This work presents a progressive image vectorization technique that reconstructs the raster image as layer-wise vectors from semantic-aligned macro structures to finer details. Our approach introduces a new image simplification method leveraging the feature-average effect in the Score Distillation Sampling mechanism, achieving effective visual abstraction from the detailed to coarse. Guided by the sequence of progressive simplified images, we propose a two-stage vectorization process of structural buildup and visual refinement, constructing the vectors in an organized and manageable manner. The resulting vectors are layered and well-aligned with the target image’s explicit and implicit semantic structures. Our method demonstrates high performance across a wide range of images. Comparative analysis with existing vectorization methods highlights our technique’s superiority in creating vectors with high visual fidelity, and more importantly, achieving higher semantic alignment and more compact layered representation.

Large Multimodal Models (LMMs) has demonstrated capabilities across various domains, but comprehensive benchmarks for agricultural remote sensing (RS) remain scarce. Existing benchmarks designed for agricultural RS scenarios exhibit notable limitations, primarily in terms of insufficient scene diversity in the dataset and oversimplified task design. To bridge this gap, we introduce AgroMind, a comprehensive agricultural remote sensing benchmark covering four task dimensions: spatial perception, object understanding, scene understanding, and scene reasoning, with a total of 13 task types, ranging from crop identification and health monitoring to environmental analysis. We curate a high-quality evaluation set by integrating nine public datasets and one private global parcel dataset, containing 28,482 QA pairs and 20,850 images. The pipeline begins with multi-source data pre-processing, including collection, format standardization, and annotation refinement. We then generate a diverse set of agriculturally relevant questions through the systematic definition of tasks. Finally, we employ LMMs for inference, generating responses, and performing detailed examinations. We evaluated 20 open-source LMMs and 4 closed-source models on AgroMind. Experiments reveal significant performance gaps, particularly in spatial reasoning and fine-grained recognition, it is notable that human performance lags behind several leading LMMs. By establishing a standardized evaluation framework for agricultural RS, AgroMind reveals the limitations of LMMs in domain knowledge and highlights critical challenges for future work. Data and code can be accessed at https://rssysu.github.io/AgroMind/.

Agricultural parcels serve as basic units for conducting agricultural practices and applications, which is vital for land ownership registration, food security assessment, soil erosion monitoring, etc. However, existing agriculture parcel extraction studies only focus on mid-resolution mapping or regular plain farmlands while lacking representation of complex terraced terrains due to the demands of precision agriculture. In this paper, we introduce a more fine-grained terraced parcel dataset named GTPBD (Global Terraced Parcel and Boundary Dataset), which is the first fine-grained dataset covering major worldwide terraced regions with more than 200,000 complex terraced parcels with manually annotation. GTPBD comprises 47,537 high-resolution images with three-level labels, including pixel-level boundary labels, mask labels, and parcel labels. It covers seven major geographic zones in China and transcontinental climatic regions around the world. Compared to the existing datasets, the GTPBD dataset brings considerable challenges due to the: (1) terrain diversity; (2) complex and irregular parcel objects; and (3) multiple domain styles. Our proposed GTPBD dataset is suitable for four different tasks, including semantic segmentation, edge detection, terraced parcel extraction and unsupervised domain adaptation (UDA) tasks. Accordingly, we benchmark the GTPBD dataset on eight semantic segmentation methods, four edge extraction methods, three parcel extraction methods and five UDA methods, along with a multi-dimensional evaluation framework integrating pixel-level and object-level metrics. GTPBD fills a critical gap in terraced remote sensing research, providing a basic infrastructure for fine-grained agricultural terrain analysis and cross-scenario knowledge transfer. The code and data are available at https://github.com/Z-ZW-WXQ/GTPBD/.

While domain adaptation (DA) methods have made significant strides in remote sensing community, most current works assume that the source domain labels are accurate. However, limited emphasis has been placed on the scenario where source data are mislabeled with noisy annotations, which is more common in real applications and referred to as noisy DA (NDA). This article formulates remote sensing cross-scene classification on NDA scenarios and proposes a novel network called bilateral adaptation network (BAN), which consists of two parts: 1) forward learning (FL), which utilizes a model learning from the noisy source domain and transfers knowledge to target domain; and 2) backward learning (BL), which utilizes a dual model to acquire knowledge from the target domain and transfer it to source domain. We conduct two parts alternately and adopt a symmetrical Kullback-Leibler (KL) loss to align predictions of the model and its dual model in the same domain. This interactive strategy is able to explore bilateral relationships between domains, implicitly reducing label noise in the source domain. In addition, BAN could serve as a universal paradigm to not only improve the existing NDA methods but also enhance recent DA approaches. Comprehensive evaluations on three publicly available RGB-band remote sensing datasets and two hyperspectral datasets validate the superior effectiveness of our proposed BAN. BAN improves the average accuracy by 6.70%–15.70% on RGB datasets and overall accuracy (OA) by 1.36%–3.14% on hyperspectral datasets with flip-20% noise compared to other state-of-the-art DA and NDA approaches. Promising results indicate the potential of our approach in tackling more general and practical problems with noisy source domain.

