It is important to explore active faults in urban areas and their surroundings for earth- quake disaster mitigation. Satellite remote sensing techniques can play an important role in such active fault exploration. It ...It is important to explore active faults in urban areas and their surroundings for earth- quake disaster mitigation. Satellite remote sensing techniques can play an important role in such active fault exploration. It can not only reveal the pattern of active faults and active tectonics on a macroscop- ic scale, but also monitor the occurrence, development and rules of temporal-spatial evolution of active faults. In this paper, we use the Hangzhou area as an example to introduce methods of extracting de- tailed active fault information when covered by thick unconsolidated Quaternary sediment, using im- age enhancement and image fusion etc. to improve the definition and precision of satellite images and presenting a three-dimensional （3D） image to illustrate tectono-geomorphic features along the relevant faults. We have also collected aeromagnetic anomaly data, shallow seismic exploration data and dating data, and carried out field surveys to validate the characteristics of active faults based on remote sens- ing images. The results revealed about the faults showed a high consistency with traditional geological knowledge, and demonstrate that it is feasible to explore active faults in a weakly active tectonic area by using satellite remote sensing techniques and contribute to large engineering projects and research on neotectonics.展开更多
Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi...Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.展开更多
Both M_(W) 7.8 and M_(W) 7.5 earthquakes occurred in southeastern Türkiye on February 6,2023,resulting in numerous buildings collapsing and serious casualties.Understanding the distribution of coseismic surface r...Both M_(W) 7.8 and M_(W) 7.5 earthquakes occurred in southeastern Türkiye on February 6,2023,resulting in numerous buildings collapsing and serious casualties.Understanding the distribution of coseismic surface ruptures and secondary disasters surrounding the epicentral area is important for post-earthquake emergency and disaster assessments.High-resolution Maxar and GF-2 satellite data were used after the events to extract the location of the rupture surrounding the first epicentral area.The results show that the length of the interpreted surface rupture zone(part of)is approximately 75 km,with a coseismic sinistral dislocation of 2-3 m near the epicenter;however,this reduced to zero at the tip of the southwest section of the East Anatolia Fault Zone.Moreover,dense soil liquefaction pits were triggered along the rupture trace.These events are in the western region of the Eurasian Seismic Belt and result from the subduction and collision of the Arabian and African Plates toward the Eurasian Plate.The western region of the Chinese mainland and its adjacent areas are in the eastern section of the Eurasian Seismic Belt,where seismic activity is controlled by the collision of the Indian and Eurasian Plates.Both China and Türkiye have independent tectonic histories.展开更多
This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engi...This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engineering (GGE) at the University of New Brunswick (UNB), Canada. The techniques were developed by innovatively/“smartly” utilizing the characteristics of the available very high resolution optical remote sensing images to solve important problems or create new applications in photogrammetry and remote sensing. The techniques to be introduced are: automated image fusion (UNB-PanSharp), satellite image online mapping, street view technology, moving vehicle detection using single set satellite imagery, supervised image segmentation, image matching in smooth areas, and change detection using images from different viewing angles. Because of their broad application potential, some of the techniques have made a global impact, and some have demonstrated the potential for a global impact.展开更多
Forest fire is one of the main natural hazards because of its fierce destructiveness. Various researches on fire real time monitoring, behavior simulation and loss assessment have been carried out in many countries. A...Forest fire is one of the main natural hazards because of its fierce destructiveness. Various researches on fire real time monitoring, behavior simulation and loss assessment have been carried out in many countries. As fire prevention is probably the most efficient means for protecting forests, suitable methods should be developed for estimating the fire danger. Fire danger is composed of ecological, human and climatic factors. Therefore, the systematic analysis of the factors including forest characteristics, meteorological status, topographic condition causing forest fire is made in this paper at first. The relationships between biophysical factors and fire danger are paid more attention to. Then the parameters derived from remote sensing data are used to estimate the fire danger variables, According to the analysis, not only PVI (Perpendicular Vegetation Index) can classify different vegetation but also crown density is captured with PVI. Vegetation moisture content has high correlation with the ratio of actual evapotranspiration (LE) to potential ecapotranspiration (LEp). SI (Structural Index), which is the combination of TM band 4 and 5 data, is a good indicator of forest age. Finally, a fire danger prediction model, in which relative importance of each fire factor is taken into account, is built based on GIS.展开更多
