Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture.However,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead t...Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture.However,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead to inconsistent rice phenology,which had a significant impact on yield prediction of ratoon rice.Multi-temporal unmanned aerial vehicle(UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods.Thus,in this study,we explored the performance of combination of agronomic practice information(API)and single-phase,multi-spectral features[vegetation indices(VIs)and texture(Tex)features]in predicting ratoon rice yield,and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice.The results showed that the integrated use of VIs,Tex and API(VIs&Tex+API)improved the accuracy of yield prediction than single-phase UAV imagery-based feature,with the panicle initiation stage being the best period for yield prediction(R^(2) as 0.732,RMSE as 0.406,RRMSE as 0.101).More importantly,compared with previous multi-temporal UAV-based methods,our proposed multi-temporal method(multi-temporal model VIs&Tex:R^(2) as 0.795,RMSE as 0.298,RRMSE as 0.072)can increase R^(2) by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield forecasting.This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture,which is of great significance to take timely means for ensuring ratoon rice production and food security.展开更多
As a consumed and influential natural plant beverage,tea is widely planted in subtropical and tropical areas all over the world.Affected by(sub)tropical climate characteristics,the underlying surface of the tea distri...As a consumed and influential natural plant beverage,tea is widely planted in subtropical and tropical areas all over the world.Affected by(sub)tropical climate characteristics,the underlying surface of the tea distribution area is extremely complex,with a variety of vegetation types.In addition,tea distribution is scattered and fragmentized in most of China.Therefore,it is difficult to obtain accurate tea information based on coarse resolution remote sensing data and existing feature extraction methods.This study proposed a boundary-enhanced,object-oriented random forest method on the basis of high-resolution GF-2 and multi-temporal Sentinel-2 data.This method uses multispectral indexes,textures,vegetable indices,and variation characteristics of time-series NDVI from the multi-temporal Sentinel-2 imageries to obtain abundant features related to the growth of tea plantations.To reduce feature redundancy and computation time,the feature elimination algorithm based on Mean Decrease Accuracy(MDA)was used to generate the optimal feature set.Considering the serious boundary inconsistency problem caused by the complex and fragmented land cover types,high resolution GF-2 image was segmented based on the MultiResolution Segmentation(MRS)algorithm to assist the segmentation of Sentinel-2,which contributes to delineating meaningful objects and enhancing the reliability of the boundary for tea plantations.Finally,the object-oriented random forest method was utilized to extract the tea information based on the optimal feature combination in the Jingmai Mountain,Yunnan Province.The resulting tea plantation map had high accuracy,with a 95.38%overall accuracy and 0.91 kappa coefficient.We conclude that the proposed method is effective for mapping tea plantations in high heterogeneity mountainous areas and has the potential for mapping tea plantations in large areas.展开更多
Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperatur...Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC (grain protein content) estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.展开更多
Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. M...Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. Much research has been carried out to apply MT-InSAR to monitor ground and infrastructure deformation in urban areas related to land reclamation, underground construction and groundwater extraction.This paper reviews the progress in the research and identifies challenges in applying the technology, including the inconsistency in coherent point identification when different approaches are used, the reliability issue in parameter estimation, difficulty in accurate geolocation of measured points, the one-dimensional line-of-sight nature of InSAR measurements, the inability of making complete measurements over an area due to geometric distortions, especially the shadowing effects, the challenges in processing large SAR datasets, the decrease of the number of coherent points with the increase of the length of SAR time series, and the difficulty in quality control of MT-InSAR results.展开更多
Land subsidence is a major factor that affects metro line (ML) stability. In this study, an improved multi- temporal interferometric synthetic aperture radar (InSAR) (MTI) method to detect land subsidence near M...Land subsidence is a major factor that affects metro line (ML) stability. In this study, an improved multi- temporal interferometric synthetic aperture radar (InSAR) (MTI) method to detect land subsidence near MLs is presented. In particular, our multi-temporal InSAR method provides surface subsidence measurements with high observation density. The MTI method tracks both point-like targets and distributed targets with temporal radar back- scattering steadiness. First, subsidence rates at the point targets with low-amplitude dispersion index (ADI) values are extracted by applying a least-squared estimator on an optimized freely connected network. Second, to reduce error propagation, the pixels with high-ADI values are classified into several groups according to ADI intervals and processed using a Pearson correlation coefficient and hierarchical analysis strategy to obtain the distributed targets. Then, nonlinear subsidence components at all point-like and distributed targets are estimated using phase unwrapping and spatiotemporal filtering on the phase residuals. The proposed MTI method was applied to detect land subsidence near MLs of No. 1 and 3 in the Baoshan district of Shanghai using 18 TerraSAR-X images acquired between April 21, 2008 and October 30, 2010. The results show that the mean subsidence rates of the stations distributed along the two MLs are -12.9 and -14.0 ram/year. Furthermore, three subsidence funnels near the MLs are discovered through the hierarchical analysis. The testing results demonstrate the satisfactory capacity of the proposed MTI method in providing detailed subsidence information near MLs.展开更多
Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background...Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.展开更多
This study conducted computer-aided image analysis of land use and land cover in Xilin River Basin, Inner Mongolia, using 4 sets of Landsat TM/ETM+ images acquired on July 31, 1987, August 11, 1991, Sep...This study conducted computer-aided image analysis of land use and land cover in Xilin River Basin, Inner Mongolia, using 4 sets of Landsat TM/ETM+ images acquired on July 31, 1987, August 11, 1991, September 27, 1997 and May 23, 2000, respectively. Primarily, 17 sub-class land cover types were recognized, including nine grassland types at community level: F.sibiricum steppe, S.baicalensis steppe, A.chinensis+ forbs steppe, A.chinensis+ bunchgrass steppe, A.chinensis+ Ar.frigida steppe, S.grandis+ A.chinensis steppe, S.grandis+ bunchgrass steppe, S.krylavii steppe, Ar.frigida steppe and eight non-grassland types: active cropland, harvested cropland, urban area, wetland, desertified land, saline and alkaline land, cloud, water body + cloud shadow. To eliminate the classification error existing among different sub-types of the same gross type, the 17 sub-class land cover types were grouped into five gross types: meadow grassland, temperate grassland, desert grassland, cropland and non-grassland. The overall classification accuracy of the five land cover types was 81.0% for 1987, 81.7% for 1991, 80.1% for 1997 and 78.2% for 2000.展开更多
Satellite images are considered reliable data that preserve land cover information. In the field of remote sensing, these images allow relevant analyses of changes in space over time through the use of computer tools....Satellite images are considered reliable data that preserve land cover information. In the field of remote sensing, these images allow relevant analyses of changes in space over time through the use of computer tools. In this study, we have applied the “discriminant” change detection algorithm. In this, we have verified its effectiveness in multi-temporal studies. Also, we have determined the change in forest dynamics in the Ikongo district of Madagascar between 2000 and 2015. During the treatments, we have used the Landsat TM satellite images for the years 2000, 2005 and 2010 as well as ETM+ for 2015. Thus, analyses carried out have allowed us to note that between 2000-2005, 1.4% of natural forest disappeared. And, between 2005-2010, forests degradation<span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">was 1.8%. Also, between 2010-2015, about 0.5% of the natural forest conserved in 2010 disappeared. Furthermore, we have found that the discriminant algorithm is considerably efficient in terms of monitoring the dynamics of forest cover change.</span></span></span>展开更多
As landmass of the world is covered by vegetation, taking into account phenology when performing land cover classification may yield more accurate maps. The availability of no-cost Moderate Resolution Imaging Spectrom...As landmass of the world is covered by vegetation, taking into account phenology when performing land cover classification may yield more accurate maps. The availability of no-cost Moderate Resolution Imaging Spectrometer (MODIS) NDVI dataset that provides high-quality continuous time series data is representing a potentially significant source of land cover information especially for detection natural forest distribution. This study intends to assess the advantage of MODIS 250 m Normalized Difference Vegetation Index (NDVI) multi-temporal imagery for detection of densely vegetation cover distribution in Java and then for identification of remaining natural forest in Java from densely vegetation cover distribution. Result of this study successfully demonstrated the contribution of MODIS NDVI 250 m for detection the natural forest distribution in Java Island. Therefore, the approach described herein provided classification accuracy comparable to those of maps derived from higher resolution data and will be a viable alternative for regional or national classifications.展开更多
Constrained by complex imaging mechanism and extraordinary visual appearance,change detection with synthetic aperture radar(SAR)images has been a difficult research topic,especially in urban areas.Although existing st...Constrained by complex imaging mechanism and extraordinary visual appearance,change detection with synthetic aperture radar(SAR)images has been a difficult research topic,especially in urban areas.Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information,there are still two problems to be solved in practical applications.First,change indicators constructed from incoherent feature only cannot characterize the change objects accurately.Second,the results of pixel-level methods are usually presented in the form of the noisy binary map,making the spatial change not intuitive and the temporal change of a single pixel meaningless.In this study,we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images.The coefficients of variation in timeseries incoherent features and the man-made object index(MOI)defined with coherent features are first combined to identify the initial change pixels.Afterwards,an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise(DBSCAN)and dynamic time warping(DTW),which can transform the initial results into noiseless object-level patches,and take the cluster center as a representative of the man-made object to determine the change pattern of each patch.An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.展开更多