Recently, solar-induced chlorophyll fluorescence (SIF) from satellites has shown potential for evaluating vegetation status and stress responses. Fluorescence quantum yield (ΦF) is essentially linked to vegetation stress. However, the complex physiological and structural responses of SIF and ΦFto drought need further study. This study normalized SIF as SIFnto account for angular variations and fluctuations in photosynthetically active radiation (PAR), aiming for more accurate drought monitoring. SIFnanomalies were compared to historical baselines (2019–2021 averages) of vegetation indices (VIs), raw SIF, and ΦFduring a 2019 drought in the North China Plain (NCP). The results show SIFnprovides an effective method for drought monitoring, showing the earliest decline compared to raw SIF, VIs, and ΦF. In the first two weeks of drought, SIFndecreased by 8.2%, 7.0%, 12.5%, and 8.2% across the four NCP subdivisions. SIFnoutperformed other indicators, proving sensitive to early drought detection. SIFnwas also examined for tracking drought alleviation by rainfall. The uncertainty under different viewing geometries was quantified. SIFnanomalies showed a strong correlation with rainfall anomalies (R: 0.45 ~ 0.52) and meteorological factors like PAR (R: 0.80 ~ 0.84) and relative humidity (R:0.52 ~ 0.54). The correlation of near-infrared reflectance (NIRv) and ΦFanomalies with SIF was weak during drought onset (R: 0.16 ~ 0.32) but strong at the end (R: 0.83 ~ 0.87). These suggest both canopy structure (mainly characterized by NIRv) and vegetation chlorophyll (ΦF) are impacted by drought and influence SIF at different stages.

In the field of remote sensing image classification, domain adaptation (DA) methods have been extensively utilized to overcome the challenges posed by data discrepancies between source and target domains that arise from varying imaging conditions, sensor differences, or geographical variations. Stemming from the existence of unseen classes in both the source and target domains, universal DA (UniDA) poses the greatest challenge that demands innovative solutions. Existing UniDA methods often overlook intra-domain variations within the target domain and face difficulties in distinguishing between similar known and unknown classes, which significantly hinder cross-domain transfer. To overcome these challenges, we propose a dual-classifier network tailored for cross-domain classification of remote sensing images, namedDCmix. DCmix introduces a dual-classifiers network that utilizes both closed-set and open-set classifiers to improve the accuracy of identifying unknown sample classes. To our knowledge, this is the first attempt to introduce dual classifiers into the UniDA remote sensing image classification task. We further enhance the feature generalization capability of the target domain based on sample neighborhood relations, resulting in a more adaptable and robust feature representation. A cross-domain feature mixup scheme is also designed based on the consistency discrimination of the dual classifiers, achieving smoother decision boundaries and simpler hidden layer representations. Extensive experiments conducted on four hyperspectral image datasets and three RGB datasets prove that the introduced approach attains state-of-the-art performance in remote sensing image classification under the UniDA scenario.

Aboveground biomass (AGB) of rice is crucial for monitoring growth and predicting yields. While deep learning algorithms, such as deep convolutional neural networks (DCNNs), show compelling performance in estimating crop parameters, gathering sufficient ground-truth samples for model training poses a significant challenge, leading to the “small sample problem.” To address this, we propose a framework that utilizes a hybrid inversion model based on the PROSAIL-PRO radiative transfer model (RTM) combined with machine learning techniques [XGBoost and random forest (RF)]. This framework incorporates active learning optimization and the spectral angle mapper (SAM) method to select simulated samples that closely match real-world conditions, simultaneously assigning geographic location information to the samples. Using these qualified samples, we constructed both single-branch and multibranch DCNN models that integrate uncrewed aerial vehicle (UAV)-based hyperspectral principal components (PCs), canopy height (CH) information from the canopy surface model (CSM), and canopy temperature derived from thermal infrared (TIR) images. The effectiveness of this approach was validated across two experimental sites. The single-branch DCNN achieved the highest accuracy at site A ($R^{2} =0.816$and root-mean-square error (RMSE) =61.608 g/m2) with PCs, TIR, and CSM as inputs, while the multibranch DCNN performed best at site B ($R^{2} =0.784$and RMSE =65.533 g/m2), using PCs and TIR as inputs. Results indicate that simulated samples have considerable potential for practical applications. PCs were the primary contributors to the model, with TIR playing a more significant role than CSM. Overall, this study demonstrates high-precision estimation of rice AGB despite limited measured samples, offering valuable insights for crop monitoring under small sample conditions.

The accurate simulation of Bidirectional Reflectance Distribution Function (BRDF) across varied Crop Residue Cover (CRC) scenarios is pivotal for crop residue monitoring and management. Addressing the limitations of prior research in simulating BRDF for cropland with CRC, we have developed the novel Crop Residue-covered Bidirectional Reflectance (CRBR) model. This model couples Geometric Optical (GO) and Radiative Transfer (RT) model, which involves adding a clumping index and Crop Residue Tilt Angle (CRTA) distribution function through terrestrial laser scanning to parameterize the spatial distribution of covered crop residue. Validation of the CRBR model was conducted using corn residue cover data from Lishu County, Jilin Province, China, collected in April 2023. The results demonstrated strong alignment between the simulated and measured multi-angle bands reflectance (R² = 0.90, RMSE = 0.03, MAPE = 8.91%). Under various CRC scenarios, the CRBR model consistently outperformed linear mixed models (R² ≥ 0.99, RMSE ≤ 0.02, MAPE ≤ 4.14% vs R² ≥ 0.97, RMSE ≤ 0.05, MAPE ≤ 23.69%). Sensitivity analysis revealed the impact of key model parameters on reflectance simulation. Furthermore, we also examined the adaptability of our model under different moisture conditions and CRC scenarios, confirming its robustness and flexibility. The CRBR model not only helps our understanding of radiative transfer in crop residue-soil scenarios but also offers a promising approach for efficient and precise CRC estimation on a regional scale. Such advancements in the CRBR model hold significant implications for conservation tillage monitoring, biomass energy reserve estimation, and cropland carbon storage capacity assessment.

