Satellite observed dryland greening in Asian endorheic basins: Drivers and implications to sustainable development.

A large portion of Central-Western Asia is made up of contiguous closed basins, collectively termed as the Asian Endorheic Basins (AEBs). As these retention basins are only being replenished by the intermittent and scarce rainfall, global warming coupled with ever-rising human demand for water is exerting unprecedented pressures on local water and ecological security. Recent studies revealed a persistent and widespread water storage decline across the AEBs, yet the response of dryland vegetation to this recent hydroclimatic trend and a spatially explicit partitioning of the impact into the hydroclimatic factors and human activities remain largely unknown. To fill in this knowledge gap, we conducted trend and partial correlation analysis of vegetation and hydroclimatic change from 2001 to 2021 using multi-satellite observations, including vegetation greenness, total water storage anomalies (TWSA) and meteorological data. Here we show that much of the AEB (65.53 %), encompassing Mongolia Plateau, Northwest China, Qinghai Tibet Plateau, and Western Asia (except the Arabian Peninsula), exhibited a significant greening trend over the past two decades. In arid AEB, precipitation dominated the vegetation productivity trend. Such a rainfall dominance gave way to TWSA dominance in the hyper-arid AEB. We further showed that the decoupling of rainfall and hyper-arid vegetation greening was largely due to a significant expansion (17.3 %) in irrigated cropland across the hyper-arid AEB. Given the extremely harsh environment in the AEB, our results therefore raised a significant concern on the ecological and societal sustainability in this region, where a mild increase in precipitation cannot catch up the rising evaporative demand and water consumption resulted from global warming and agriculture intensification.

A general and simple automated impervious surface mapping approach based on three-dimensional texture features (3DTF) using fine spatial resolution remotely sensed imagery.

The mapping of impervious surfaces using remote sensing techniques offer essential technical support for sustainable development objectives and safeguard the environment. In this study, we developed an automated method without training samples for mapping impervious surfaces using texture features. The different aggregated impervious surface patterns and distributions in study areas of Site A-C in China (Beijing, Huainan, Jinhua) were considered. The Site D-E in Dubai and Tehran, surrounded with deserts in arid areas. They were selected to develop and evaluate the performance of the proposed automated method. The texture features of the Contrast, Gabor wavelets, and secondary texture extraction (Con_Gabor) derived from Sentinel-2 images at each site were used to construct the three-dimensional texture features (3DTF) of impervious surfaces. The 3DTF-combined K-means classifier was used to automatically map the impervious surfaces. The results showed that the overall accuracies of mapping impervious surface were 91.15 %, 89.75 %, and 91.90 % in Site A-C. The overall accuracies of mapping impervious surface were 90.95 %, 91.45 % and 88.23 % in rural areas. The distributions of impervious surface on automated method, GHS-BUILT-S and ESA WorldCover were similar in study areas. The automated method for mapping impervious surfaces performed as well as the artificial neural network (ANN) and Random Forest (RF), and the advantage of not requiring training samples. The automated method was tested in the in Dubai and Tehran. The overall accuracies of the automatic method for mapping impervious surfaces >89 % at Site D-E, and >88 % at rural area. In addition, the 3DTF was proved as the simplest and most effective feature combination to map impervious surfaces. The impervious surface mapped using the automated method was robust across bands, seasons and sensors. However, further evaluation is necessary to assess the effectiveness of automated methods using high spatial resolution images for mapping impervious surface in complex areas.

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index.

Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.

Drought evaluation using the GRACE terrestrial water storage deficit over the Yangtze River Basin, China.

Droughts are some of the worst natural disasters that bring significant water shortages, economic losses, and adverse social consequences. Gravity Recovery and Climate Experiment (GRACE) satellite data are widely used to characterize and evaluate droughts. In this work, we evaluate drought situations in the Yangtze River Basin (YRB) using the GRACE Texas Center for Space Research (CSR) mascon (mass concentration) data from 2003 to 2015. Drought events are identified by water storage deficits (WSDs) derived from GRACE data, while the drought severity evaluation is based on the water storage deficit index (WSDI), standardized WSD time series, and total water storage deficit (TWSD). The WSDI is subsequently compared with the Palmer drought severity index (PDSI), standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized runoff index (SRI). The results indicate the YRB experienced increased wetness during the study period, with WSD values increasing at a rate of 5.20mm/year. Eight drought events are identified, and three major droughts occurred in 2004, 2006, and 2011, with WSDIs of -2.05, -2.38, and -1.30 and TWSDs of -620.96mm, -616.81mm, and -192.44mm, respectively. Our findings suggest that GRACE CSR mascon data can be used effectively to assess drought features in the YRB and that the WSDI facilitates robust and reliable characterization of droughts over large-scale areas.

Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set.

This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM) method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C) and kernel width (s), in mapping homogeneous specific land cover.

An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions.

Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven't been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value < 0.001); Root Mean Square Error (RMSE) values were 0.0245, 0.0300, 0.0401, respectively; and ERGAS values were 0.5416, 0.6507, 0.8737, respectively. The USTARM showed consistently higher performance than STARM when the degree of heterogeneity ranged from 2 to 10, highlighting that the use of this method provides the capacity to solve the data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation.

Change in the Green-Up Dates for Quercus mongolica in Northeast China and Its Climate-Driven Mechanism from 1962 to 2012.

The currently available studies on the green-up date were mainly based on ground observations and/or satellite data, and few model simulations integrated with wide coverage satellite data have been reported at large scale over a long time period (i.e., > 30 years). In this study, we combined phenology mechanism model, long-term climate data and synoptic scale remote sensing data to investigate the change in the green-up dates for Quercus mongolica over 33 weather stations in Northeast China and its climate-driven mechanism during 1962-2012. The results indicated that the unified phenology model can be well parameterized with the satellite derived green-up dates. The optimal daily mean temperature for chilling effect was between -27°C and 1°C for Q. mongolica in Northeast China, while the optimal daily mean temperature for forcing effect was above -3°C. The green-up dates for Q. mongolica across Northeast China showed a delayed latitudinal gradient of 2.699 days degree-1, with the earliest date on the Julian day 93 (i.e., 3th April) in the south and the latest date on the Julian day 129 (i.e., 9th May) in the north. The green-up date for Q. mongolica in Northeast China has advanced 6.6 days (1.3 days decade-1) from 1962 to 2012. With the prevailing warming in autumn, winter and spring in Northeast China during the past 51 years, the chilling effect for Q. mongolica has been weakened, while the forcing effect has been enhanced. The advancing trend in the green-up dates for Q. mongolica implied that the enhanced forcing effect to accelerate green-up was stronger than the weakened chilling effect to hold back green-up while the changes of both effects were caused by the warming climate.

[Spatiotemporal variation patterns of potential evapotranspiration in five provinces of Northwest China in 1960-2011].

By using Penman-Monteith model and Hurst index model, this paper analyzed the spatiotemporal variation patterns of potential evapotranspiration (ET0) in the five provinces of Northwest China in 1960-2011. In the meantime, the dominant factors driving the variations of the ET0 were quantitatively analyzed by using sensitivity analysis method. In 1960-2011, the ET0 in the five provinces presented an overall decreasing trend, with a drop rate of -0.72 mm x a(-1), but the ET0 increased gradually after 1993. An obvious spatial difference was shown in the annual average ET0. The average ET0 in the five provinces was 1158 mm (675-2282 mm), wit the maximum (2282 mm) in Qijiaojing of Xinjiang and the low values (>800 mm) in Qinba Mountains in south Shaanxi. Except in spring, the ET0 in other seasons showed a decreasing trend. In the analysis of future trend, the ET0 in most areas (81.4%) of Northwest China would present a trend from decrease to increase. Therefore, under the background of global warming, the warm and wet degree in Northwest China would be somewhat weakened, but the ET0 in the middle part of Xinjiang would be decreased continuously. Wind speed was the main factor affecting the ET0 in Northwest China at both annual and monthly scales, but the affecting extent of wind speed differed with seasons and areas. The spatial extent affected by the wind speed in winter expanded across the entire five provinces of Northwest China, while the spatial extent affected by the wind speed in summer included the entire Xinjiang and the northwest of Gansu and Qinghai.

[Division of winter wheat yield estimation by remote sensing based on MODIS EVI time series data and spectral angle clustering].

