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Nanocellulose are promising Pickering emulsion stabilizers for being sustainable and non-toxic. In this work, semicrystalline cellulose oligomers (SCCO), which were synthesized from maltodextrin using cellobiose as primer by in vitro enzymatic biosystem, were exploited as stabilizers for oil-in-water Pickering emulsions. At first, the morphology, structure, thermal and rheological properties of SCCO suspensions were characterized, showing that SCCO had a sheet morphology and typical cellulose-Ⅱ structure with 56 % crystallinity. Then the kinetic stabilities of emulsions containing various amounts of SCCO were evaluated against external stress such as pH, ionic strength, and temperature. Noting that SCCO-Pickering emulsions exhibited excellent stabilities against changes in centrifugation, pH, ionic strengths, and temperatures, and it was also kinetically stable for up to 6 months. Both SCCO suspensions and their emulsions exhibited gel-like structures and shear-thinning behaviors. These results demonstrated great potential of SCCO to be applied as nanocellulosic emulsifiers in food, cosmetic and pharmaceutical industries.

D-Mannitol (hereinafter as mannitol) is a six-carbon sugar alcohol with diverse applications in food and pharmaceutical industries. To overcome the drawbacks of the chemical hydrogenation method commonly used for mannitol production at present, there is a need to search for novel prospective mannitol production strategies that are of high yield and low cost. In this study, we present a novel approach for the stoichiometric synthesis of mannitol via an in vitro synthetic enzymatic biosystem using the low-cost starch as substrate. By dividing the overall reaction pathway into three modules which could be executed sequentially in one pot, our design aimed at the stoichiometric conversion of starch-based materials into mannitol in an ATP-independent and cofactor-balanced manner. At optimized conditions, high product yields of around 95-98% were achieved using both 10 g/L and 50 g/L maltodextrin as substrate, indicating the potential of our designed system for industrial applications. This study not only provides a high-efficient strategy for the synthesis of mannitol but also expands the product scope of sugar alcohols by the in vitro synthetic enzymatic biosystems using low-cost starch-based materials as the input. KEY POINTS : • We described a design-build-test-learn pipeline to construct in vitro biosystems. • The designed system comprised six key enzymes and another three enzymes. • The system converted maltodextrin stoichiometrically to mannitol in one pot.

Hydrophilic PVDF membrane was fabricated in single step via cellulose molecular coating prepared by homogeneous solution of cellulose oligomer synthesized by enzyme catalysis (EC)/Trifluoroacetic acid (TFA). Both smooth and rough coatings can be prepared by regulating the aggregative structure of cellulose molecular coatings. The chemical structures of the cellulose and its coating were confirmed by 1H Nuclear Magnetic Resonance Spectroscopy (1H NMR) and Fourier Transform Infrared Spectroscopy (FTIR). The aggregation structure of the cellulose coating was characterized by wide-angle X-ray diffraction (WAXD). X-ray Photoelectron Spectroscopy (XPS) studies unveiled the strong binding of cellulose molecular to PVDF membranes at the interface. Scanning Electron Microscopy (SEM) morphologies show that the cellulose molecules were uniformly coated on the surface and cross section of the membranes. Water contact angle measurement suggested that cellulose molecular coating possessed high hydrophilicity and wetting rate, giving the PVDF membranes high water flux and excellent anti-fouling performances. The coated membranes also show excellent adsorption and desorption performances for crystal violet (CV). The superior performances of incompletely crystallized cellulose coating over amorphous cellulose coating were attributed to the increased specific surface area resulting from the surface roughness. The construction of cellulose molecular coating with controlled aggregation structure provides a novel efficient technology in the field of purification of wastewater, opening up a new approach to the microstructural manipulation of the hydrophilic coating.

Delineation of agricultural fields is desirable for operational monitoring of agricultural production and is essential to support food security. Due to large within-class variance of pixel values and small inter-class difference, automated field delineation remains to be a challenging task. In this study, a strategy is proposed to effectively address this issue. Firstly, a framework was developed using the Canny operator connected with the Watershed segmentation algorithm (CW) to quickly label the training dataset, which minimizes the workload of dataset generation in comparison with the commonly used manual vectorization. Secondly, a CW-trained deep semantic segmentation network, recurrent residual U-Net, was selected to mine the low level and deep semantic features. Finally, a boundary connecting method (to integrate fragmented boundaries) was used to generate the agricultural field boundary. The proposed methods are tested over smallholder agricultural landscape in Heilongjiang province, China, using Sentinel-2 imagery. Compared with the U-Net (overall accuracy (OA) 82.18%), the residual U-Net (ResU-Net, with OA 85.78%), traditional object-based image analysis (OBIA, with OA about 82%), and the existing 10-m resolution global land cover map (FROM-GLC10), the proposed method shows an improved performance (OA 89.28%, and Kappa 0.85). The successful application of the proposed method suggests that the recurrent residual U-Net model has great universality in agricultural field boundary extraction, and the automated technique has the potential of being applied to other regions.