Solar-induced chlorophyll fluorescence (SIF) shows potential in exploring plant responses to environmental changes caused by extreme climatic factors. However, how to accurately assess climate stresses (especially the low-temperature stress) suffered on crops at the regional scale in a systematic approach has not been extensively explored. In this study, we developed a climate vegetation stress index (CVSI) to assess and quantify the impacts of climate stress on crops at large scales by combining TROPOspheric Monitoring Instrument (TROPOMI) SIF and land surface temperature (LST) data through an easy-to-operate approach. This index was employed to identify low-temperature stress conditions in Henan Province’s winter wheat in 2018. Results indicate that, influenced by climate characteristics, crops in the northern part of Henan Province experienced more severe low-temperature stress than those in the southern part. The daily average SIF values experienced reductions of 0.74, 0.45, 0.61, and 0.86 mW$\cdot ~\text{m}^{-2}~\cdot $sr$^{-1}~\cdot $nm−1 during the four cooling episodes within the two phenological periods, respectively. As low-temperature stress intensified, winter wheat growth was hindered, reducing grain yield. Indeed, the CVSI provides an accurate depiction of crop stress levels and patterns. In areas with high-CVSI values, yield losses are particularly severe. In addition, the significant positive correlation between the CVSI and net primary productivity (NPP), along with the similar spatial intensity pattern, shows the effectiveness of CVSI in monitoring low-temperature stress. CVSI provides a new approach to understand the impacts of climate change on overwintering crops and offers a practical reference for climate stress effects monitoring at the regional scale.

Data assimilation has been demonstrated as the potential crop yield estimation approach. Accurate quantification of model and observation errors is the key to determining the success of a data assimilation system. However, the crop growth model error is not fully taken into account in most of the previous studies. The objective of this study is to better quantify the model uncertainty in the data assimilation system. Firstly, we calibrated a crop growth model and inferred its posterior uncertainty based on the Global LAnd Surface Satellite (GLASS) 250-m LAI product, regional statistical data, station observations, and field measurements with a Markov chain Monte Carlo (MCMC) method. Secondly, the model posterior uncertainty was used in the Ensemble Kalman Filter (EnKF) algorithm to better characterize the ensemble distribution of model errors. Our results indicated the proposed Bayesian posterior-based EnKF can improve the accuracy of winter wheat yield estimation at both the point scale (the coefficient of determination R2value increasing from 0.06 to 0.41, the mean absolute percentage error MAPE value decreasing from 12.65% to 7.82%, and the root mean square error RMSE value decreasing from 987 to 688 kg∙ha-1) and the regional scale (R2value from 0.30 to 0.57, MAPE value from 19.67% to 10.13%, and RMSE value from 1275 to 695 kg∙ha-1) compared with the open-loop estimation. Our analysis also indicated that the Bayesian posterior-based EnKF can perform better compared to the standard Gaussian perturbation-based EnKF. The proposed framework provides an important reference for crop yield estimation at the regional scale in similar agricultural landscapes worldwide.

Against rapid development in urban areas, timely urban land cover changes (ULCC) information is beneficial for understanding the urban environment and promoting sustainable development. To realize real-time urban land cover change detection, high-frequency remotely sensed data are urgently needed. In this study, we tested the detection capability of urban land cover changes using a new daily nighttime light image (Black Marble). Firstly, time series of VNP46V2 from 2012-2019 were collected and decoded into annual trend segments using the BFAST Monitor model. Then, we recognized the jump point in trend segments and defined the corresponding pixel as urban land cover change. We analyzed the Normalized Difference Vegetation Index (NDVI) time series from Landsat images spanning 2014-2019 and removed pixels without significant seasonal fluctuations from ULCC assembled. Finally, the magnitude and change time of ULCC pixels were quantified through BFAST Monitor. It was proved that Black Marble performed well in ULCC detection, achieving an overall accuracy of 87.75%, and detected change time was accurate to 81.12% under ± 1 year allowable error. The present Black Marble data have the potential for real-time urban land use detection and global mapping of ULCC, especially in areas without enough clear-sky observations.

In this work, we compare the impact of two ways of representing the crop growth model uncertainty (the crop growth model was calibrated by Markov Chain Monte Carlo (MCMC)) on the subsequent assimilation of the Sentinel-2 LAI through Ensemble Kalman Filter (EnKF): generating N sets of crop parameters based on the Optimal-posterior parameter value (reference method, we name it OMCMC-EnKF) and Randomly selecting N sets of crop parameters from the most frequent parameters (our proposed method, RMCMC-EnKF). The results show that our proposed method is better than the reference model, when compared with the field measured winter wheat LAI and yield, its performance in the R2, RMSE, and uncertainty estimation is better. Furthermore, our proposed method is more robust to feedback from remote sensing data and more resistant to lower quality observations encountered during assimilation.

Accurate and timely regional crop yield information, particularly field-level yield estimation, is essential for commodity traders and producers in planning production, growing, harvesting, and other interconnected marketing activities. In this study, we propose a novel data assimilation framework. Firstly, we construct the county-level prior and likelihood constraints for a process-based crop growth model based on the previous year's statistical yield and the current year's field observations. Then, we infer the posterior sets of model-simulated time-series LAI and the final yield of winter wheat with an MCMC (Markov chain Monte Carlo) method for each meteorological data grid of ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5). Finally, we estimate the winter wheat yield at the spatial resolution of 10 m by combining Sentinel-2 LAI and the WOFOST model in Hengshui, the prefecture-level city of Hebei province of China. The results show that the proposed framework can estimate the winter wheat yield with a coefficient of determination R2equal to 0.29 and mean absolute percentage error MAPE equal to 7.20% compared with field measurements. However, agricultural stress that crop growth models cannot quantitatively simulate, such as lodging, can greatly reduce the accuracy. The results also suggest good agreements with county-level statistics of the growing year with a coefficient of determination R2equal to 0.52 and mean absolute percentage error MAPE equal to 7.19%.