Crop yield estimation division is the basis of crop yield estimation; it provides an important scientific basis for estimation research and practice. In the paper, MODIS EVI time-series data during winter wheat growth period is selected as the division data; JiangSu province is study area; A division method combined of advanced spectral angle mapping(SVM) and K-means clustering is presented, and tested in winter wheat yield estimation by remote sensing. The results show that: division method of spectral angle clustering can take full advantage of crop growth process that is reflected by MODIS time series data, and can fully reflect region differences of winter wheat that is brought by climate difference. Compared with the traditional division method, yield estimation result based on division result of spectral angle clustering has higher R2 (0.702 6 than 0.624 8) and lower RMSE (343.34 than 381.34 kg x hm(-2)), reflecting the advantages of the new division method in the winter wheat yield estimation. The division method in the paper only use convenient obtaining time-series remote sensing data of low-resolution as division data, can divide winter wheat into similar and well characterized region, accuracy and stability of yield estimation model is also very good, which provides an efficient way for winter wheat estimation by remote sensing, and is conducive to winter wheat yield estimation.

[Integration of soft and hard classifications using linear spectral mixture model and support vector machines].

This paper presents a new soft and hard classification. By analyzing the target objects in the image distribution, and calculating the adaptive threshold automatically, the image is divided into three regions: pure regions, non-target objects regions and mixed regions. For pure regions and non-target objects regions, hard classification method (support vector machine) is used to quickly extract classified results; For mixed regions, soft classification method (selective endmember for linear spectral mixture model) is used to extract the abundance of target objects. Finally, it generates an integrated soft and hard classification map. In order to evaluate the accuracy of this new method, it is compared with SVM and LSMM using ALOS image. The RMSE value of new method is 0.203, and total accuracy is 95.48%. Both overall accuracies and RMSE show that integration of hard and soft classification has a higher accuracy than single hard or soft classification. Experimental results prove that the new method can effectively solve the problem of mixed pixels, and can obviously improve image classification accuracy.

[Farmland parcel extraction based on high resolution remote sensing image].

A new method of farmland parcel extraction from high resolution remote sensing image based on wavelet and watershed segmentation was proposed in the present paper. First, classification results were used to enhance the contrast of gray-scale value of typical pixels in the original image using the high resolution remote sensing image's spectral information. Second, wavelet transform and watershed segmentation were applied to the enhanced image, then improved region merger algorithm was used to solve the problem of over-segmentation. Finally, inverse wavelet transform was taken to get the reconstructed image, then Canny operator was introduced to add the edge information, and the result of farmland parcel segmentation was obtained. To validate the proposed approach, experiments on Quickbird images were performed, we rapidly extracted the farmland parcel from the test image, and the results had a high accuracy. Despite it had a lot to do in extracting the small size parcels, on the whole the method this paper proposed had a very good robustness. Compared with the traditional methods, it had the following advantages: (1) it was an automatic extraction method, did not need too much manual intervention, and could extract the large area of farmland parcels accurately and effectively. (2) It was a very good solution to the problem of over-segmentation by using improved region merger algorithm, and improved the accuracy of the extraction. All these indicated that the proposed approach was an effective farmland parcel extraction method based on high resolution remote sensing image.

[Establish remote of sensing monitoring method and standard on wild rare medical plant].

At present the shortage of the resources of wild and rare medicinal plants is very serious in China. So grasping the situation and change tendency of medicinal plant resources comprehensive and real-timely, is vital significance to realize the sustainable using of medicinal plant resources. In order to use the remote sensing monitor technology and method to study the resources of the wild and rare medicinal plants, this article discusses the principle, method, technology and the principle and standard based on the operation and experiment of remote sensing monitor on the resources of wild rare medicinal plant.

[Estimation of vegetation coverage based on an improved sub-pixel model].

Vegetation coverage is an important parameter in terrestrial ecological process, meteorological, and climatic models. By eliminating the errors from the precision of image classification and the noises of remote sensing images, and by using the actual data from fieldwork, this paper determined the maximum and minimum values of normalized difference vegetation index (NDVI), improved the sub-pixel model, and verified this model by calculating the vegetation coverage of Beijing. The results showed that the estimation value of the improved model was very close to the measurements, especially for the herbaceous plants whose vegetation types were the same but the densities were different. However, the estimation error of arborous vegetation coverage was relatively large, probably due to the effects of remote sensing image resolution, vegetation fragmentation, and mixed pixel model.

[Biomass structure analysis of Atractylodes lancea in different ecological environments].