In the karst regions of southwest China, rocky desertification is one of the most serious problems in land degradation. The bedrock exposure rate is an important index to assess the degree of rocky desertification in karst regions. Because of the inherent merits of macro-scale, frequency, efficiency, and synthesis, remote sensing is a promising method to monitor and assess karst rocky desertification on a large scale. However, actual measurement of the bedrock exposure rate is difficult and existing remote-sensing methods cannot directly be exploited to extract the bedrock exposure rate owing to the high complexity and heterogeneity of karst environments. Therefore, using unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data for Xingren County, Guizhou Province, quantitative extraction of the bedrock exposure rate based on multi-scale remote-sensing data was developed. Firstly, we used an object-oriented method to carry out accurate classification of UAVimages. From the results of rock extraction, the bedrock exposure rate was calculated at the 30 m grid scale. Parts of the calculated samples were used as training data; other data were used for model validation. Secondly, in each grid the band reflectivity of Landsat-8 OLI data was extracted and a variety of rock and vegetation indexes (e.g., NDVI and SAVI) were calculated. Finally, a network model was established to extract the bedrock exposure rate. The correlation coefficient of the network model was 0.855, that of the validation model was 0.677 and the root mean square error of the validation model was 0.073. This method is valuable for wide-scale estimation of bedrock exposure rate in karst environments. Using the quantitative inversion model, a distribution map of the bedrock exposure rate in Xingren County was obtained.

In this study, an empirical assessment approach for the risk of crop loss due to water stress was developed and used to evaluate the risk of winter wheat loss in China, the United States, Germany, France and the United Kingdom. We combined statistical and remote sensing data on crop yields with climate data and cropland distribution to model the effect of water stress from 1982 to 2011. The average value of winter wheat loss due to water stress for the three European countries was about −931 kg/ha, which was higher than that in China (−570 kg/ha) and the United States (−367 kg/ha). Our study has important implications for the operational assessment of crop loss risk at a country or regional scale. Future studies should focus on using higher spatial resolution remote sensing data, combining actual evapotranspiration to estimate water stress, improving the method for downscaling of statistical crop yield data and establishing more sophisticated zoning methods.

Information on crop types derived from remotely sensed images provides valuable input for many applications such as crop growth modeling and yield forecasting. In this paper, a random forest (RF) classifier was used for crop classification using multispectral RapidEye imagery over two study sites, one in north-eastern China and one in eastern Ontario, Canada. Both vegetation indices (VIs) and textural features were derived from the RapidEye imagery and used for classification. A total of 20 VIs, categorized into two groups with and without the red edge (RE) band in an index, were calculated. A total of eight types of textural features were derived using four different window sizes from both the RE and the near-infrared bands. To reduce redundancies among the VIs and textural features, feature selection using the principal component analysis, correlation analysis, and stepwise discriminant analysis was performed. Results showed that the overall classification accuracy was improved by ∼7% when the RE indices were combined with the five spectral bands in classification, as compared with that using the five bands alone. When textural information was included, the overall classification accuracy increased by ∼6% compared with that using the band reflectance alone. Furthermore, when all the features (band reflectance, VIs, and texture) were used, the overall classification accuracy increased by ∼12% compared with that using only the band reflectance. The RF importance measures showed that the RE reflectance was important for classification, as indicated by the high importance for the triangular vegetation index, transformed chlorophyll absorption in reflectance index, and green-rededge normalized difference vegetation index. The gray-level co-occurrence matrix mean is the most useful for classification among the textural features. The study provides a means to feature extraction and selection for crop classification from remote sensing imagery.

Timely and accurate mapping crops is essential for agricultural management, policy making and food security. The smallholder agricultural systems lead to large number of fragmented and heterogeneous landscape, making fine crop mapping a huge challenge. Feature and classifier selection are two important influencing factors in crop classification. However, there are few systematic tests to determine the specific features and classifiers needed for heterogeneous agricultural landscapes. In this study, 24 candidate spectral features, 8 spatial texture features from the red-edge (RE) and near-infrared (NIR) bands, and 4 supervised classifiers (i.e. random forest (RF), support vector machine (SVM), artificial neural network (ANN), and maximum likelihood classifier (MLC)) were used for crop mapping. 60 spatially heterogeneous landscapes in Heilongjiang Province, northeastern China were selected as the study areas, evaluated by compositional heterogeneity (homogeneity index, HOI) and configurational heterogeneity (splitting index, SPLIT). The results summarized a look-up table for searching the optimum classification features and classifiers in different landscape, providing a reference for future crop classifications and preventing the consumption of computing time. The results revealed that (1) an optimal feature-subset (with reduced data volume by 65%) can achieve high-accuracy crop mapping in heterogeneous regions. (2) The optimum type and number of features and classifiers are landscape sensitive. When a specific accuracy was required, homogenous regions need a smaller number of features and a simple MLC could meet the requirement. (3) The impact of configurational heterogeneity on textural features is more significant, while compositional heterogeneity performs better on spectral Vegetation Indices (VIs). Findings from this study provide a general guideline for crop mapping in plains or fragmented landscape and areas with single or complex planting structure.

Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data ( $R^{\mathrm {\mathbf {2}}} = 0.6942$ , RMSE =0.0884), compared with the widely used dimidiate pixel model ( $R^{\mathrm {\mathbf {2}}} = 0.7034$ , RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.

In this study, an object-based image analysis (OBIA) approach was developed to classify field crops using multi-temporal SPOT-5 images with a random forest (RF) classifier. A wide range of features, including the spectral reflectance, vegetation indices (VIs), textural features based on the grey-level co-occurrence matrix (GLCM) and textural features based on geostatistical semivariogram (GST) were extracted for classification, and their performance was evaluated with the RF variable importance measures. Results showed that the best segmentation quality was achieved using the SPOT image acquired in September, with a scale parameter of 40. The spectral reflectance and the GST had a stronger contribution to crop classification than the VIs and GLCM textures. A subset of 60 features was selected using the RF-based feature selection (FS) method, and in this subset, the near-infrared reflectance and the image acquired in August (jointing and heading stages) were found to be the best for crop classifica...