Assessing crop lodging at the regional scale is an important requirement for breeding lodging-resistant varieties and harvest planning. Accurately and continuously estimating crop lodging area from remote sensing data remains challenging due to the high randomness scattering signal of SAR images and the insufficient number of applicable optical images. This study developed a new framework for estimating crop lodging area based on SAR data using the spatial aggregation approach of field units, overcoming the deficit of the traditional pixel-based approach susceptible to speckle noise and spatial heterogeneity. We aggregated the field’s pixel in SAR images using the spatial aggregation approach. The lodging area estimation models of dual-pol and quad-pol were established using a random forest (RF) algorithm. The Sobol approach evaluated the uncertainty and sensitivity at a regional scale. Finally, we analyzed the scattering mechanisms of the lodging field. Results indicate that the proposed method achieves the high performance of the lodging area estimates at the regional scale, in the testing set, with R2and RMSE of the GF-3 model being 0.57 and 18.63%, and the Sentinel-1 model is 0.49 and 20.59%. Besides, the uncertainties of models are below 10%, and are insensitive to the variation of parameters inter-correlation. The depolarization effect and scattering randomness gradually weaken with the increase of lodging percentages. In contrast, the surface scattering quickly increases and finally dominates the total scattering after lodging percentages greater than 80%. This proposed approach would help develop a real-time crop lodging monitoring system using SAR data.

Spatially distribution information on wheat yield forecasting at the large regional scale is important for market forecast and agricultural sustainable development. Assimilating remote sensing information into the crop growth model has demonstrative to be the effective approach for crop yield prediction. However, it remains a challenge to determine the crop growth model of input parameters and initial conditions at the spatial regional scale. In the paper, we proposed a Markov Chain Monte Carlo (MCMC) and 4DVAR hierarchical data assimilation scheme, which enables the winter wheat yield forecasting at the 500 m grid size ahead of harvest time in Henan province. This approach applies data assimilation algorithms at two spatial scales. At the county scale, the MCMC algorithm was used to recalibrate the uncertain and sensitive parameters of the WOFOST model using the county-level statistical yield. Then, we assimilated time-series MODIS reflectance into WOFOST-PROSAIL coupled model using the 4DVAR method for each 500 m pixel across the entire Henan province of China. The results show that the simulated yield was strong positive correlated with the statistical yield at county-level scale with R2 = 0.81 and RMSE = 877 kg/hm2, which demonstrated the potential usage of the MCMC-4DVAR based large area yield estimation with remote sensed data and yield statistics.

Crop yield estimation at the field scale is essential for farmers, crop insurance companies to make informed decisions. Methodologies based on assimilating remote sensing LAI into crop growth models have shown advantages in crop yield estimates. Compared with MODIS and Landsat, Sentinel-2 satellites provide higher spatial and temporal resolution data, which brings revolutionary opportunities for crop monitoring. This study is to evaluate the performance of assimilating Sentinel-2 LAI into the WOFOST model for winter wheat yield estimation using the Ensemble Kalman Filter algorithm. The results showed that assimilating Sentinel-2 LAI improved the yield estimation (R2= 0.45; RMSE = 512 kg/ha) compared to the situation without data assimilation (R2= 0.27; RMSE = 818 kg/ha), which demonstrated the potential usage of the Sentinel-2 LAI for yield estimation at the field scale.

As an important land surface parameter for crop growth model, leaf area index (LAI) is desired to be estimated accurately on regional scale. Before applying LAI estimation model built at local scale to the regional scale, the mismatch between multi-scale sensors should be corrected. We firstly applied the kernel-driven BRDF model to describe the two-dimensional reflection characteristics of Landsat 5-TM data based on the kernel weights from MODIS MCD43A1 products and the angle vectors obtained from TM Collection 1 Level-1 dataset. Then the point spread function (PSF) was used to simulate the spatial response of the sensor. Results show that there is a good agreement between TM observations and corrected MODIS values (Red band: R2= 0.736, RMSE=2.65e-4, Near-infrared band: R2=0.539, RMSE=5.39e-4). If we apply the LAI estimation model that has been validated at local scale (TM) directly to the data at large scale (MODIS) without any correction, more than 50% uncertainty of LAI estimation would be introduced. This study implies that the estimation of LAI in winter wheat could be significantly improved by correcting the differences between multi-scale sensors.

The rain-fed agriculture dominates the agricultural production in Northeast China, which leads to the drought risk for crops growing. To assess the yields and necessity of irrigation for maize in the Three Northeast Provinces of China, the calibrated and validated WOrld FOod STudies (WOFOST) model was used to estimate maize yieldss in the regional scale after it was optimized by assimilating leaf area index retrieved from remote sensing data based on Shuffled Complex Evolution (SCE-UA) algorithm. Then the gaps between potential and water limiting yieldss (the irrigation was equal to zero) were computed and mapped to detect the area where the crop irrigation improvement was demanded. The presented result could support the decision from government on the crop management which suggested that crops in the northwest of Liaoning Province, the west of Jilin Province and the southwest of Heilongjiang Province lacked the irrigation most.

In this paper, we focus on winter wheat yield assessment in Hebei province in china. The method take two procedures: first, we extract phenological transition dates from MODIS-LAI. Then, using SCE_UA algorithm, we assimilated the phenology information into crop growth model SWAP. The results shown that an improved accuracy of crop yield can be achieved compared to the method with no assimilation algorithm.