With the rapid development of remote sensing technology, it has become a technical means of geological research and geological survey. Remote sensing can realize rapid and large area information extraction and provide...With the rapid development of remote sensing technology, it has become a technical means of geological research and geological survey. Remote sensing can realize rapid and large area information extraction and provide effective method for geological hazard extraction. However, due to regional differences, there are some differences between geological hazard occurrence characteristics and extraction indexes in different regions. This paper takes Linjiang City, Jilin Province as an example to extract geological hazards with multisource data. The results show that the remote sensing method can realize the extraction of debris flow, collapse, landslide and other geological hazards in the study area, and obtain high accuracy.展开更多
The ASTER (Advanced Space-borne Thermal Emission and Reflection radiometer) data, including all the 3 parts: VNIR (Visible and Near-Infrared), SWIR (Short Wave Infrared), TIR (Thermal Infrared), were applied for extra...The ASTER (Advanced Space-borne Thermal Emission and Reflection radiometer) data, including all the 3 parts: VNIR (Visible and Near-Infrared), SWIR (Short Wave Infrared), TIR (Thermal Infrared), were applied for extraction of mineral deposits, such as the Ni-Cu deposit in eastern Tianshan, the gypsum in western Tianshan, and the borax in Tibetan. This paper discusses the extraction methodology using the ASTER remote sensing data and reveals the good extraction results. This paper bravely represents the summary of the main achievement for this field by the scientists in other countries and gives a comparison with the works by others. The new achievements, described in this paper, comprise the extraction of anomalies for Ni-Cu deposit, gypsum, and borax.展开更多
The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and...The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy.Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP,SMOS,AMSR2,FY3B,and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture.We further evaluated our product,the official soil moisture products of the five satellites,and the ensemble mean (i.e.,arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province,China.The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas.The root mean square error (RMSE ) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province,respectively.Relative to the ensemble mean of the five satellite products,our product improves the accuracy by 9.1% and 57.7% in Central Tibet and Anhui Province,respectively.This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.展开更多
Flash floods in arid environments are a major hazard feature to human and to the infrastructure. Shortage of accurate environmental data is main reason for inaccurate prediction of flash flooding characteristics. The ...Flash floods in arid environments are a major hazard feature to human and to the infrastructure. Shortage of accurate environmental data is main reason for inaccurate prediction of flash flooding characteristics. The curve number (CN) is a hydrologic number used to describe the storm water runoff potential for drainage area. This study introduces an approach to determine runoff coefficient in Jeddah, Saudi Arabia using remote sensing and GIS. Remote sensing and geographic information system techniques were used to obtain and prepare input data for hydrologic model. The land cover map was derived using maximum likelihood classification of a SPOT image. The soil properties (texture and permeability) were derived using the soil maps published my ministry of water and agriculture in Saudi Arabia. These soil parameters were used to classify the soil map into hydrological soil groups (HSG). Using the derived information within the hydrological modelling system, the runoff depth was predicted for an assumed severe storm scenario. The advantages of the proposed approach are simplicity, less input data, one software used for all steps, and its ability to be applied for any site. The results show that the runoff depth is directly proportional to runoff coefficient and the total volume of runoff is more than 136 million cubic meters for a rainfall of 103.6 mm.展开更多
Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical st...Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.展开更多