Human diets strongly rely on wheat, maize, rice and soybean; research on the potential crop productivity of these four main crops could provide the basis for increasing global crop yields. The evaluation model of realistic potential crop productivity based on remote sensing and agro-ecological zones was proposed in this study to provide reliable reference data for world food security. The statistical data on these four main crops yields were obtained from the FAO. The model was used to investigate the potential production of four staple crops in the world. The distributions of the realistic potential productivity of four staple crops (winter wheat, maize, rice and soybean) were produced. In the main producing countries of the four staple crops, statistical analysis was conducted on the realistic potential productivity (RPP) of the four staple crops, the highest productivity (HP) during the period 1983–2011 and the gap between RPP and HP.

In North Korea, reliable and timely information on crop acreage and spatial distribution is hard to obtain. In this study, we developed a fast and robust method to estimate crop acreage in North Ko...

With the degradation of the environment and the acceleration of urbanization, urban residential land has been undergoing rapid changes and has attracted great attention worldwide. Meanwhile, the quantitative evaluation of the suitability of urban residential land is essential for a better and more powerful understanding of urban residential land planning and improvement. Most urban land suitability studies rely solely on remote sensing data and GIS data to evaluate natural suitability, and few studies have focused on urban land suitability from a socioeconomic perspective. Consequently, this paper integrates remote sensing data (GaoFen-2 satellite image) and social sensing data (Tencent User Density data, Point-of-interest data and OpenStreetMap data) to establish an evaluation framework for analyzing the suitability of urban residential land in the Haidian District, Beijing, China, in which, ecological comfortability, locational livability and overall suitability were evaluated according to five attributes extracted from urban residential land via the factor analysis method. The evaluation results of this case study show that, compared with the suburban area in the northwest, the urban area tends to have lower ecological comfortability and higher locational livability. The overall suitability increases from southeast to northwest, consistent with the spatial distribution of ecological comfortability. This framework can potentially assist with the sustainable development of residential lands and urban land use planning.

Additional file 1. Additional Figures and Tables.

The spatial distribution of the population is uneven for various reasons, such as urban-rural differences and geographical conditions differences. As the basic element of the natural structure of the population, the age structure composition of populations also varies considerably across the world. Obtaining accurate and spatiotemporal population age structure maps is crucial for calculating population size at risk, analyzing populations mobility patterns, or calculating health and development indicators. During the past decades, many population maps in the form of administrative units and grids have been produced. However, these population maps are limited by the lack of information on the change of population distribution within a day and the age structure of the population. Urban functional regions (UFRs) are closely related to population mobility patterns, which can provide information about population variation intraday. Focusing on the area within the Beijing Fifth Ring Road, the political and economic center of Beijing, we showed how to use the temporal scaling factors obtained by analyzing the population survey sampling data and population dasymetric maps in different categories of UFRs to realize the intraday variation mapping of elderly individuals and children. The population dasymetric maps were generated on the basis of covariates related to population. In this article, 50 covariates were calculated from remote sensing data and geospatial data. However, not all covariates are associate with population distribution. In order to improve the accuracy of dasymetric maps and reduce the cost of mapping, it is necessary to select the optimal subset for the dasymetric model of elderly and children. The random forest recursive feature elimination (RF-RFE) algorithm was introduced to obtain the optimal subset of different age groups of people and generate the population dasymetric model in this article, as well as to screen out the optimal subset with 38 covariates and 26 covariates for the dasymetric models of the elderly and children, respectively. An accurate UFR identification method combining point of interest (POI) data and OpenStreetMap (OSM) road network data is also introduced in this article. The overall accuracy of the identification results of UFRs was 70.97%, which is quite accurate. The intraday variation maps of population age structure on weekdays and weekends were made within the Beijing Fifth Ring Road. Accuracy evaluation based on sampling data found that the overall accuracy was relatively high—R2 for each time period was higher than 0.5 and root mean square error (RMSE) was less than 0.05. On weekdays in particular, R2&nbsp, for each time period was higher than 0.61 and RMSE was less than 0.02.

Spatially explicit mapping of the Phenological Transition Zone (PTZ) is the prerequisite to PTZ dynamics monitoring and characterization, and is significant for more effective management of natural resources, biodiversity, and conservation areas in the context of climate change. However, to our knowledge there have been no studies to delineate spatially explicit PTZs and there are some potential issues associated with the mapping of other biogeographical transition zones. This paper developed a method to explicitly delineate PTZs continuously from 2003 to 2017 in the study area of the Forest-Grassland Mosaic Region (FGMR) in northeastern China, highlighting both their transitional and phenological characteristics. Annual Normalized Difference Vegetation Index (NDVI) time series were fed into the dynamic-time-warping (DTW) based fuzzy c-medoids (FCMdd) clustering to produce 12 fuzzy phenoregions. Potential PTZ pixels were identified for each pair of fuzzy phenoregions via the fuzzy intersection operator. The Delaunay tessellation field estimator was applied to convert the discrete potential PTZ pixels to continuous weighted density surface. The 7 consistently and 8 intermittently existing PTZs were exhaustively derived via thresholding. Three different types of gradient, inclusive and mosaic PTZs were identified. The PTZ dynamics were rapid and dramatic due to its phenological and transitional nature. This PTZ mapping method can be easily extended to other areas with no or minor adjustments, and it can also be adapted to map other biogeographical transition zones.