With the fast development of Internet technology, human society is moving into an information age. Digital city, as well as digital earth, has become a new symbol of the progress of society, and at the meanwhile, in order to represent the real world, as we know, GIS has been proved to be the most effective technical platform. Traditional GIS map service generally provides some simple spatial data analysis, such as the enquiry of the location, the shortest path analysis, analysis of the buffer zone, and so on. People online ask for more services with a growing demand nowadays, so these services can no longer meet the people's needs, not only the shortest path between the two places what the computer is giving, they would also like to know more detail about information of this strange street, such as the special features along the street, so that they can have a direct sense of the street. In order to meet people's needs, and promote the development of GIS map service, the author studies on this area. As we know, there are so many ways to design a WebGIS platform, and this paper chooses a popular one. Based on the technology of asp.net, we can use the ArcIMS to distribute our maps, which is a product of the ESRI, and the activeX to distribute our video, which has been edited. Generally, when you establish a geographic information system, the big problem is the acquisition of data.

In recent years, it was one of the currently hot issues, which study the evolution of river way and hydro-system using 3S technology. Aerial photos, TM/ETM+ and SPOT5 imagery were offered to study the evolution of riverway in QingKou region. Automatic detection for change information method such as spectral variation method and false color composite method were used to detect change information, the results showed that the former method was better. Five Rivers in Qingkou Region were selected for our research, change information such as length and width was extracted separately. On this basis, the driving factors of evolution were analyzed. The riverway gradually become steady due to effective river rectification, the influence caused by man-made factors was greater than natural factors in the evolution.

Traditional land cover performance from remote sensing imagery using the statistical characteristics of the pixel have encountered a lot of difficulties in dealing with the issue of classification of high-resolution images. Object-oriented classification techniques based on image segmentation are a good solution to this problem. Qingkou region, the Beijing-Hangzhou Grand Canal and other eight natural rivers junctions, has take break dramatic changes in the last 50 years, therefore, research on land cover performance in this region have great significance. In this paper, object-oriented classification method was used to extract land cover information from SPOT5 imagery in Qingkou region. The imagery was segmented in two scale parameters to create homogeneous objects for different classes. And the overall classification stability is up to more than 87%. It is available to provide research data for land cover performance evaluation in Beijing-Hangzhou Grand Canal and development research on cities along the canal.

In this paper, the Kuusk-Nilson forest reflectance and transmittance (FRT) model was inverted to retrieve the overstory and understory leaf area index (LAI) of forest stands in the Longmenhe Nature Reserve (Xingshan County, Hubei province, China). Atmospherically and topographically corrected hyperspectral Hyperion imagery and field data had been input to retrieve the overstory and understory LAI simultaneously using FRT inverted model. An uncertainty and sensitivity matrix was used to analyze the sensitivity of the FRT model parameters based on field data. Twenty-one Hyperion bands were selected based on principal-components analysis and band importance from total Hyperion bands. Eight different Hyperion band combinations from 21 Hyperion bands were tested to evaluate the accuracy of the inversion of overstory and understory LAI. Our study showed that the overstory LAI of stands can be better retrieved when considering the understory LAI compared to only use total LAI.

Wetland is a kind of natural resource with fast dynamic changing. The study of wetland information extracting way which is fast and measurable is front of wetland remote sensing. SPOT5 imagery has high spatial resolution and multi-spectral resolution, which provided a rich, reliable and accurate source data for wetland resources investigation. This paper took the Nansihu wetlands as the study area and combining with the characteristics of SPOT5 imagery discussed wetland resources investigation methods. In the study, SWIR band threshold method was used to extract information of water bodies, Spectral Relations method was used to get rid of shadows from extraction of water bodies and Visual Interpretation method based on GIS information was used for the extraction of other type wetlands. Using above method, Nansihu area 12 categories wetlands information were extracted. Practice proved that this method was simple and practical.

The primary goal of this research was to study the method to calculate the green space accessibility, which was used to assess the capacity and chance of inhabitants to approach green space. Many factors affecting the green space accessibility were taken into account, such as attraction of green space, distribution of population, land use pattern and traffic cost. Based on GIS software, the study area was divided into regular grids with the size of 500 meters. For each grid, the resistance for accessing a green space was calculated regarding to qualified factors based on gravity & spatial interaction model and shortest path analysis. Then the green space accessibility of whole city was accumulated by the sum of all grids' computing results. Taking Beijing city as a case study, SPOT imagery in three periods were used to acquire information of green space to assess its accessibility and check the evolution process.

This paper reports the results of a research project on applying geospatial information technology to the protection of China's Grand Canal. The canal is the longest man-made waterway in the world. The earliest section of the canal can be dated back to 500 BC. The course of the canal has been changed many times during its history mainly depending on the locations of China's capitals. The current course of the canal was completed in 1291 running from Beijing in the north to Hangzhou in the south. Its total length is 1200 miles. It is very difficu.lt to use conventional methods to conduct archaeological studies on the canal. This study focuses on the section of the canal near Nanwang township in Shandong province. The archaeological study of this research is based on the remote sensing technology with data from various sources such as ancient maps, historical documents, aerial photographs in 1950s, and recent remote sensing imageries. The results of this research are verified in the field.

This paper presents the major outcomes of a multidisciplinary research project - the Research and Application of Spatial Information Technology for Conservation of Large-scale Heritage Sites, which is within the National Key Technology R&D Program and aims to apply spatial information technology for cultural heritage conservation, especially the large-scale heritage sites, and takes the Grand Canal of China (GCC) to conduct a case study. At first an integrated framework for the application of spatial information technology in conserving cultural heritage was conceived comprehensively. Then some standards were established with the effort of domain experts including the standards for metadata, spatial data and professional data. Besides that a spatial data base of GCC, a PDA based system for data collecting and GIS system for GCC was set up and reviewed in detail. Finally this paper appraises the project's established results objectively; present the research planning in the future.