It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree(MST)tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems i...It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree(MST)tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems in the traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in a complex boundary exist.By using the MST model and shape information,the object boundary and geometrical noise can be expressed and reduced respectively.Firstly,the static MST tessellation is employed for dividing the image domain into some sub-regions corresponding to the components of homogeneous regions needed to be segmented.Secondly,based on the tessellation results,the RHMRF model is built,and regulation terms considering the KL information and the information entropy are introduced into the FCM objective function.Finally,the partial differential method and Lagrange function are employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results.To verify the robustness and effectiveness of the proposed algorithm,the experiments are carried out with WorldView-3(WV-3)high resolution image.The results from proposed method with different parameters and comparing methods(multi-resolution method and watershed segmentation method in eCognition software)are analyzed qualitatively and quantitatively.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
The northern Tibet plateau is the core of generalized Qinghai—Tibet plateau. The main part of Qiangtang—Changdu massif, which is 45×10 4km 2 and more than 5000m in altitude, conforms to the northern Tibet plate...The northern Tibet plateau is the core of generalized Qinghai—Tibet plateau. The main part of Qiangtang—Changdu massif, which is 45×10 4km 2 and more than 5000m in altitude, conforms to the northern Tibet plateau in area.1 The shape features and boundary conditions of Qiangtang—Changdu massif\;(1) Qiangtang—Changdu massif shows huge flat\|lying “S” area In MSS7 mosaic image, Qiangtang—Changdu massif extends in west and east, and appears a long\|elliptic huge block composed of feathered and dendritic textures.. Noticeably, there are two similar texture “tails" in the west and east ends of the massif. The western tail turns and constringes to the north, and eastern tail to the south. Thereby, the massif shows huge “S" area. According to the regional analysis, the eastern tail locates between Shaluli Mt.\|Taniantaweng Mt. and Mujiang River, and western part through Bangong\|Co connects with Pamirs along Karakoram Mt. In regional tectonics, the massif locates between Lazhulong\|Xijinwulan\|Co\|Jinshajiang River and Bangong\|Co\|Dongqiao\|Nujiang River fault belts.展开更多
Remote sensing(RS) technologies provide robust techniques for quantifying net primary productivity(NPP) which is a key component of ecosystem production management. Applying RS, the confounding effects of carbon consu...Remote sensing(RS) technologies provide robust techniques for quantifying net primary productivity(NPP) which is a key component of ecosystem production management. Applying RS, the confounding effects of carbon consumed by livestock grazing were neglected by previous studies, which created uncertainties and underestimation of NPP for the grazed lands. The grasslands in Xinjiang were selected as a case study to improve the RS based NPP estimation. A defoliation formulation model(DFM) based on RS is developed to evaluate the extent of underestimated NPP between 1982 and 2011. The estimates were then used to examine the spatiotemporal patterns of the calculated NPP. Results show that average annual underestimated NPP was 55.74 gC·m^(-2)yr^(-1) over the time period understudied, accounting for 29.06% of the total NPP for the Xinjiang grasslands. The spatial distribution of underestimated NPP is related to both grazing intensity and time. Data for the Xinjiang grasslands show that the average annual NPP was 179.41 gC·m^(-2)yr^(-1), the annual NPP with an increasing trend was observed at a rate of 1.04 gC·m^(-2)yr^(-1) between 1982 and 2011. The spatial distribution of NPP reveals distinct variations from high to low encompassing the geolocations of the Tianshan Mountains, northern and southern Xinjiang Province and corresponding with mid-mountain meadow, typical grassland, desert grassland, alpine meadow, and saline meadow grassland types. This study contributes to improving RS-based NPP estimations for grazed land and provides a more accurate data to support the scientific management of fragile grassland ecosystems in Xinjiang.展开更多
How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classif...How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.展开更多