Frost events pose a threat to normal development of winter wheat and increase risk of production loss, even given the context of global warming. On the Huang-Huai-Hai Plain (HHHP), which is the largest region of winter wheat cultivation in China, frost damage has become one of the major meteorological catastrophes. In recent years, both frequency of occurrence and severity of frost events in this region have exhibited an upward trend. For timely and accurate monitoring of single-event frost damage of winter wheat, this study developed a frost-monitoring framework that considers the processes of the evolution of a frost event and the response of winter wheat, as well as the lag between them. The framework adopts a NDVI valley area index (NVAI) to represent the gradual response process of winter wheat to frost. In this framework, the normalized NVAI (NNVAI) is used to indicate the degree of damage, and a two-phased damage assessment (i.e., T1 and T2) is adopted to resolve the contradiction between the urgency of obtaining information on frost severity, the spatial distribution of frost damage, and the delayed response process of winter wheat to frost. Using prediction models based on historical statistics, assessment at the T1 stage estimates the possible damage after the end of the frost, and assessment at the T2 stage evaluates the actual damage caused by frost when the winter wheat has recovered. Application of the proposed framework to a frost event that occurred on the HHHP during March 4–9, 2018 revealed that the average absolute percentage error between actual and predicted damage across the entire region was 33 %. Moreover, the prediction of damage was made 13 days ahead of the occurrence of actual damage. Thus, the framework could provide guidance to enable action to be taken to reduce the impact of frost. The proposed monitoring framework fully considers the processes of the evolution of a frost event and the response of winter wheat to frost, and it learns potential patterns from historical frost events to provide early prediction of potential frost damage. Consequently, the framework is considered suitable for wide practical application.

Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 2.28%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy.

Over the past decades, croplands in China experienced rapid losses as a result of nationwide urbanization, industrialization, socioeconomic development, and implementation of ecological projects. These developments were driven by continuous economic reforms and environmental protection policies. Croplands are precious land resources influenced by overpopulation in China. Cropland losses threaten national food security. This study analyzed spatial patterns of cropland changes, driving forces, and its adaptation. Object-based approach was adopted to accurately measure cropland changes using remote sensing data. Conversion matrix and statistical regression model were used to explore impacts of socioeconomic factors on cropland changes. A case study on Beijing revealed that cropland in Beijing decreased by 47% in the past 35 years. Primary driving forces in cropland loss included urban expansion, implementation of ecological projects, orchard plantation, and water resource protection. Increased migration and economic development resulted in urban expansion. To promote sustainable development and to meet demands of urban and environmental protection, cropland adaptation was performed by increasing value-added commercial crop plantation, improving crop yield, and reducing cultivation intensity for effective water consumption.

Wheat is the most widely grown crop globally and an essential source of calories in human diets. Maintaining and increasing global wheat production is therefore strongly linked to food security. In this paper, the evaluation model of winter wheat potential productivity was proposed based on agro-ecological zone and the historical winter wheat yield data in recent 30 years (1983-2011) obtained from FAO. And the potential productions of winter wheat in the world were investigated. The results showed that the realistic potential productivity of winter wheat in Western Europe was highest and it was more than 7500 kg/hm2. The realistic potential productivity of winter wheat in North China Plain were also higher, which was about 6000 kg/hm2. However, the realistic potential productivity of winter wheat in the United States which is the main winter wheat producing country were not high, only about 3000 kg/hm2. In addition to these regions which were the main winter wheat producing areas, the realistic potential productivity in other regions of the world were very low and mainly less than 1500 kg/hm2, like in southwest region of Russia. The gaps between potential productivity and realistic productivity of winter wheat in Kazakhstan and India were biggest, and the percentages of the gap in realistic productivity of winter wheat in Kazakhstan and India were more than 40%. In Russia, the gap between potential productivity and realistic productivity of winter wheat was lowest and the percentage of the gap in realistic productivity of winter wheat in Russia was only 10%.

The North China Plain (NCP) was selected as the study area and the effects of water and heat were analysed to determine the dominant factor affecting winter wheat growth. The mean, minimum and maximum temperatures, precipitation and soil moisture data were selected to analyse the correlations between the leaf area index (the growth indicator) and these factors using long time series half-monthly data (2–5 months) (from 1982 to 2010). The results showed that temperature was the main factor affecting the growth of winter wheat in the NCP. The growth of winter wheat had weak correlations with precipitation and soil moisture and the influence of water on winter wheat growth was smaller than the influence of heat. In the northern part of the NCP, mainly including the north-west region of Shandong Province and the southern region of Hebei Province, irrigation was necessary in late February and early March.

Rice acreage estimation is a key aspect to guarantee food security and also important to support government agricultural subsidy system. In this paper, we explored a sophisticated method to improve rice estimation accuracy at county scale and we developed our approach with China Environment Satellite HJ-1A/B data in Hunan Province, a fragmented area with complex rice cropping patterns. Our approach improved the estimation accuracy by combing supervised and unsupervised classification upon decomposition of mixed pixels model, and the rice estimation results, validated by ground survey data, showed a close relationship (RMSE≈3.40) with survey figures, the estimated accuracy (EA) reached 83.74% at county level according to the sub-pixel method, and the accuracy can be increased about 12% compared to the pure-pixel method. The results suggest that decomposition of mixed pixels method has great significance to the improvement of rice acreage estimation accuracy, and can be used in mountainous and broken planting area.