Land cover change has largely resulted in deforestation, biodiversity loss, global warming and reduction of environmental services, so many countries and organizations establish land cover data through multiform methods. Despite its importance, accurate statistics on land cover change data is not available in most countries, the detection and monitoring of land cover dynamics is highly desirable. With increasing frequency, remotely sensed data sets have been used to classify global land cover. The objective of this paper is to construct an operational system to update land cover dataset based on CBERS-02B image and outdated land cover data.

It is important to inventory and monitor wetlands and their adjacent environment. People can't go to somewhere of wetlands. Satellite remote sensing has several advantages for monitoring wetland resources, especially for large geographic areas and no man's land This paper uses multi-temporal Landsat TM and ETM+ data to study the degradation of wetlands. The simple method to classify wetlands is unsupervised classification or clustering. Wetland classification is difficult because of spectral confusion with other landcover classes and among different types of wetlands. However, multi-temporal remote sensing data and ancillary data such as soil data, elevation or topography data usually improves the classification of wetlands. Change detection studies have taken advantage of the repeat coverage and archival data available with satellite remote sensing. The result of multi-temporal monitoring indicates the degradation of Ruoergai Wetland.

The ability to extract the special land use type of environment, and associated temporal changes, has important societal and economic meaning. This paper uses the high spatial resolution of the image---QuickBird to extract greenhouse in agriculture of Hexian region, ANHUI province in China. The paper uses software package eCognition to process data and extract information. The software adopted object-oriented image segmentation and classification which is based on fuzzy logic. In this study the greenhouse is a special land cover type in agricultural land, we use not only image object's attributes, but also the relationship between networked image objects; it can perform sophisticated classification and get satisfied classification result, allows the integration of a broad spectrum of different object features, such as spectral values, shape and texture. The aim of this work was to develop an object-oriented segmentation and classification approach for extracting special land cover type.

The main objective of this research is to analyses the accuracy and calibrate the sensors to develop a mobile mapping system for automatic surveying of the 3D objects. This system has become quite popular in recent years due to it's capability of providing information directly in three dimension. In our system, we have use laser scanners as the main data acquisition device, supplemented by line CCD cameras for texture information and as usual combination of GPS, INS and odometer for position and attitude information. Because every sensor and device has it's own local coordinate system. For example, GPS output is based on WGS84 coordinates system, Laser data is based on it's own local coordinate system, the origin of which lies at the laser scanning head and so on for other sensors and device. The major problem is to identify the spatial position of the objects scanned by the laser at any time while the vehicle is moving with reference to a common coordinate system. It involves the integration of all the sensors and devices to a common coordinate system, which is the local mapping coordinate system. The integration process mainly involves the computation of fixed rotation and shift vectors between the INS body and sensors. As the GPS and INS are physically located in two different places, we also need to know the shift vector between the GPS and INS.

Remote sensing dynamic monitoring of land use can detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management to land resources. This paper discussed the technological procedure of land use dynamic monitoring, including the process of remote sensed images, the information classification and extraction of remote sensed imagery, and analysis of land use changes. Based on SPOT imagery data in three periods, the paper took Beijing city as an example, extracted the land use information during 1986-2004, and the land use changes were required in the period. The object-oriented method was used to extract information, and contrastive method after classification was used to confirm change zones.

Vegetation cover is of great significance in understanding climate change process due to its vital role in controlling water and carbon cycles. The properties of vegetation's surfaces are usually estimated by remotely sensed data through regression models or physical-based models, which simulates the interactions of solar radiation with the vegetation medium. In real domain, the spectral responses measured by the sensor in forested area are strongly influenced by the different understory natural conditions that limit the possibility of applying both retrieval methods to predict overstory vegetation parameters. Understory information is therefore needed for estimating trees' parameters; moreover from a biodiversity point of view and perspective of forest management, understory represents a critical component of forest ecosystem that needs a better characterization. An experiment has been conducted using hyperion and panchromatic QuickBird data to explore the status of different vegetation's understory under a sparse forest in the Longmenhe Nature Reserve, China. Understory vegetation information of study area is classified into five classes. The novel aspect of the method is the integration of spectral (hyperspectral) domain fusion and spatial domain fusion techniques within a multi-layer perceptron artificial neural network model. Real data from the experiment on a limited ground as well as hyperion and QuickBird data are used as input dataset. A nonlinear artificial neural network achieved a classification accuracy of 80% despite the presence of co-occurring mid-story and understory vegetation. The achieved results show that this method is able to identify the different vegetation information under the tree canopy. Our studies suggest that it is necessary to incorporate the geographic and vegetation community prior information to further improve the accuracy in order to monitor understory vegetation.