Indian coast harbors richly diverse and critical coastal habitats like coral reefs and mangroves. Mangroves form one of the most important ecosystems of coastal and marine areas. It safeguards the ecology of the coast...Indian coast harbors richly diverse and critical coastal habitats like coral reefs and mangroves. Mangroves form one of the most important ecosystems of coastal and marine areas. It safeguards the ecology of the coastal areas and provides livelihood opportunities to the fishermen and pastoral families living in these areas. In real sense, mangrove is the Kalpvriksh (divine tree which fulfills all the desires) for the coastal communities. The restoration and plantation of mangroves have received a lot of attentions worldwide. To assess the impact of mangrove plantation activities and to monitor the mangrove regeneration and restoration in various villages, a joint study under the Integrated Coastal Zone Management Project (ICZMP) was taken up by Gujarat Ecology Commission (GEC) and Bhaskaracharya Institute for Space Applications and Geo-Informatics (BISAG) in the Gulf of Kachchh, Gujarat State.?The major objective of this study was to monitor the increase in mangrove cover in coastal areas of Gulf of Kachchh using the Indian Remote Sensing Satellite data of 2005, 2011 and 2014. The mangrove regeneration was monitored using multi-temporal Indian Remote Sensing Satellite (IRS) LISS-III and LISS-IV digital data covering Gulf of Kachchh region. The multi-temporal IRS LISS-III data covering Gulf of Kachchh of October-2005, November-2011 and LISS-IV data of April-2014 was analyzed. The mangrove density and mangrove area in different talukas was estimated based on the analysis of IRS LISS-III digital data. The mangroves have been delineated based on the pink colour observed on satellite images and the area was estimated in the Geographic Information System (GIS) environment. The taluka or block-level mangrove areas were estimated and changes in the areas were monitored during the period of six years from 2005 to 2011. It was observed that the areas where mangrove regeneration activities were carried out with active participation of Community Based Organizations (CBOs), mangrove density as well as mangrove area have substantially increased in the Gulf of Kachchh region.展开更多
Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfe...Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem. Nonlinearity and effects from land cover type are two main problems in NPP scaling. In this paper, the contextural approach based on mixed pixels and support vector machine （SVM） algorithm are used to make the scaling model from the fine resolution （TM） to the coarse resolution （MODIS）. Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed, and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished. The result shows that the correlation between Rj_coereted of the correction factor for scale effect and 1-Fmiddle dessity grassland estimated by SVM regression model is higher （R2=0.81）. Before the correction for scale effect, the correlation between NPPMODIS and NPPTM is lower （R2=0.69; RMSE=3.47）, while the correlation between NPPTM and corrected NPPMODIS_corrected is higher （R2=0.84; RMSE= 1.87）. Therefore, NPP corrected for scale effect has been greatly improved in both correlation and error.展开更多
NOAA-AVHRR data have been more and more used by scientists because of its short temporal resolution,large scope, inexpensive cost and broad wave bands. On macro and middle scale of vegetation remote sensing, NOAAAVHRR...NOAA-AVHRR data have been more and more used by scientists because of its short temporal resolution,large scope, inexpensive cost and broad wave bands. On macro and middle scale of vegetation remote sensing, NOAAAVHRR possesses an advantage when compared with other satellites. However, because NOAA-AVHRR also problem of low resolution, data distortion and geometrical distortion, in the area of application of NOAA-AVHRR in largescale vegetation - mapping, the accuracy of vegetation classification should be improved. This paper discuss the feasibilityof integrating the geographic data in GIS(Geographical Information System) and remotely sensed data in GIS. Under theenvironment of GIS, temperature, precipitation and elevation, which serve as main factors affecting vegetation growth,were processed by a mathematical model and qualified into geographic image under a certain grid system. The geographicimage were overlaid to the NOAA-AVHRR data which had been compressed and processed. In order to evaluate the usefulness of geographic data for vegetation classification, the area under study was digitally classified by two groups of interpreter: the proposed methodology using maximum likelihood classification assisted by the geographic database and a conventional maximum likelihood classification only. Both result were compared using Kappa statistics. The indices to both theproposed and the conventional digital classification methodology were 0. 668(yew good) and 0. 563(good), respetively.The geographic database rendered an improvement over the conventional digital classification. Furthermore, in this study,some problems related to multi-sources data integration are also discussed.展开更多