Multi-temporal synthetic aperture radar interferometry(MT-InSAR)is a standard technique for mapping clustering and wide-scale deformation.A linear model is often used in phase unwrapping to overcome the underdetermina...Multi-temporal synthetic aperture radar interferometry(MT-InSAR)is a standard technique for mapping clustering and wide-scale deformation.A linear model is often used in phase unwrapping to overcome the underdetermination.It’s difficult to identify different types of nonlinear deformation.However,the interpretation of nonlinear deformation is very important in monitoring potential risk.This paper introduces a comprehensive approach for identifying and interpreting different types of deformation within InSAR datasets,integrating initial clustering and classification simplification.Initial classification is performed using the K-means clustering method to cluster the collected InSAR deformation time-series data.Then we use F test and Anderson-Darling test(AD test)to simplify the clusters after initial classification.This technique distinctly discerns the changing trends of deformation signals,thereby providing robust support for interpreting potential deformation scenarios within observed InSAR regions.展开更多
High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection...High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.展开更多
Background:The NASA’s Global Ecosystem Dynamics Investigation(GEDI)satellite mission aims at scanning forest ecosystems on a multi-temporal short-rotation basis.The GEDI data can validate and update statistics from n...Background:The NASA’s Global Ecosystem Dynamics Investigation(GEDI)satellite mission aims at scanning forest ecosystems on a multi-temporal short-rotation basis.The GEDI data can validate and update statistics from nationwide airborne laser scanning(ALS).We present a case in the Northwest of Spain using GEDI statistics and nationwide ALS surveys to estimate forest dynamics in three fast-growing forest ecosystems comprising 211,346 ha.The objectives were:i)to analyze the potential of GEDI to detect disturbances,ii)to investigate uncertainty source regarding non-positive height increments from the 2015–2017 ALS data to the 2019 GEDI laser shots and iii)to estimate height growth using polygons from the Forest Map of Spain(FMS).A set of 258 National Forest Inventory plots were used to validate the observed height dynamics.Results:The spatio-temporal assessment from ALS surveying to GEDI scanning allowed the large-scale detection of harvests.The mean annual height growths were 0.79(SD=0.63),0.60(SD=0.42)and 0.94(SD=0.75)m for Pinus pinaster,Pinus radiata and Eucalyptus spp.,respectively.The median annual values from the ALS-GEDI positive increments were close to NFI-based growth values computed for Pinus pinaster and Pinus radiata,respectively.The effect of edge border,spatial co-registration of GEDI shots and the influence of forest cover in the observed dynamics were important factors to considering when processing ALS data and GEDI shots.Discussion:The use of GEDI laser data provides valuable insights for forest industry operations especially when accounting for fast changes.However,errors derived from positioning,ground finder and canopy structure can introduce uncertainty to understand the detected growth patterns as documented in this study.The analysis of forest growth using ALS and GEDI would benefit from the generalization of common rules and data processing schemes as the GEDI mission is increasingly being utilized in the forest remote sensing community.展开更多
Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cyc...Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.展开更多
Landuse and land cover change is regarded as a good indicator that represents the impact of human activities on earth’s environment.When the large collection of multi-temporal satellite images has become available,it...Landuse and land cover change is regarded as a good indicator that represents the impact of human activities on earth’s environment.When the large collection of multi-temporal satellite images has become available,it is possible to study a long-term historical process of land cover change.This study aims to investigate the spatio-temporal pattern and driving force of land cover change in the Pearl River Delta region in southern China,where the rapid development has been witnessed since 1980s.The fast economic growth has been associated with an accelerated expansion of urban landuse,which has been recorded by historical remote sensing images.This paper reports the method and outcome of the research that attempts to model spatio-temporal pattern of land cover change using multi-temporal satellite images.The classified satellite images were compared to detect the change from various landuse types to built-up areas.The trajectories of land cover change have then been established based on the time-series of the classified land cover classes.The correlation between the expansion of built-up areas and selected economic data has also been analysed for better understanding on the driving force of the rapid urbanisation process.The result shows that,since early 1990s,the dominant trend of land cover change has been from farmland to urban landuse.The relationship between economic growth indicator(measured by GDP)and built-up area can well fit into a linear regression model with correlation coefficients greater than 0.9.It is quite clear that cities or towns have been sprawling in general,demonstrating two growth models that were closely related to the economic development stages.展开更多
Fengyun-4 A(FY-4 A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme...Fengyun-4 A(FY-4 A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme weather such as sudden flood disasters. In this study, the flood disasters occurred in Bangladesh, India, and some other areas of South Asia in August 2018 were investigated by using a rapid multi-temporal synthesis approach for the first time for removal of thick clouds in FY-4 A images. The maximum between-class variance algorithm(OTSU;developed by Otsu in 2007) and linear spectral unmixing methods are used to extract the water area of flood disasters. The accuracy verification shows that the water area of flood disasters extracted from FY-4 A is highly correlated with that from the high-resolution satellite datasets Gaofen-1(GF-1) and Sentinel-1 A, with the square correlation coefficient R2 reaching 0.9966. The average extraction accuracy of FY-4 A is over 90%. With the rapid multi-temporal synthesis approach used in flood disaster monitoring with FY-4 A satellite data, advantages of the wide coverage, fast acquisition,and strong timeliness with geostationary meteorological satellites are effectively combined. Through the synthesis of multi-temporal images of the flood water body, the influence of clouds is effectively eliminated, which is of great significance for the real-time flood monitoring. This also provides an important service guarantee for the disaster prevention and reduction as well as economic and social development in China and the Asia-Pacific region.展开更多
Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral mixture.Especially for medium-resolution remote sensing images use...Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral mixture.Especially for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution.Thus,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change detection.In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion.Nonlinear spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences.The proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas.The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(i.e.change vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.展开更多
Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great ...Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise CNN(79.18%and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes(e.g., forest landscapes), with a wide application prospect.展开更多
Most forest fires in the Margalla Hills are related to human activities and socioeconomic factors are essential to assess their likelihood of occurrence.This study considers both environmental(altitude,precipitation,f...Most forest fires in the Margalla Hills are related to human activities and socioeconomic factors are essential to assess their likelihood of occurrence.This study considers both environmental(altitude,precipitation,forest type,terrain and humidity index)and socioeconomic(population density,distance from roads and urban areas)factors to analyze how human behavior affects the risk of forest fires.Maximum entropy(Maxent)modelling and random forest(RF)machine learning methods were used to predict the probability and spatial diffusion patterns of forest fires in the Margalla Hills.The receiver operating characteristic(ROC)curve and the area under the ROC curve(AUC)were used to compare the models.We studied the fire history from 1990 to 2019 to establish the relationship between the probability of forest fire and environmental and socioeconomic changes.Using Maxent,the AUC fire probability values for the 1999 s,2009 s,and 2019 s were 0.532,0.569,and 0.518,respectively;using RF,they were 0.782,0.825,and 0.789,respectively.Fires were mainly distributed in urban areas and their probability of occurrence was related to accessibility and human behaviour/activity.AUC principles for validation were greater in the random forest models than in the Maxent models.Our results can be used to establish preventive measures to reduce risks of forest fires by considering socio-economic and environmental conditions.展开更多
Background: The Shelduck (Tadorna tadorna) is a characteristic waterbird species of inland wetlands in northeastern Algeria. Its wintering behavior in relation to changes of local abundances and foraging group density...Background: The Shelduck (Tadorna tadorna) is a characteristic waterbird species of inland wetlands in northeastern Algeria. Its wintering behavior in relation to changes of local abundances and foraging group density is poorly known. Objectives: This study aims at monitoring patterns of diurnal activities and the variation of behavioral time-budgets in relation to numbers of wintering Shelducks. We investigate temporal variations of diurnal activities across multipletime scales and consider their interrelationships. Methods: Assessments of local population abundance were weekly surveyed during two wintering seasons (2010– 2012), whereas diurnal activities (feeding, sleeping, swimming, preening, loafing, flying, courtship, and antagonism) were studied three times a month during seven hours (08:00–16:00) using the Scan method. Time budget variations of each behavioral activity were tested using nested ANOVAs following multiple time scales. Generalized linear mixedeffects models (GLMM) tested whether variations in diurnal activities were density-dependent. Results: During the wintering season, Shelduck’s numbers followed a bell-shaped trend, which indicated that the species was typically a wintering migrant in Sabkha Djendli. The first individuals arrived onsite in October–November then numbers reached a peak in January (up to 2400 individuals in 2012) with steady density during December–February, afterward individuals left the site progressively until late April when the site is deserted. During both wintering seasons, diurnal activities were dominated by feeding (60%), followed by sleeping (12%) then swimming and preening with 9% and 8%, respectively. The rest of the activities (loafing, flying, courtship and antagonistic behaviors) had low proportions of time budget. ANOVAs showed that activity time budgets varied significantly following multiple time scales (year, season, month, day, semi-hour). Time budgets of diurnal activities during each wintering season were significantly interrelated. Correlations patterns between the two seasons were similar. GLMMs revealed that the variations of diurnal activities were not density-dependent, except for preening and swimming. Conclusion: During the wintering season, habitats of Sabkha Djendli are important for waterbirds, including the Shelduck that used the lake mainly for food-foraging and resting. The 2400 individuals censused in mid-winter are important locally and at the North African scale. This stresses the need to strengthen the protection status of this wetland and mitigate degradation sources that threaten wintering waterfowl.展开更多
基金supported by the Key Research and Development Program of Heilongjiang,China(Grant No.2022ZX01A25)Cooperative Funding between Huazhong Agricultural University and Shenzhen Institute of Agricultural Genomics(Grant No.SZYJY2022014)+2 种基金Fundamental Research Funds for the Central Universities,Beijing,China(Grant Nos.2662022JC006 and 2662022ZHYJ002)National Natural Science Foundation of China(Grant No.32101819)Huazhong Agriculture University Research Startup Fund,China(Grant Nos.11041810340 and 11041810341).