Soil erosion is a major environmental problem worldwide, threatening the human sustainable development. Global water erosion and wind erosion affect 1094 and 549 Mha, respectively. Soil erosion concerns multi factors, for example, land cover, climate, vegetation cover, topographic factors. Soil erosion is also different in different spatial and temporal scales. To monitor and assess the extent of soil erosion, multi data concerning these influencing elements need to be considered by combined use. Among these factors that influent the process of soil erosion, vegetation cover and slope steepness are selected. The vegetation cover data in the Chengdu plain have been estimated from normalized difference vegetation index derived from Landsat-7 ETM+ image acquired at 2000-11-02. Slope steepness is computed based on the pixels of DEM (Digital Elevation Model), the pixels of DEM are transformed from the 1:100000 terrain map. Vegetation cover and topographic factor can be combined as a cross tab model. A soil erosion risk map with six grades can be drawn. Using the method the probability and the extent of soil erosion can be measured. Remote sensing data provide a significant information source for mapping, monitoring and predicting current rapid soil erosion. In the analysis process of soil erosion monitoring geography information system takes very important roll. It can fuse different thematic data and different formats of data. This paper gives a brief synthesis of the information obtainable from remote-sensing data and DEM data, and assesses the status of soil erosion in Chengdu plain.

North piedmont of Yinshan mountain is a typical ecological fragile zone and an important eco-shelter in north China. It is very important to study impact of global change in this area in order to monitor and analyze dynamics of vegetation cover. This paper illustrates the application of a local variance technique to assess vegetation cover change in Yinshan Mountain using integrated growing season NDVI measurements. The paper calculated seasonal integrated normalized difference vegetation index (NDVI) for each of 8 years using a time-series of 1-km data from SPOT Vegetation (1998-2005) sensors. Based on the data, we can construct the smoothed NDVI time-series to locate the onset and end of the growing season of vegetation. Then we can calculate the integrated NDVI (iNDVI) as the area under the NDVI curve from the start of season to the end of season. The paper uses a local variance method to detect local spatial anomalies of iNDVI for study area. We summarized the number of years that a given pixel was identified as an anomaly. The resulting anomaly maps were analyzed using Landsat ETM+ imagery and extensive ground knowledge to assess the results. The local variance analysis is a reliable method for assessing vegetation cover change from human pressures or increased land productivity from natural resource management practices. The result provides a good support for analyzing relation between vegetation change and climatic factors.

Crop growth monitoring is very important in agriculture resource management. Crop growth monitoring could provide crop state information for the decisive maker and reflect variety information of crop yield in time. The index of crop growth monitoring has closely relation with crop yield, which could as early as forecast large-scale food state that possibility missing or surplus. Therefore, there are important meanings to the macro control for food. Traditionally, the monitoring of crop growth and yield forecasts are made on the basis of samples by field visits or written inquiries. On national scale, the processing of these sample data is an expensive and time-consuming procedure. Recent developments in remote sensing technologies have created promising opportunities for monitoring agricultural crop growth. The moderate resolution imaging spectroradiometer (MODIS) is one detector board on Terra's (EOS-AMI), which was lunched on December 18, 1999 by NASA. It offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop growth monitoring. The paper studied the method of crop growth monitoring based on data of MODIS/TERRA vegetation indices. Results from the study not only monitor the crop growth in investigation area, but also illustrate the powerful potential to provide information about crop growth based MODIS VI data.

Crop acreage monitoring is basic information necessary for wise management of plant natural resources. Recent developments in remote sensing technologies have created promising opportunities for improving agricultural statistics systems. The Moderate Resolution Imaging Spectroradiometer (MODIS) is one detector board on Terra's (EOS-AM1), which was lunched on December 18, 1999 by NASA. It offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop acreage estimating. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation, especially in China. This paper developed and tested an unmixing approach with MODIS data that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. A zone that can get LANDSAT/TM data was chosen to be train dataset in this method. The paper assumes that the crop area estimating from LANDSAT/TM data is correct; in the training zone the crop area based on MODIS data can get from the classification result of LANDSAT/TM data. Then we can extend the result to a large-scale; finally we compare the result to national statistic data. The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of crop distributions using MODIS data. I INTRODUCTION

Miyun reservoir is an important water supplier of Beijing, China, therefore soil erosion of this area is very critical and must be paid sufficient attention. To monitor and manage soil erosion information in Miyun reservoir area, a monitoring information system that is dynamic, interactive and Internet-based was developed. The paper describes how the WebGIS application was developed, implemented and used. The overall monitoring information system is multi-scale, multi-source, flexible and geographically organized. It uses an Internet-based GIS ("WebGIS") technology, and has obtained information about soil erosion in Miyun reservoir area through four methods: RUSLE model, TM images visual interpretation, model based on vegetation cover and slope, and data fusion of three results. The decision-making officers can access and analyze these data more effectively and conveniently via the Internet.

Drought is one of the major environmental disasters in north China, and it is very important to detect and monitor drought periodically at large scale for decision making. The Normalized Difference Vegetation Index (NDVI) has been widely used to monitor moisture-related vegetation condition. To better understand the relationship between vegetation vigor and moisture availability, the Normalized Difference Water Index (NDWI) was calculated in addition to the NDVI. In this study, the analysis was conducted on time series compositing NDVI and NDWI of the period of ten days. With the support of land use map and soil humidity of crop for the growing season, we build the simple model of northern China for crop drought monitoring. The results of July 2002 show that the large scale temporal and spatial characteristics of drought in Northern China can be effectively detected by this way. Based on this method, we have developed a operational crop drought monitoring system for whole China land areas.

With the great agriculture population and limited cropland, it is very important to estimate the output of grain produce in China by remote sensing technology. However, the smallholders of cropland can plant what they like, thus it is difficult to monitor the crop planting proportion with only RS images, even with IKONOS/QUICKBIRD data. In GVG agro-status sampling system, the video camera connected with a notebook by a video capture card and GPS receive card are integrated into the GIS environment. GVG is fixed on a motor and restore the crop pictures and their GPS data when the car is moving along the country road derived from the linear sampling frame in a plantation division unit. A great many of crop pictures along the sample lines are obtained on the field in a limited time, and then all pictures with geographical data are interpreted to calculate the ratio of each type of crop plantation. The crop proportion of plantation division unit is estimated by all picture's plantation ratio of sample line due to this unit. This literature review has demonstrated the GVG systems hardware's constitute, working principle and the case studies. GVG agro-status sampling system not only can be acquire every crop's planting proportion of large areas in short time, but also can check up the results, which get from remote sensing crop classification.