One of the main concerns of physical planning is the proper designation of suitable sites for feasible and sustainable land use. A main importance of such issue is that it withdraws attention to the necessity of adopt...One of the main concerns of physical planning is the proper designation of suitable sites for feasible and sustainable land use. A main importance of such issue is that it withdraws attention to the necessity of adopting a multidisciplinary approach to the zoning and site selection problem. Egypt has a top priority objective to develop Sinai Peninsula and to create new sustainable and attracting communities that should ensure a stable, economic and sustainable environment in vast desert zones. Due to the difficulty in solving a zoning problem in a desert, the use of remote sensing and Geographic Information System (GIS) was to explore the desert potentials in the region. Five sub-models were created for five themes using Spatial Multicriteria Analysis (SMCA) and used as inputs to the final suitability model. These themes are: land resources, land stability, accessibility, cost of construction and land protection. A GIS-based model was designed following a sustainable development approach. Economic, social and environmental factors were introduced in the model to identify and map land suitable zones for urban development using Analytical Hierarchy Process (AHP). The suitability index map for urban development was produced by weighted overlay of the five sub-models themes. The most suitable zones for urban development in Sinai Peninsula amounted to 5327 square kilometers representing 17% of total area, whereas high suitable zones reached 40% indicating a high suitability of Sinai Peninsula lands for residing new urban communities.展开更多
基金supported by the Major Research Project of the Ministry of Land and Resources,China(No.1212011120887)
文摘It is important to explore active faults in urban areas and their surroundings for earth- quake disaster mitigation. Satellite remote sensing techniques can play an important role in such active fault exploration. It can not only reveal the pattern of active faults and active tectonics on a macroscop- ic scale, but also monitor the occurrence, development and rules of temporal-spatial evolution of active faults. In this paper, we use the Hangzhou area as an example to introduce methods of extracting de- tailed active fault information when covered by thick unconsolidated Quaternary sediment, using im- age enhancement and image fusion etc. to improve the definition and precision of satellite images and presenting a three-dimensional （3D） image to illustrate tectono-geomorphic features along the relevant faults. We have also collected aeromagnetic anomaly data, shallow seismic exploration data and dating data, and carried out field surveys to validate the characteristics of active faults based on remote sens- ing images. The results revealed about the faults showed a high consistency with traditional geological knowledge, and demonstrate that it is feasible to explore active faults in a weakly active tectonic area by using satellite remote sensing techniques and contribute to large engineering projects and research on neotectonics.
基金supported by National Nature Science Foundation of China (Nos. 61462046 and 61762052)Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202049 and 20161BAB204172)+2 种基金the Bidding Project of the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (Nos. WE2016003, WE2016013 and WE2016015)the Science and Technology Research Projects of Jiangxi Province Education Department (Nos. GJJ160741, GJJ170632 and GJJ170633)the Art Planning Project of Jiangxi Province (Nos. YG2016250 and YG2017381)
文摘Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
基金funded by the Basic Research Program of the Institute of Earthquake Forecasting,China Earthquake Administration(Grant Nos.CEAIEF20220102,2021IEF0505,and CEAIEF2022050502)the National Natural Science Foundation of China(Grant Nos.42072248 and 42041006)the National Key Research and Development Program of China(Grant Nos.2021YFC3000601-3 and 2019YFE0108900)。
文摘Both M_(W) 7.8 and M_(W) 7.5 earthquakes occurred in southeastern Türkiye on February 6,2023,resulting in numerous buildings collapsing and serious casualties.Understanding the distribution of coseismic surface ruptures and secondary disasters surrounding the epicentral area is important for post-earthquake emergency and disaster assessments.High-resolution Maxar and GF-2 satellite data were used after the events to extract the location of the rupture surrounding the first epicentral area.The results show that the length of the interpreted surface rupture zone(part of)is approximately 75 km,with a coseismic sinistral dislocation of 2-3 m near the epicenter;however,this reduced to zero at the tip of the southwest section of the East Anatolia Fault Zone.Moreover,dense soil liquefaction pits were triggered along the rupture trace.These events are in the western region of the Eurasian Seismic Belt and result from the subduction and collision of the Arabian and African Plates toward the Eurasian Plate.The western region of the Chinese mainland and its adjacent areas are in the eastern section of the Eurasian Seismic Belt,where seismic activity is controlled by the collision of the Indian and Eurasian Plates.Both China and Türkiye have independent tectonic histories.