文摘Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture.However,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead to inconsistent rice phenology,which had a significant impact on yield prediction of ratoon rice.Multi-temporal unmanned aerial vehicle(UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods.Thus,in this study,we explored the performance of combination of agronomic practice information(API)and single-phase,multi-spectral features[vegetation indices(VIs)and texture(Tex)features]in predicting ratoon rice yield,and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice.The results showed that the integrated use of VIs,Tex and API(VIs&Tex+API)improved the accuracy of yield prediction than single-phase UAV imagery-based feature,with the panicle initiation stage being the best period for yield prediction(R^(2) as 0.732,RMSE as 0.406,RRMSE as 0.101).More importantly,compared with previous multi-temporal UAV-based methods,our proposed multi-temporal method(multi-temporal model VIs&Tex:R^(2) as 0.795,RMSE as 0.298,RRMSE as 0.072)can increase R^(2) by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield forecasting.This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture,which is of great significance to take timely means for ensuring ratoon rice production and food security.
基金National Natural Science Foundation of China(No.41830110)National Key Research Development Program of China(No.2018YFC1503603)+2 种基金Key Laboratory of Land Satellite Remote Sensing Application,Ministry of Natural Resources of the People’s Republic of China(No.KLSMNR-202106)Water Conservancy Science and Technology Project of Jiangsu Province,China(No.2020061)Natural Science Foundation of Jiangsu Province,China(No.20180779)。
文摘As a consumed and influential natural plant beverage,tea is widely planted in subtropical and tropical areas all over the world.Affected by(sub)tropical climate characteristics,the underlying surface of the tea distribution area is extremely complex,with a variety of vegetation types.In addition,tea distribution is scattered and fragmentized in most of China.Therefore,it is difficult to obtain accurate tea information based on coarse resolution remote sensing data and existing feature extraction methods.This study proposed a boundary-enhanced,object-oriented random forest method on the basis of high-resolution GF-2 and multi-temporal Sentinel-2 data.This method uses multispectral indexes,textures,vegetable indices,and variation characteristics of time-series NDVI from the multi-temporal Sentinel-2 imageries to obtain abundant features related to the growth of tea plantations.To reduce feature redundancy and computation time,the feature elimination algorithm based on Mean Decrease Accuracy(MDA)was used to generate the optimal feature set.Considering the serious boundary inconsistency problem caused by the complex and fragmented land cover types,high resolution GF-2 image was segmented based on the MultiResolution Segmentation(MRS)algorithm to assist the segmentation of Sentinel-2,which contributes to delineating meaningful objects and enhancing the reliability of the boundary for tea plantations.Finally,the object-oriented random forest method was utilized to extract the tea information based on the optimal feature combination in the Jingmai Mountain,Yunnan Province.The resulting tea plantation map had high accuracy,with a 95.38%overall accuracy and 0.91 kappa coefficient.We conclude that the proposed method is effective for mapping tea plantations in high heterogeneity mountainous areas and has the potential for mapping tea plantations in large areas.
基金the National Natural Science Foundation of China (41171281, 40701120)the Beijing Nova Program, China (2008B33)
文摘Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC (grain protein content) estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.
基金The National Natural Science Foundation of China(41774023)The Research Grants Council(RGC)of Hong Kong(PolyU152232/17E,PolyU152164/18E),The Faculty of Construction and Environment(ZZGD)+1 种基金The Research Institute for Sustainable Urban Development(RISUD)(1-BBWB)The TerraSAR-X Science plan(GEO3603)。
文摘Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR) is one of the most powerful Earth observation techniques, especially useful for measuring highly detailed ground deformation over large ground areas. Much research has been carried out to apply MT-InSAR to monitor ground and infrastructure deformation in urban areas related to land reclamation, underground construction and groundwater extraction.This paper reviews the progress in the research and identifies challenges in applying the technology, including the inconsistency in coherent point identification when different approaches are used, the reliability issue in parameter estimation, difficulty in accurate geolocation of measured points, the one-dimensional line-of-sight nature of InSAR measurements, the inability of making complete measurements over an area due to geometric distortions, especially the shadowing effects, the challenges in processing large SAR datasets, the decrease of the number of coherent points with the increase of the length of SAR time series, and the difficulty in quality control of MT-InSAR results.
文摘Land subsidence is a major factor that affects metro line (ML) stability. In this study, an improved multi- temporal interferometric synthetic aperture radar (InSAR) (MTI) method to detect land subsidence near MLs is presented. In particular, our multi-temporal InSAR method provides surface subsidence measurements with high observation density. The MTI method tracks both point-like targets and distributed targets with temporal radar back- scattering steadiness. First, subsidence rates at the point targets with low-amplitude dispersion index (ADI) values are extracted by applying a least-squared estimator on an optimized freely connected network. Second, to reduce error propagation, the pixels with high-ADI values are classified into several groups according to ADI intervals and processed using a Pearson correlation coefficient and hierarchical analysis strategy to obtain the distributed targets. Then, nonlinear subsidence components at all point-like and distributed targets are estimated using phase unwrapping and spatiotemporal filtering on the phase residuals. The proposed MTI method was applied to detect land subsidence near MLs of No. 1 and 3 in the Baoshan district of Shanghai using 18 TerraSAR-X images acquired between April 21, 2008 and October 30, 2010. The results show that the mean subsidence rates of the stations distributed along the two MLs are -12.9 and -14.0 ram/year. Furthermore, three subsidence funnels near the MLs are discovered through the hierarchical analysis. The testing results demonstrate the satisfactory capacity of the proposed MTI method in providing detailed subsidence information near MLs.