Automatic land cover update was an effective means to obtain objective and timely land cover maps without human disturbance. This study investigated the efficacy of multi-temporal remote sensing data and advanced non-parametric classifier on improving the classification accuracy of the automatic land cover update approach integrating iterative training sample selection and Markov Random Fields model when the historical remote sensing data were unavailable. The results indicated that two-temporal remote sensing data acquired in one crop growth season could significantly improve the classification accuracy of the automatic land cover update approach by approximately 3–4%. However, the support vector machine (SVM) classifier was not suitable to be integrated in the automatic land cover update approach, because the huge initially selected training samples made the training of the SVM classifier unrealizable.

Forest cover information is essential for natural resource management and for climate change studies. In this paper, the fractional forest cover (FFC) in Northeast China was estimated using neural networks (NNs) based on the Global Inventory Modeling and Mapping Studies (GIMMS3g) Normalized Difference Vegetation Index (NDVI) data with 8-km resolution from 1982 to 2011. Furthermore, the relationship between FFC and two key climatic parameters (temperature and precipitation) was also analyzed. The validation results indicated a satisfactory performance ( ${\bf {R}}^{\bf {2}} = {\bf {0.81}}$ , ${\bf {RMSE}} = {\bf {11.7\%}}$ ) of the FFC estimation method using NNs and time-series GIMMS3g NDVI data. The temporal and spatial characteristics of FFC changes were analyzed. The forest cover had a slightly decreasing trend during the study period for the entire Northeast China region. However, there were two distinct periods with opposite trends in the FFC change. The FFC had first increased from 1982 to 1998 ( ${\bf {0.391\%}}\ {\bf {year}}^{{\bf {- 1}}}$ ), and then decreased from 1998 to 2011 ( ${\bf {- 0.667\%}}\ {\bf {year}}^{{\bf {- 1}}}$ ). The correlation analysis between the FFC and the climatic variations suggested that temperature and precipitation were not the decisive factors on controlling FFC changes in most of the Northeast China regions, and active forest disturbance might be the more important factor for FFC change in Northeast China.

Crop type identification is the basis of crop acreage estimation and plays a key role in crop production prediction and food security analysis. However, the accuracy of crop type identification using remote-sensing data needs to be improved to support operational agriculture-monitoring tasks. In this paper, a new method integrating high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data is proposed to improve crop type identification accuracy in Hungary. Four crop growth features, including peak value, date of peak occurrence, average rate of green-up, and average rate for the senescence period were extracted from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) profiles and spatially enhanced to 30 m resolution using resolution merge tools based on a multiplicative method to match the spatial resolution of Landsat Thematic Mapper (TM) data. A maximum likelihood classifier (ML...

Winter wheat biomass was estimated using HJ CCD and MODIS data, combined with a radiation use efficiency model. Results were validated with ground measurement data. Winter wheat biomass estimated with HJ CCD data correlated well with observed biomass in different experiments (coefficients of determination R2 of 0.507, 0.556 and 0.499; n = 48). In addition, R2 values between MODIS estimated and observed biomass are 0.420, 0.502 and 0.633. Even if we downscaled biomass estimated using HJ CCD data to MODIS pixel size (9 × 9 HJ CCD pixels to approximate that MODIS pixel), R2 values between estimated and observed biomass were still higher than those from MODIS. We conclude that estimation with remote sensing data, such as the HJ CCD data with high spatial resolution and shorter revisit cycle, can show more detail in spatial pattern and improve the application of remote sensing on a local scale. There is also potential for applying the approach to many other studies, including agricultural production estimation...

Many methods have been used to generate gridded population maps by downscaling demographic data. As one of these methods, the accuracy of the dasymetric model depends heavily on the covariates. Point-of-interest (POI) data, as important covariates, have been widely used for population estimation. However, POIs are often used indiscriminately in existing studies. A few studies further used selected categories of POIs identified based only on the nonspatial quantitative relationship between the POIs and population. In this paper, the spatial association between the POIs and population distribution was considered to identify the POIs with a strong spatial correlation with the population distribution, i.e., population-sensitive POIs. The ability of population-sensitive POIs to improve the fine-grained population mapping accuracy was explored by comparing the results of random forest dasymetric models driven by population-sensitive POIs, all POIs, and no POIs, along with the same sets of multisource remote sensing and social sensing data. The results showed that the model driven by population-sensitive POI had the highest accuracy. Population-sensitive POIs were also more effective in improving the population mapping accuracy than were POIs selected based only on their quantitative relationship with the population. The model built using population-sensitive POIs also performed better than the two popular gridded population datasets WorldPop and LandScan. The model we proposed in this study can be used to generate accurate spatial population distribution information and contributes to achieving more reliable analyses of population-related social problems.

In some practical classification applications, our focus may be just a specific crop (e.g., rice, corn, or soybean). Therefore, how to distinguish the crop of interest from other classes accurately and quickly is an issue worth studying. We propose an approach that combines whitening transformation (WT)/interest-class-based whitening transformation (ICWT) and one-class classification (OCC) methods to identify the crop of interest. The image created by applying WT/ICWT to the original image is referred to as the totally/partially whitened image, respectively. Three typical OCC classifiers—one-class support vector machine (OCSVM), positive and unlabeled learning (PUL), and maximum entropy (MaxEnt)—are trained to identify the crop of interest, using a totally/partially whitened image. The research results demonstrate that (1) both ICWT and WT enabled OCSVM to improve the producer’s accuracy of the crop of interest by more than 20%, and the performance of the proposed approach reached or surpassed that of conventional supervised methods; (2) PUL and MaxEnt performed better than OCSVM, especially in identifying local major crops using different phased data; and (3) the proposed approach is efficient in single crop identification with remote sensing imagery and is worth recommending for practical applications.

Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices. China's global crop-monitoring system (CropWatch) uses remote sensing data combined with selected field data to determine key crop production indicators: crop acreage, yield and production, crop condition, cropping intensity, crop-planting proportion, total food availability, and the status and severity of droughts. Results are combined to analyze the balance between supply and demand for various food crops and if needed provide early warning about possible food shortages. CropWatch data processing is highly automated and the resulting products provide new kinds of inputs for food security assessments. This paper presents a comprehensive overview of CropWatch as a remote sensing-based system, describing its structure, components, and monitoring approaches. The paper also presents examples of monitoring results and discusses the strengths and...

This study presents a crop planting and type proportion (CPTP) method for crop acreage estimation of complex and diverse agricultural landscapes. CPTP has three major components: (1) Crop planting proportion (CPP), estimated with wide-swath satellite remote sensing data to completely cover the monitoring area by segmenting cropped and non-cropped areas through unsupervised classification. (2) Crop type proportion (CTP), estimated by transect sampling and a special GPS-Video-GIS instrument (GVG) and a visual interpretation of crop type proportion in collected pictures for different strata. (3) Multiplication of CPP and CTP with arable land area at the strata level, summed to the province and national level. Validation has been done with in situ data for different agricultural landscapes over China. Both CPP estimation with remote sensing data and CTP estimation through ground survey have a high accuracy with average relative error (RE) and root mean square error (RMSE) equal to 1.42% and 1.67% for CPP and to 2.63% and 2.25% for CTP. The RE for crop acreage estimation equals to 4.09%. The CPTP method thus has a high accuracy, yields timely information at low costs, and is robust and provides objective results. The study concludes that the CPTP method can be used for large area crop acreage estimation of complex agriculture landscapes.

Opium is a narcotic obtained from opium poppy and is the raw material of heroin for the illegal drug trade. Monitoring the illegal concentrated cultivation of opium poppy in major regions is critical for the understanding by governments and international communities of the scale of illegal drug trade. This paper investigates whether opium poppy can be discriminated from its coexisting plants using analytical-spectral-device field spectrometer data in the visible to short-wave infrared spectral range. Canopy spectral measurements were conducted during three different growth periods of opium poppy. A synthetic method with three analysis levels was applied to discriminate opium poppy from other species and to select optimal bands for opium poppy discrimination. First, the Mann-Whitney U-test method was used to test the spectral reflectance difference between opium poppy and coexisting crops at each wavelength. Then, the Jeffries-Matusita distance and band correlation analysis were conducted to select the optimal wavebands for discriminating opium poppy using the significant wavebands from the test results. Finally, classification and regression tree analysis was employed to validate the classification accuracy based on the selected optimal wavebands. The results indicated that the spectral reflectance of opium poppy was significantly different from that of coexisting crops in many surveyed wavebands, and opium poppy could be discriminated using a field survey spectrum at canopy level. The best time for discriminating opium poppy from coexisting crops was around flowering time. This paper provided the prerequisite for monitoring opium poppy using satellite remote sensing data in some regions of concern.

Crop acreage estimation is a key aspect to forecast crop production. Maize acreage estimation becomes more and more important because the fast production changes every year due to the dynamics of the prices. This paper focuses on maize acreage estimation in the North China Plain using ENVISAT MERIS and CBERS-02B CCD data of 2008. Firstly, adaptive maximum likelihood classification of CBERS-02B CCD images based on ground survey provided reliable maize area fraction image (AFI). CBERS derived AFIs (as reference AFI) were used to train a 3-layer back-propagation neural network, this was then used to the whole MERIS data to generate MERIS AFIs (AFIe). To estimate maize acreage, the maize AFI from MERIS was masked with cropland dataset and maize acreages were estimated by zonal statistic of maize AFI at district level. The statistical results were also modified using a non-arable coefficient to remove the effects of non-arable factors. The results showed a close relationship between estimated and statistical maize acreage (R^2~0.88). At province level, the estimation error is approximately 8%. This method is valuable for wide-scale, regional crop acreage estimation at the early stage of growing season. The study gives suggestions about high resolution image acquisition, spatial distribution and cropland datasets.

Crop classification is a key issue for agricultural monitoring using remote-sensing techniques. Synthetic aperture radar (SAR) data are attractive for crop classification because of their all-weather, all-day imaging capability. The objective of this study is to investigate the capability of SAR data for crop classification in the North China Plain. Multi-temporal Envisat advanced synthetic aperture radar (ASAR) and TerraSAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using different combinations of these SAR data and texture features. The results indicated that multi-configuration SAR data achieved satisfactory classification accuracy (best overall accuracy of 91.83%) in the North China Plain. ASAR performed slightly better than TerraSAR data acquired in the same time span for crop classification, while the combination of two frequencies of SAR data (C- and X-band) was better than the multi-temporal C-band data. Two temporal ASAR data acquired in late...

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.

