demonstrate the approach by mapping functional traits of over one million trees in a subtropical forest in China. We retrieved canopy morphological traits (95th quantile height, leaf area index and foliage height diversity) and physiological traits (proxies of nitrogen, carotenoids and specific leaf area) for each individual canopy tree crown from LiDAR and imaging spectroscopy data, respectively. Based on the multivariate trait space spanned by the six trait axes and filled by measured tree individuals, we mapped forest FD as richness, divergence and evenness, and explored spatial patterns of FD as well as FD–area and FD–tree number relationships. The results show that LiDAR-derived morphological traits and spectral indices of physiological traits are consistent with field measurements and show weak correlations between each other at individual tree level. Morphological functional richness follows a hump-shaped pattern along the elevational gradient of 984–1805 m, with maximum values at elevations around 1450 m, while high physiological functional richness occurs at medium and high elevations. At an ecosystem scale of 30 × 30 m, morphological richness increases continuously with tree density, but physiological richness decreases again at very high densities. Moreover, functional richness shows a logarithmic relationship with increasing area or number of individual trees, and local trait convergence is predominant in our study area. We demonstrate the ability to quantify FD using morphological and physiological traits by remote sensing, which provides a pathway to conduct individual-level trait-based ecology with wall-to-wall data. • Individual tree-based morphological and physiological traits by remote sensing. • Functional diversity representing the variation of traits between individuals. • Functional richness follows a logarithmic increase with area or tree number. • Potential to conduct individual-level trait-based ecology with wall-to-wall data. [ABSTRACT FROM AUTHOR]

The interactions between human activities and land cover have a significant impact on ecosystems. Therefore, studying human activity intensity based on land use or land cover is crucial for understanding the sustainable development of ecosystems. In this study, we selected Anhui Province as the study area and estimated the human activity intensity of land surface (HAILS) in 2015 and 2020 based on the ChinaCover datasets. We further analyzed the spatial, slope, and hydrological distribution characteristics of HAILS and explored the drivers of HAILS changes. The results show that the areas with higher HAILS were mainly located in the central part of Hefei, as well as along the Yangtze and the Huaihe rivers. The largest changes from 2015 to 2020 happened in the gentle slopes with the HAILS of 20–30%, and the percentage of HAILS > 20% decreased over the slope of 15°. In the riparian zone, the HAILS showed a clear decreasing trend after 2 km, while the HAILS in 2020 were higher than in 2015 in each flow-path distance belt, except for the Huaihe river. The HAILS index was strongly correlated with population density, rural population density, urban population density, average land GDP of primary industry, and nighttime light data. The rapid growth of the urban population and economy, as well as ecological protection policies, were identified as drivers of the above changes. Therefore, the HAILS in 2015 and 2020 of Anhui Province provide effective data support to address regional ecological conservation issues. [ABSTRACT FROM AUTHOR]

Spatially and thematically explicit information of wetlands is important to understanding ecosystem functions and services, as well as for establishment of management policy and implementation. However, accurate wetland mapping is limited due to lacking an operational classification system and an effective classification approach at a large scale. This study was aimed to map wetlands in China by developing a hybrid object-based and hierarchical classification approach (HOHC) and a new wetland classification system for remote sensing. Application of the hybrid approach and the wetland classification system to Landsat 8 Operational Land Imager data resulted in a wetland map of China with an overall classification accuracy of 95.1%. This national scale wetland map, so named CAS_Wetlands, reveals that China's wetland area is estimated to be 451,084 ± 2014 km2, of which 70.5% is accounted by inland wetlands. Of the 14 sub-categories, inland marsh has the largest area (152,429 ± 373 km2), while coastal swamp has the smallest coverage (259 ± 15 km2). Geospatial variations in wetland areas at multiple scales indicate that China's wetlands mostly present in Tibet, Qinghai, Inner Mongolia, Heilongjiang, and Xinjiang Provinces. This new map provides a new baseline data to establish multi-temporal and continuous datasets for China's wetlands and biodiversity conservation. [ABSTRACT FROM AUTHOR]

In this article, a vegetation classification hypothesis based on plant biochemical composition is presented. The basic idea of this hypothesis is that the vegetation species/crops have their own biochemical composition characteristics, which are separable from each other for those co-existing species at a specific region. Therefore, vegetation species can be classified based on the biochemical composition characteristics, which can be retrieved from hyperspectral remote-sensing data. In order to test this hypothesis, an experiment was conducted in north-western China. Field data on the biochemical compositions and spectral responses of different plants and an Earth-observing 1 (EO-1) Hyperion image were simultaneously collected. After analysing the relationship between biochemical composition and spectral data collected from Hyperion, the vegetation biochemical compositions were estimated using sample biochemical data and bands of Hyperion data. The vegetation classification was completed using the biochemical content classifier (BCC) and maximum-likelihood classifier (MLC) with all Hyperion bands (MLC_A) and selected bands (MLC_S), which were used for estimating considered biochemical contents (cellulose and carotenoid). The overall classification accuracy of the BCC (95.2%) was as good as MLC_S (95.2%) and better than MLC_A (91.1%), as was the kappa value (BCC 92.849%, MLC_S 92.845%, MLC_A 86.637%), suggesting that the BCC was a feasible classification method. The biochemical-based classification method has higher vegetation classification accuracy and execution speed, reduces data dimension and redundancy and needs only a few spectral bands to retrieve biochemical contents instead of using all of the spectral bands. It is an effective method to classify vegetation based on plant biochemical composition characteristics. [ABSTRACT FROM AUTHOR]