OBJECTIVE: To study the ecological environments of Atractylodes lancea by biomass structural analysis. METHOD: Through the scientific investigation in Maoshan, the sampling spots were set up, the relation between growth and ecological environments was researched and the ecological environments of A. lancea were divided as following: the vegetation layer, the shrub layer, the shrub-weed layer and the weed layer. The ramet biomass, height, leaves and coverage of A. lancea were studied. RESULT: The several factors (ramet biomass, height, leaves and coverage) showed the regular change. Among maximum, minimum and average, the shrub layer was the biggest, the shrub and weed layer was the second biggest and the vegetation layer and the weed layer was the least. CONCLUSION: A. lancea tends to distribute in the shrub layer and the shrub-weed layer.

[Analysis in distribution of Atractylodes lancea at Maoshan regions].

OBJECTIVE: To Investigate the distribution of Atractylodes lancea at Maoshan regions. METHODS: To combine the plot sampling with GIS technology in the analysis of distribution and its factors. RESULTS: The biomass of Atractylodes lancea was related to the growth of Ouercus serrata var. brevipetiolata, slope and humidity. The distribution of Atractylodes lancea which was less in north Maoshen region, most in south region, least in middle region. CONCLUSION: The main factor of distributing sintuation is the human beings. The leading factors in the biomass of Atractylodes lancea are Ouercus serrata var. brevipetiolata and slope.

[Comparing of different methods on habitat adaptive division of Chinese material medica].

OBJECTIVE: To comparing two kinds habitat adaptive division of Chinese material medica with different models. METHOD: The habitat adaptive divisions of A. lancea according essential oil accumulation with two kinds pattern, model pattern and template pattern were carrid and compared. RESULT: Two habitat adaptive divisions of A. lancea maps according essential oil accumulation were gotten. CONCLUSION: Both model pattern and template pattern were efficient on habitat adaptive division of Chinese material medica, but they shoud bu used with different processesd and based different background [corrected]

[Remote sensing resource monitoring on Atractylodes lancea].

Remote sensing technology was used for investigation of the resources of Atractylodes lancea. Firstly, the general situation of Jiangshu Maoshan and A. lancea in Maoshan was introduced; Secondly, the methods of remote sensing on the resource of the wild drugs were explained. Thirdly, the TM images were interpret according to the differences of the objects reflex spectrum, and growth environments in Damao mountain, Ermao mountain and Xiaomao mountain were divided into different sub-areas according to the results of the field investigations. Finally, the resource of A. lancea in Jiangshu Maoshan was estimated.

[Study on dynamical changes of Ginkgo biloba resources in Pizhou city of Jiangsu Province base on RS and GIS].

OBJECTIVE: To analyse the dynamical changes of the Ginkgo biloba's resources from 2001 to 2006, in Pizhou city, Jiangshu province by useing spatial analytical function of GIS and RS technology. METHOD: Use the GIS and RS technology, extracted the information of G. biloba by scientific investigation, researched the spatial distribution and dynamical changes of G. biloba based on landsat 5 TM: the Apr. 3rd, 2001; Jan. 16th, 2005; July 30th, 2006. RESULT: Ginkgo biloba's resource was 1.61 x 10(5) hm2 in 2001, 1.84 x 10(5) hm2 in 2005, 1.88 x 10(5) hm2 in 2006. CONCLUSION: Ginkgo biloba's resource rised from 1.61 x 10(5) hm2 to 1.88 x 10(5) hm2 from 2001 to 2006, showed the gradually rise.

[Remote sensing monitoring methods for medicinal plants resource in different types of ecological environment].

Different remote sensing monitoring methods are needed for the medicinal plant resource in different types of ecological environment. This paper explained remote sensing monitoring methods for the resource of the wild medicinal plants and cultivated plants, and analyzed the rare species, generous species and species in special ecological environment in detail. It provides a new method to all kind of medicinal plants resources' remote sensing monitoring.

[Application of multivariate statistical methods in research of Chinese medicinal materials resource ecology].

In this paper the application of multivariate statistical methods in research of Chinese medicinal materials resource ecology was introduced. The importance of multivariate statistical analysis used in research on analyzing ecological environment factors and region suitability of Chinese crude drugs has been pointed out. Several frequent used multivariate statistical methods have been presented.