Opium is a narcotic obtained from opium poppy and raw materials of heroin for illegal drug trade. Information on opium production is important for governments and international communities to understand the scale of illegal drug trade and to implement drug fighting policies. Opium production can be estimated from opium yield and poppy cultivation acreage. Poppy cultivation acreage in turn can be estimated using remote sensing technique. This study explores the feasibility of assessing opium yield using remote sensing technique. The objective of this study is to find an effective biophysical indicator that can be related to both opium yield estimation and remote sensing measurements. Data are obtained from three years ground experiments conducted in an official farm in North-Western China. Opium yield and main plant and environmental parameters of opium poppy are measured. A linear regression analysis measures the relationship between opium yield and main plant parameters. A strong correlation between opium yield and leaf area index (LAI) in flowering and harvesting periods of opium poppy is observed (R^2=0.826 and 0.862 respectively). LAI is a good indicator during flowering and harvesting periods and can be used to build a model for opium yield estimation. Furthermore, LAI can be retrieved using remote sensing at a region scale. The study shows that remote sensing could be used to estimate opium yield in opium poppy concentrated regions, and that a more comprehensive and precise opium production information could be obtained for further analysis of illegal drug circulating trend and policy.

Presents corrections to affiliation information in the paper, “Image classification using rapideye data: Integration of spectral and textual features in a random forest classifier,” (Zhang, H., et al),IEEE J. Sel. Topics Appl. Earth Observ. Remote Ses., vol. 10, no. 12, pp. 5334–5349, Dec. 2017.

In light of the need for fine-grained, accurate, and timely urban land use information, a per-field classification approach was proposed in this paper to automatically map fine-grained urban land use in a study area within Haidian District, Beijing, China, in 2016. High-resolution remote sensing imagery and multi-source social sensing data were used to provide both physical and socioeconomic information. Four categories of attributes were derived from both data sources for urban land use parcels segmented by the OpenStreetMap road network, including spectral/texture attributes, landscape metrics, Baidu Point-Of-Interest (POI) attributes, and Weibo attributes. The random forests technique was adopted to conduct the classification. The importance of each attribute, attribute category, and data source was evaluated for the classification as a whole and the classification of individual land use types. The results showed that a testing accuracy of 77.83% can be achieved. The approach is relatively good at classifying open space and residential parcels, and poor at classifying institutional parcels. While using solely remote sensing data or social sensing data can achieve equally high overall accuracy, their importance varies in terms of the classification of individual classes. Landscape metrics are the most important for open space parcels. Spectral/texture attributes are more important in identifying institutional and residential parcels. The classification of business parcels relies more on landscape metrics and social sensing data, and less on spectral/texture attributes. The classification accuracy can be potentially improved upon the acquisition of purer parcels and the addition of new attributes. It is expected that the proposed approach will be useful for the routine update of urban land use information and large-scale urban land use mapping.

Assessment of land condition is a basic prerequisite for finding the degradation of a territory under climatic and human pressures leading to desertification. The temporal change in vegetation productivity is a key indicator of land degradation. In this paper, taking the Beijing-Tianjin dust and sandstorm source region (BTDSSR) as a case, the annual-maximum normalized difference vegetation index (NDVI_max) and net primary production (NPP) dynamic trends during 2001-2010 were analyzed, with the Mann-Kendall test and the Correlation Analysis method. The results showed that the two vegetation indicators (NDVI_max and NPP) had shown a downward trend with the two methods in the past 10 years and the land was in the degradation. For the analysis of the two vegetation indicators (NDVI_max and NPP), it indicated a decreasing trend in 66.60% and 70.26% of the study area according to the Mann-Kendall test and in 59.53% and 64.35% of the study area according to the correlation analysis method. However, the change trends were not significant, the significant trends at the 95% confidence level accounted for only a small proportion. Analysis of NDVI_max and NPP series showed a significant decreasing trend in 6.79% and 12.3% with the Mann-Kendall test. The NDVI_max and NPP change trends showed obvious positive link with the precipitation in the study area.

Wheat yield estimation in this paper is based on the above ground biomass estimation. The model of above ground biomass estimation requires satellite data, which express the vegetation status. The model was applied in Russia where winter wheat is widely grown. The results showed a high accuracy in estimating winter wheat yield. The range of fractional differences between estimated and observed yields is between -0.40 and 0.50 in 2011, and 83% of the fractional differences are between -0.30 and 0.30. When this method is used in large areas, parameters calibration is crucial. We also summarize that in future study, high resolution images, meteorological factors retrieved by remote sensing data and more field observed data should be used to improve the method.

Crop residue, as one of important factors of eco-agricultural system, can influence the flow of nutrients, carbon, water, and energy in terrestrial ecosystems. Remote sensing approaches have been investigated to obtain the crop residue cover (CRC) over large areas rapidly and objectively. While Cellulose Absorption Index (CAI) is a reliable indicator for crop residue cover estimation, it can only be applied to hyperspectral sensor, which usually has a narrow swath width. A new approach utilizing the shape of the spectrum of consecutive bands of MODIS data was introduced to distinguishing soil, crop residue and live crops. Since Landsat 5 TM's band configuration is similar to MODIS and provides fine spatial resolution (30 m) for agriculture in China, it offers an opportunity to assess crop residue cover over large areas. The objective of this research is to evaluate the Landsat-based angle indices for estimating CRC. Four spectral angle indices were derived by surface reflectance of Landsat TM. The results showed that shortwave angle slope index (SASI) is an ideal index for crop residue cover estimation. Simple linear regression reveals a coefficient of determination (R2) of 0.7895 between measured CRC and SASI for the calibration dataset and RMSE of 0.0886 between measured and predicted CRC for validation dataset. Crop residue cover for Hongxing farm was estimated using Landsat-based SASI and sorted into three categories, corresponding to intensive (30% cover) tillage. Results in this study demonstrated that Landsat-based SASI was capable of crop residue cover estimation.