Miyun Reservoir is located in the northeast of Beijing and it is the most important drinking water resource of the city. Soil and water loss in this area directly affects local eco-environment and people's life. The soil and water conservation project has been launched out to combat the degrading environment in the upper reach of Miyun Reservoir basin. A ruled-based approach based on the objective of catchments, which is the unit of most soil conservation project, was applied to evaluate soil loss. For each heterogeneous hilly valley in the study area, a set of knowledge-based rules was formulated with remotely sensed images, land use map, DEM and ground investigated data. The relevant parameters, such as slope, vegetation fraction, ravine density, and rainfall distribution, which are also the input parameters of the widely applied universal soil loss equation (USLE), were scaling to the object properties to count this ruled-based model. Finally, all catchments were grouped into four grades according to the soil loss intensity, namely very severe, severe, moderate and slight, the result of the study was practicable to support to make the soil conservation planning.

The China Brazil Earth Resources Satellite (CBERS)-2 CCD has 4 bands of 19.5m spatial resolutions in the visible/near-infrared wavelength regions and 1 pan band of 19.5m spatial resolutions. These bands (except pan) have the same spectral zones as Landsat7 ETM+ multispectral bands. We compared the performance of CCD image with ETM image from 4 key aspects in order to accelerate its application for agriculture monitoring. These 4 key aspects are geometric correction, typical surface features identification, land target area measurement, image classification and interpretation. The results show that CCD image can be geometrically corrected with high accuracy, is better than ETM+ image for typical surface features identification, records the small land targets in detail, can be more suit for recognition by eyes, can be used for measuring land targets area with high accuracy, has the same good performance as ETM image for image classification and interpretation. CBERS-2 CCD shows great potential for the applications of agricultural monitoring.

By interactive interpretation method with the assistance of GIS and RS, soil and water loss classification information in the upper basin of Miyun reservoir was derived from Landsat Enhanced Thematic Mapper (ETM) images and other relevant datum. The ecological and environmental background database was built in the region, which consists of the controlling factors, such as vegetation fraction, ravine density, isohyet map, to the intensity of soil and water loss. Although these factors, directly or indirectly influencing the process of soil erosion, are the key parameters in the widely applied revised universal soil loss equation (RUSLE), they may have the different contribution to the soil erosion at the diverse spatial scale. The correlation between these environmental factors and soil erosion intensity was researched at a regional scale in This work. The purpose of the paper is to bring the reference of the case study for the application of the revised universal soil loss equation in the same region, as well as to verify these factors data, and the veracity of the result of RUSLE application.

The memory organization of Radix-4 FFT is considered. The new memory addressing assignment allows simultaneously access to all the data needed for butterfly calculation. The advantage of this memory addressing lies in the fact that it reduces the delay of address generation to one fourth of the existed

The ability to map and monitor the spatial extent of the built environment, and associated temporal changes, has important societal and economic meaning. In This work, the high spatial resolution of the image - Quick Bird was used to create a detailed land cover maps of Taigu region, Shanxi province, China. Adopting object-oriented image segmentation and classification which is based on fuzzy logic allows the integration of a broad spectrum of different object features, such as spectral values, shape and texture. In this study we use not only image object's attributes, but also the relationship between networked image objects, it can perform sophisticated classification and get satisfied classification result. The aim of this work was to develop an object-oriented segmentation and classification approach for operational land cover mapping.

In China, crop structure adjustment policy has brought great change of different breed's planting area in different years. Government managers of agricultural industry need timely crop structure information to monitor the performance of the crop structure adjustment policy. The objective of this research was to evaluate the synergistic effects of multitemporal RADAR SAT synthetic aperture radar (SAR) and Land sat ETM+ data for extracting agricultural crops structure using an object-oriented classification approach. This work instructs and analyses the crop structure near the Kaifeng city area in 2002. Four crop types were extracted: corn, soybean, cotton, and peanut. With the object-oriented classification approach, the overall accuracy of crop structure extracting from two-date F5 mode's SAR data (mid- to last-season) and two-date Land sat ETM+ is over 90%.

The process of soil erosion is complex. Among these factors that influence the process of water and soil erosion, vegetation cover and slope steepness are selected. The vegetation cover data in the Upper Basin of Miyun Reservoir have been estimated from normalized difference vegetation index derived from Landsat-7 ETM+ images. Based on the pixel of DEM slope, steepness is computed. Vegetation cover and topographic factor can be combined as a crosstab model. A soil erosion risk map with six grades can be drawn. Using the method the probability and the extent of soil loss can be measured. This information is very useful to watershed manager. This case study describes and assesses soil erosion in the Upper Basin of Miyun Reservoir

With the assistance of GIS and RS technology, it is extremely crucial to manage the huge volume, multisource and multiscale spatial data for the design and development of geographic information system. The database and its client management system have been developed to act as a data management and integrating platform in the soil and water loss monitoring system in the upper basin of Miyun reservoir. This work expounded the detail about the design technique and framework of the database in practice, and focused on manipulating the huge volume spatial data management, which was based on the software of Oracle9i and ArcSDE version 8.2 and the support of object-oriented technology. The paper approached to put forward a feasible and practical technology project reference for the construction of the applied GIS on the aspect of system database construction, and especially for which needs to manage both spatial data and nonspatial data.