基金The Science and Technoloav Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2020-A11-02)is appreciated for supporting this study.
文摘Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.
基金Knowledge Innovation Project of CAS No.KZCX02-308+1 种基金 The NASA Land Use and Land Cover Change Program No.NAG5-11160
文摘This study conducted computer-aided image analysis of land use and land cover in Xilin River Basin, Inner Mongolia, using 4 sets of Landsat TM/ETM+ images acquired on July 31, 1987, August 11, 1991, September 27, 1997 and May 23, 2000, respectively. Primarily, 17 sub-class land cover types were recognized, including nine grassland types at community level: F.sibiricum steppe, S.baicalensis steppe, A.chinensis+ forbs steppe, A.chinensis+ bunchgrass steppe, A.chinensis+ Ar.frigida steppe, S.grandis+ A.chinensis steppe, S.grandis+ bunchgrass steppe, S.krylavii steppe, Ar.frigida steppe and eight non-grassland types: active cropland, harvested cropland, urban area, wetland, desertified land, saline and alkaline land, cloud, water body + cloud shadow. To eliminate the classification error existing among different sub-types of the same gross type, the 17 sub-class land cover types were grouped into five gross types: meadow grassland, temperate grassland, desert grassland, cropland and non-grassland. The overall classification accuracy of the five land cover types was 81.0% for 1987, 81.7% for 1991, 80.1% for 1997 and 78.2% for 2000.
文摘Satellite images are considered reliable data that preserve land cover information. In the field of remote sensing, these images allow relevant analyses of changes in space over time through the use of computer tools. In this study, we have applied the “discriminant” change detection algorithm. In this, we have verified its effectiveness in multi-temporal studies. Also, we have determined the change in forest dynamics in the Ikongo district of Madagascar between 2000 and 2015. During the treatments, we have used the Landsat TM satellite images for the years 2000, 2005 and 2010 as well as ETM+ for 2015. Thus, analyses carried out have allowed us to note that between 2000-2005, 1.4% of natural forest disappeared. And, between 2005-2010, forests degradation<span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">was 1.8%. Also, between 2010-2015, about 0.5% of the natural forest conserved in 2010 disappeared. Furthermore, we have found that the discriminant algorithm is considerably efficient in terms of monitoring the dynamics of forest cover change.</span></span></span>
文摘As landmass of the world is covered by vegetation, taking into account phenology when performing land cover classification may yield more accurate maps. The availability of no-cost Moderate Resolution Imaging Spectrometer (MODIS) NDVI dataset that provides high-quality continuous time series data is representing a potentially significant source of land cover information especially for detection natural forest distribution. This study intends to assess the advantage of MODIS 250 m Normalized Difference Vegetation Index (NDVI) multi-temporal imagery for detection of densely vegetation cover distribution in Java and then for identification of remaining natural forest in Java from densely vegetation cover distribution. Result of this study successfully demonstrated the contribution of MODIS NDVI 250 m for detection the natural forest distribution in Java Island. Therefore, the approach described herein provided classification accuracy comparable to those of maps derived from higher resolution data and will be a viable alternative for regional or national classifications.
基金supported by the National Natural Science Foundation of China(41774006)the Comparative Study of Geo-environment and Geohazards in the Yangtze River Delta and the Red River Delta Projectthe Shanghai Science and Technology Development Foundation(20dz1201200)。
文摘Constrained by complex imaging mechanism and extraordinary visual appearance,change detection with synthetic aperture radar(SAR)images has been a difficult research topic,especially in urban areas.Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information,there are still two problems to be solved in practical applications.First,change indicators constructed from incoherent feature only cannot characterize the change objects accurately.Second,the results of pixel-level methods are usually presented in the form of the noisy binary map,making the spatial change not intuitive and the temporal change of a single pixel meaningless.In this study,we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images.The coefficients of variation in timeseries incoherent features and the man-made object index(MOI)defined with coherent features are first combined to identify the initial change pixels.Afterwards,an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise(DBSCAN)and dynamic time warping(DTW),which can transform the initial results into noiseless object-level patches,and take the cluster center as a representative of the man-made object to determine the change pattern of each patch.An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.
基金supported in part by the National Natural Foundation of China(No.62201158).
文摘Multi-temporal synthetic aperture radar interferometry(MT-InSAR)is a standard technique for mapping clustering and wide-scale deformation.A linear model is often used in phase unwrapping to overcome the underdetermination.It’s difficult to identify different types of nonlinear deformation.However,the interpretation of nonlinear deformation is very important in monitoring potential risk.This paper introduces a comprehensive approach for identifying and interpreting different types of deformation within InSAR datasets,integrating initial clustering and classification simplification.Initial classification is performed using the K-means clustering method to cluster the collected InSAR deformation time-series data.Then we use F test and Anderson-Darling test(AD test)to simplify the clusters after initial classification.This technique distinctly discerns the changing trends of deformation signals,thereby providing robust support for interpreting potential deformation scenarios within observed InSAR regions.