High-resolution remote sensing image is usually considered that its pixel size is less than 10 meters. Traditional classification methods based on pixels are not fit for the classification of this kind of image because this kind of image has higher spatial resolution and more local heterogeneity compared to the low-resolution remote sensing image data. Object-oriented image classification method provides a good technique to solve this problem. This method segments image to create homogeneous regions or image objects through region merging or boundary detection algorithms. Objects possess more features such as geometric and structure characteristics besides spectral characteristics than pixels. So it is important to select appropriate characterstics in classification. Class-Related features, landscape pattern metrics, geometric attributes of objects, spatial information are very useful characteristics. The paper will pay more attention to the selection and integrative utilization of these features and spectral characteristics, and give several examples to show their performance. (1) If We want to extract a kind of feature which has similar spectral characteristic as the other feature but has a certain positional relationship with a specific feature, at this time, Class-Related features will be very efficacious. (2) Both river and pounds have also similar spectral characteristic, but has different geometric characteristic in the landscape pattern metrics. Synthetically use of the landscape pattern metrics and spectral characteristic will wok well. (3) In the same segmentation scale, the objects from the region with more homogeneity will be bigger than other objects from the region with more heterogeneity. So, the area and spectral characteristic can be used in classification. The results show a better accuracy. The selection and integrative utilization of features of objects were very important in achieving these high accuracies.

Terrestrial net primary production (NPP), as an important component of carbon cycle on land, not only indicates directly the production level of vegetation community on land, but also shows the status of terrestrial ecosystem. What's more, NPP is also a determinant of carbon sinks on land and a key regulator of ecological processes, including interactions among tropic levels. In the study, three existing models are combined with each other to assess net primary production in Haihe Basin, China. The photosynthetically active radiation (PAR) model of Monteith is used for the calculation of absorbed photosynthetically active radiation (APAR), the light utilization efficiency model of Potter et al. is used for determining the light utilization efficiency, and the surface energy balance system (SEBS) of Su is used into Potter's model to describe water stress in land wetness conditions. To assess NPP, We use NOAA-AVHRR data from November 2003 to September 2004 and the corresponding daily data of temperature and hours of sunshine obtained from meteorological stations in Haihe Basin, China. After atmospheric, geometrical and radiant corrections, every ten days NOAA data are processed to become an image of NDVI by means of the maximal value composition method (MVC) in order to eliminate some noises. Using these data, we compute NPP in spring season and spring season of 2004 in Haihe Basin, China. The result shows, in Haihe Basin, NPP for spring season is averaged to 336.10gC•m -2 , and 709.16 gC•m -2 for autumn season. In spatial distribution, NPP is greater in both ends than in middle for spring season, and decrease increasingly from north to south for autumn season. Future work should rely on the integration of high and low resolution images to assess net primary production, which will probably have more accurately estimation.

Rice acreage estimation is a key aspect of assessing rice production. A method of estimating rice acreage at the county level is explored, using data from the HJ-1A/B Chinese Environmental Satellite for Hunan Province, which has complex rice cropping patterns. The method combines supervised and unsupervised classification using a mixed-pixel decomposition model. The rice acreage estimation results were validated by interpretation of RapidEye images for early-season rice and ground survey data for medium-season and late-season rice. The results showed a good correlation between the estimates derived from RapidEye and from HJ CCD data for pure rice pixels (R 2 =0.99 , 0.99, and 0.97 for early-, medium-, and late-season rice). The discrepancy was

Crop identification and acreage estimation with remote sensing were the main issues for crop production estimation. Object-oriented classification has been involved in crop extraction from high spatial resolution images. However, different imagery segmentation scales for object-oriented classification always yield quite different crop identification accuracy. In this paper, multi-scale image segmentation was conducted to carry out crop identification using HJ CCD imagery in Red Star Farm in Heilongjiang province. Corn, soybean and wheat were identified as the final crop classes. Crop identification features at different segmentation scale were generated. Crop separability based on different feature-combinations was evaluated using class separation distance. Nearest Neighbour classifier (NN) was then used for crop identification. The results showed that the best segmentation scale was 8, and the overall crop identification accuracy was about 0.969 at that scale.

The HJ satellite constellation is designed for environment and disaster monitoring by the Chinese government. This paper investigates the performance of multitemporal multispectral charge-coupled device (CCD) data on board HJ-1-A and HJ-1-B for crop classification in the North China Plain. Support vector machine classifier is selected for the classification using different combinations of multitemporal HJ multispectral data. The results indicate that multitemporal HJ CCD data could effectively identify wheat fields with an overall classification accuracy of 91.7%. Considering only single temporal data, 88.2% is the best classification accuracy achieved using the data acquired at the flowering time of wheat. The performance of the combination of two temporal data acquired at the jointing and flowering times of wheat is almost as well as using all three temporal data, indicating that two appropriate temporal data are enough for wheat classification, and much more data have little effect on improving the classification accuracy. Moreover, two temporal data acquired over a larger time interval achieves better results than that over a smaller interval. However, the field borders and smaller cotton fields cannot be identified effectively by HJ multispectral data, and misclassification phenomenon exists because of the relatively coarse spatial resolution.