文摘This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engineering (GGE) at the University of New Brunswick (UNB), Canada. The techniques were developed by innovatively/“smartly” utilizing the characteristics of the available very high resolution optical remote sensing images to solve important problems or create new applications in photogrammetry and remote sensing. The techniques to be introduced are: automated image fusion (UNB-PanSharp), satellite image online mapping, street view technology, moving vehicle detection using single set satellite imagery, supervised image segmentation, image matching in smooth areas, and change detection using images from different viewing angles. Because of their broad application potential, some of the techniques have made a global impact, and some have demonstrated the potential for a global impact.
文摘Forest fire is one of the main natural hazards because of its fierce destructiveness. Various researches on fire real time monitoring, behavior simulation and loss assessment have been carried out in many countries. As fire prevention is probably the most efficient means for protecting forests, suitable methods should be developed for estimating the fire danger. Fire danger is composed of ecological, human and climatic factors. Therefore, the systematic analysis of the factors including forest characteristics, meteorological status, topographic condition causing forest fire is made in this paper at first. The relationships between biophysical factors and fire danger are paid more attention to. Then the parameters derived from remote sensing data are used to estimate the fire danger variables, According to the analysis, not only PVI (Perpendicular Vegetation Index) can classify different vegetation but also crown density is captured with PVI. Vegetation moisture content has high correlation with the ratio of actual evapotranspiration (LE) to potential ecapotranspiration (LEp). SI (Structural Index), which is the combination of TM band 4 and 5 data, is a good indicator of forest age. Finally, a fire danger prediction model, in which relative importance of each fire factor is taken into account, is built based on GIS.
文摘With the rapid development of remote sensing technology, it has become a technical means of geological research and geological survey. Remote sensing can realize rapid and large area information extraction and provide effective method for geological hazard extraction. However, due to regional differences, there are some differences between geological hazard occurrence characteristics and extraction indexes in different regions. This paper takes Linjiang City, Jilin Province as an example to extract geological hazards with multisource data. The results show that the remote sensing method can realize the extraction of debris flow, collapse, landslide and other geological hazards in the study area, and obtain high accuracy.
文摘The ASTER (Advanced Space-borne Thermal Emission and Reflection radiometer) data, including all the 3 parts: VNIR (Visible and Near-Infrared), SWIR (Short Wave Infrared), TIR (Thermal Infrared), were applied for extraction of mineral deposits, such as the Ni-Cu deposit in eastern Tianshan, the gypsum in western Tianshan, and the borax in Tibetan. This paper discusses the extraction methodology using the ASTER remote sensing data and reveals the good extraction results. This paper bravely represents the summary of the main achievement for this field by the scientists in other countries and gives a comparison with the works by others. The new achievements, described in this paper, comprise the extraction of anomalies for Ni-Cu deposit, gypsum, and borax.
基金supported by the National Key Research and Development Program of China(Grant No.2016YFC0402701)the National Natural Science Foundation of China(Grants No.51879067 and 51579131)+4 种基金the Natural Science Foundation of Jiangsu Province(Grant No.BK20180022)the Six Talent Peaks Project in Jiangsu Province(Grant No.NY-004)the Fundamental Research Funds for the Central Universities of China(Grants No.2018842914 and 2018B04714)the China National Flash Flood Disaster Prevention and Control Project(Grant No.126301001000150068)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX18_0572)
文摘The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy.Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP,SMOS,AMSR2,FY3B,and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture.We further evaluated our product,the official soil moisture products of the five satellites,and the ensemble mean (i.e.,arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province,China.The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas.The root mean square error (RMSE ) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province,respectively.Relative to the ensemble mean of the five satellite products,our product improves the accuracy by 9.1% and 57.7% in Central Tibet and Anhui Province,respectively.This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.