基金supported by National Key Research and Development Program of China under grant number 2022YFB3903404National Natural Science Foundation of China under grant number 42325105,42071350LIESMARS Special Research Funding.
文摘High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.
基金partially supported by‘National Programme for the Promotion of Talent and Its Employability’of the Ministry of Economy,Industry,and Competitiveness(Torres-Quevedo program)via postdoctoral PTQ2018–010043 to Dr.Juan Guerra HernándezForest Research Centre,a research unit funded by Funda??o para a Ciência e a Tecnologia I.P.(FCT),Portugal(UIDB/00239/2020)Arizona State University,USDA Forest Service and the Asner Lab supported Dr.Adrián Pascual in the final stages of the research。
文摘Background:The NASA’s Global Ecosystem Dynamics Investigation(GEDI)satellite mission aims at scanning forest ecosystems on a multi-temporal short-rotation basis.The GEDI data can validate and update statistics from nationwide airborne laser scanning(ALS).We present a case in the Northwest of Spain using GEDI statistics and nationwide ALS surveys to estimate forest dynamics in three fast-growing forest ecosystems comprising 211,346 ha.The objectives were:i)to analyze the potential of GEDI to detect disturbances,ii)to investigate uncertainty source regarding non-positive height increments from the 2015–2017 ALS data to the 2019 GEDI laser shots and iii)to estimate height growth using polygons from the Forest Map of Spain(FMS).A set of 258 National Forest Inventory plots were used to validate the observed height dynamics.Results:The spatio-temporal assessment from ALS surveying to GEDI scanning allowed the large-scale detection of harvests.The mean annual height growths were 0.79(SD=0.63),0.60(SD=0.42)and 0.94(SD=0.75)m for Pinus pinaster,Pinus radiata and Eucalyptus spp.,respectively.The median annual values from the ALS-GEDI positive increments were close to NFI-based growth values computed for Pinus pinaster and Pinus radiata,respectively.The effect of edge border,spatial co-registration of GEDI shots and the influence of forest cover in the observed dynamics were important factors to considering when processing ALS data and GEDI shots.Discussion:The use of GEDI laser data provides valuable insights for forest industry operations especially when accounting for fast changes.However,errors derived from positioning,ground finder and canopy structure can introduce uncertainty to understand the detected growth patterns as documented in this study.The analysis of forest growth using ALS and GEDI would benefit from the generalization of common rules and data processing schemes as the GEDI mission is increasingly being utilized in the forest remote sensing community.
基金supported by the State Forestry Administration of China under the national forestry commonwealth project grant#201404309the Expert Workstation of Academician Tang Shouzheng of Yunnan Province,the Yunnan provincial key project of Forestrythe Research Center of Kunming Forestry Information Engineering Technology
文摘Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.
基金supported by the National Basic Research Program of China("973"Project)(Grant No.2006CB701304)Research Grants Council General Research Fund of Hong Kong(Grant No.HKBU2029/07P)Hong Kong Baptist University Faculty Research Grant(Grant No.FRG/06-07/II-76)
文摘Landuse and land cover change is regarded as a good indicator that represents the impact of human activities on earth’s environment.When the large collection of multi-temporal satellite images has become available,it is possible to study a long-term historical process of land cover change.This study aims to investigate the spatio-temporal pattern and driving force of land cover change in the Pearl River Delta region in southern China,where the rapid development has been witnessed since 1980s.The fast economic growth has been associated with an accelerated expansion of urban landuse,which has been recorded by historical remote sensing images.This paper reports the method and outcome of the research that attempts to model spatio-temporal pattern of land cover change using multi-temporal satellite images.The classified satellite images were compared to detect the change from various landuse types to built-up areas.The trajectories of land cover change have then been established based on the time-series of the classified land cover classes.The correlation between the expansion of built-up areas and selected economic data has also been analysed for better understanding on the driving force of the rapid urbanisation process.The result shows that,since early 1990s,the dominant trend of land cover change has been from farmland to urban landuse.The relationship between economic growth indicator(measured by GDP)and built-up area can well fit into a linear regression model with correlation coefficients greater than 0.9.It is quite clear that cities or towns have been sprawling in general,demonstrating two growth models that were closely related to the economic development stages.