Abstract: Conservation prioritization is an important consideration for planning of natural resources management, allowing decison makers to implement management strategies that are more sustainable in the long-term. However, only a current erosion status or a relative index cannot exactly identify priority area for conservation. The objective of this paper is to identify conservation priorities by a specific multicriteria evaluation method. Trends in erosion risk indicate regions of increasing erosion risk and are also chosen as one of the evaluation criteria to identify the priorities based on the instability of soil erosion in the Yongding river basin. In this paper, vegetation cover, land-use, and slope gradient are used to assess erosion risk and trends in erosion risk are obtained by comparing the results of erosion risk between 2000 and 2006. Using this information, the priority conservation areas are graded into six levels. The two highest priority levels cover the regions with severe erosion or a substantial recent increase in erosion risk (4722.56km2, or 11.82% of the study area), and are recommended as erosion control regions with appropriate conservation strategies. The middle two levels cover the regions with stable erosion status or slight change, needing only minor measures. The method presented is fast and straightforward, showing good potential for successful application in other areas. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) change has been recognized as one of the most important indicators to study ecological and environmental changes. Remote sensing provides an effective way to map and monitor LULC change in real time and for large areas. However, with the increasing spatial resolution of remote sensing imagery, traditional classification approaches cannot fully represent the spectral and spatial information from objects and thus have limitations in classification results, such as the "salt and pepper" effect. Nowadays, the deep semantic segmentation methods have shown great potential to solve this challenge. In this study, we developed an adaptive band attention (BA) deep learning model based on U-Net to classify the LULC in the Three Gorges Reservoir Area (TGRA) combining RapidEye imagery and topographic information. The BA module adaptively weighted input bands in convolution layers to address the different importance of the bands. By comparing the performance of our model with two typical traditional pixel-based methods including classification and regression tree (CART) and random forest (RF), we found a higher overall accuracy (OA) and a higher Intersection over Union (IoU) for all classification categories using our model. The OA and mean IoU of our model were 0.77 and 0.60, respectively, with the BA module and were 0.75 and 0.58, respectively, without the BA module. The OA and mean IoU of CART and RF were both below 0.51 and 0.30, respectively, although RF slightly outperformed CART. Our model also showed a reasonable classification accuracy in independent areas well outside the training area, which indicates the strong model generalizability in the spatial domain. This study demonstrates the novelty of our proposed model for large-scale LULC mapping using high-resolution remote sensing data, which well overcomes the limitations of traditional classification approaches and suggests the consideration of band weighting in convolution layers. [ABSTRACT FROM AUTHOR]

• FAV and SAV covers in Taihu Lake decreased rapidly after 2012. • Controlling factors of FAV and SAV were clarified. • Harvesting activity was mainly driving factor of the loss of FAV. • Water eutrophication should be responsible for the loss of SAV. A rapid degradation of aquatic vegetations in Taihu Lake has roused a wide attention in recent years. Giving large-scale harvesting activity on aquatic vegetation since 2012, whether water eutrophication or the human harvest activity induced the degradation remains controversial and unclear. In this study, based on Landsat and HJ-CCD data acquired from 1984 to 2016 and a 12-year field observation (2005–2016) of water quality, a method was proposed to quantitatively assess impacts of harvesting activity and water quality change on degradations of both floating-leaved aquatic vegetation (FAV) and submerged aquatic vegetation (SAV) in Taihu Lake. First, areas of FAV and SAV covers from 1984 to 2016 in Taihu Lake were mapped using the satellite data, and then the mapped areas were modified to those on a reference date by using phenological curves of FAV and SAV covers. Next, correlations between water quality data and FAV and SAV covers were analyzed by using Pearson correlation analysis based on the data before implementing the human harvesting activity (i.e., before 2012), and multiple general linear models were established based on the selected water quality variables with p-value <0.01 for estimating covers of FAV and SAV from 2012 to 2016. Finally, based on the predicted areas of FAV and SAV covers by the models and the modified areas mapped from satellite data, the influences of water eutrophication and the human harvesting activity on the degradation of FAV and SAV covers were quantitatively assessed. The results indicated that (1) FAV cover exhibited a significant increase from 1984 to 2011 and then a rapid decrease, while SAV cover increased significantly before 2003 and then obviously declined; (2) water level (WL) and total nitrogen (TN) showed significantly negative correlations with FAV and SAV covers, while secchi disk depth (SDD) and SDD/WL had significantly positive correlations with FAV and SAV covers; (3) the human harvesting activity made a major contribution to the loss of FAV cover, and the degradation of SAV cover was mainly due to an increased lake eutrophication and deteriorated underwater light environment. The findings derived from this study could offer a guidance for Taihu Lake ecological restoration and effective management. [ABSTRACT FROM AUTHOR]