基金Supported by the National Key Research and Development Program of China(2018YFC1506500)。
文摘Fengyun-4 A(FY-4 A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme weather such as sudden flood disasters. In this study, the flood disasters occurred in Bangladesh, India, and some other areas of South Asia in August 2018 were investigated by using a rapid multi-temporal synthesis approach for the first time for removal of thick clouds in FY-4 A images. The maximum between-class variance algorithm(OTSU;developed by Otsu in 2007) and linear spectral unmixing methods are used to extract the water area of flood disasters. The accuracy verification shows that the water area of flood disasters extracted from FY-4 A is highly correlated with that from the high-resolution satellite datasets Gaofen-1(GF-1) and Sentinel-1 A, with the square correlation coefficient R2 reaching 0.9966. The average extraction accuracy of FY-4 A is over 90%. With the rapid multi-temporal synthesis approach used in flood disaster monitoring with FY-4 A satellite data, advantages of the wide coverage, fast acquisition,and strong timeliness with geostationary meteorological satellites are effectively combined. Through the synthesis of multi-temporal images of the flood water body, the influence of clouds is effectively eliminated, which is of great significance for the real-time flood monitoring. This also provides an important service guarantee for the disaster prevention and reduction as well as economic and social development in China and the Asia-Pacific region.
基金partially supported by the National Natural Science Foundation of China(No.41171323)Jiangsu Provincial Natural Science Foundation(No.BK2012018)+2 种基金the Key Laboratory of Geo-Informatics of National Administration of Surveying,Mapping and Geoinformation of China(No.201109)partially supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the Fundamental Research Funds for the Central Universities.
文摘Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral mixture.Especially for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution.Thus,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change detection.In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion.Nonlinear spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences.The proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas.The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(i.e.change vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28070503)the National Key Research and Development Program of China(2021YFD1500100)+2 种基金the Open Fund of State Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University (20R04)Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project(CASPLOS-CCSI)a PhD studentship ‘‘Deep Learning in massive area,multi-scale resolution remotely sensed imagery”(EAA7369),sponsored by Lancaster University and Ordnance Survey (the national mapping agency of Great Britain)。
文摘Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise CNN(79.18%and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes(e.g., forest landscapes), with a wide application prospect.
基金supported by the National Key Research and Development Program of China(Grant No.2019YFE0127700)。
文摘Most forest fires in the Margalla Hills are related to human activities and socioeconomic factors are essential to assess their likelihood of occurrence.This study considers both environmental(altitude,precipitation,forest type,terrain and humidity index)and socioeconomic(population density,distance from roads and urban areas)factors to analyze how human behavior affects the risk of forest fires.Maximum entropy(Maxent)modelling and random forest(RF)machine learning methods were used to predict the probability and spatial diffusion patterns of forest fires in the Margalla Hills.The receiver operating characteristic(ROC)curve and the area under the ROC curve(AUC)were used to compare the models.We studied the fire history from 1990 to 2019 to establish the relationship between the probability of forest fire and environmental and socioeconomic changes.Using Maxent,the AUC fire probability values for the 1999 s,2009 s,and 2019 s were 0.532,0.569,and 0.518,respectively;using RF,they were 0.782,0.825,and 0.789,respectively.Fires were mainly distributed in urban areas and their probability of occurrence was related to accessibility and human behaviour/activity.AUC principles for validation were greater in the random forest models than in the Maxent models.Our results can be used to establish preventive measures to reduce risks of forest fires by considering socio-economic and environmental conditions.
文摘Background: The Shelduck (Tadorna tadorna) is a characteristic waterbird species of inland wetlands in northeastern Algeria. Its wintering behavior in relation to changes of local abundances and foraging group density is poorly known. Objectives: This study aims at monitoring patterns of diurnal activities and the variation of behavioral time-budgets in relation to numbers of wintering Shelducks. We investigate temporal variations of diurnal activities across multipletime scales and consider their interrelationships. Methods: Assessments of local population abundance were weekly surveyed during two wintering seasons (2010– 2012), whereas diurnal activities (feeding, sleeping, swimming, preening, loafing, flying, courtship, and antagonism) were studied three times a month during seven hours (08:00–16:00) using the Scan method. Time budget variations of each behavioral activity were tested using nested ANOVAs following multiple time scales. Generalized linear mixedeffects models (GLMM) tested whether variations in diurnal activities were density-dependent. Results: During the wintering season, Shelduck’s numbers followed a bell-shaped trend, which indicated that the species was typically a wintering migrant in Sabkha Djendli. The first individuals arrived onsite in October–November then numbers reached a peak in January (up to 2400 individuals in 2012) with steady density during December–February, afterward individuals left the site progressively until late April when the site is deserted. During both wintering seasons, diurnal activities were dominated by feeding (60%), followed by sleeping (12%) then swimming and preening with 9% and 8%, respectively. The rest of the activities (loafing, flying, courtship and antagonistic behaviors) had low proportions of time budget. ANOVAs showed that activity time budgets varied significantly following multiple time scales (year, season, month, day, semi-hour). Time budgets of diurnal activities during each wintering season were significantly interrelated. Correlations patterns between the two seasons were similar. GLMMs revealed that the variations of diurnal activities were not density-dependent, except for preening and swimming. Conclusion: During the wintering season, habitats of Sabkha Djendli are important for waterbirds, including the Shelduck that used the lake mainly for food-foraging and resting. The 2400 individuals censused in mid-winter are important locally and at the North African scale. This stresses the need to strengthen the protection status of this wetland and mitigate degradation sources that threaten wintering waterfowl.