文摘Flash floods in arid environments are a major hazard feature to human and to the infrastructure. Shortage of accurate environmental data is main reason for inaccurate prediction of flash flooding characteristics. The curve number (CN) is a hydrologic number used to describe the storm water runoff potential for drainage area. This study introduces an approach to determine runoff coefficient in Jeddah, Saudi Arabia using remote sensing and GIS. Remote sensing and geographic information system techniques were used to obtain and prepare input data for hydrologic model. The land cover map was derived using maximum likelihood classification of a SPOT image. The soil properties (texture and permeability) were derived using the soil maps published my ministry of water and agriculture in Saudi Arabia. These soil parameters were used to classify the soil map into hydrological soil groups (HSG). Using the derived information within the hydrological modelling system, the runoff depth was predicted for an assumed severe storm scenario. The advantages of the proposed approach are simplicity, less input data, one software used for all steps, and its ability to be applied for any site. The results show that the runoff depth is directly proportional to runoff coefficient and the total volume of runoff is more than 136 million cubic meters for a rainfall of 103.6 mm.
基金supported in part by the National Natural Science Foundation of China (61661136006 and 41371396)
文摘Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.
基金National Natural Science Foundation of China(No.41271435)National Natural Science Foundation of China Youth Found(No.41301479)。
文摘It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree(MST)tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems in the traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in a complex boundary exist.By using the MST model and shape information,the object boundary and geometrical noise can be expressed and reduced respectively.Firstly,the static MST tessellation is employed for dividing the image domain into some sub-regions corresponding to the components of homogeneous regions needed to be segmented.Secondly,based on the tessellation results,the RHMRF model is built,and regulation terms considering the KL information and the information entropy are introduced into the FCM objective function.Finally,the partial differential method and Lagrange function are employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results.To verify the robustness and effectiveness of the proposed algorithm,the experiments are carried out with WorldView-3(WV-3)high resolution image.The results from proposed method with different parameters and comparing methods(multi-resolution method and watershed segmentation method in eCognition software)are analyzed qualitatively and quantitatively.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
文摘The northern Tibet plateau is the core of generalized Qinghai—Tibet plateau. The main part of Qiangtang—Changdu massif, which is 45×10 4km 2 and more than 5000m in altitude, conforms to the northern Tibet plateau in area.1 The shape features and boundary conditions of Qiangtang—Changdu massif\;(1) Qiangtang—Changdu massif shows huge flat\|lying “S” area In MSS7 mosaic image, Qiangtang—Changdu massif extends in west and east, and appears a long\|elliptic huge block composed of feathered and dendritic textures.. Noticeably, there are two similar texture “tails" in the west and east ends of the massif. The western tail turns and constringes to the north, and eastern tail to the south. Thereby, the massif shows huge “S" area. According to the regional analysis, the eastern tail locates between Shaluli Mt.\|Taniantaweng Mt. and Mujiang River, and western part through Bangong\|Co connects with Pamirs along Karakoram Mt. In regional tectonics, the massif locates between Lazhulong\|Xijinwulan\|Co\|Jinshajiang River and Bangong\|Co\|Dongqiao\|Nujiang River fault belts.
基金supported by the international Partnership Program of the Chinese Academy of Science(Grant No.131965KYSB20160004)the National Natural Science Foundation of China(Grant No.U1803243)+1 种基金the Network Plan of the Science and Technology Service,Chinese Academy of Sciences(STS Plan)Qinghai innovation platform construction project(2017-ZJ-Y20)
文摘Remote sensing(RS) technologies provide robust techniques for quantifying net primary productivity(NPP) which is a key component of ecosystem production management. Applying RS, the confounding effects of carbon consumed by livestock grazing were neglected by previous studies, which created uncertainties and underestimation of NPP for the grazed lands. The grasslands in Xinjiang were selected as a case study to improve the RS based NPP estimation. A defoliation formulation model(DFM) based on RS is developed to evaluate the extent of underestimated NPP between 1982 and 2011. The estimates were then used to examine the spatiotemporal patterns of the calculated NPP. Results show that average annual underestimated NPP was 55.74 gC·m^(-2)yr^(-1) over the time period understudied, accounting for 29.06% of the total NPP for the Xinjiang grasslands. The spatial distribution of underestimated NPP is related to both grazing intensity and time. Data for the Xinjiang grasslands show that the average annual NPP was 179.41 gC·m^(-2)yr^(-1), the annual NPP with an increasing trend was observed at a rate of 1.04 gC·m^(-2)yr^(-1) between 1982 and 2011. The spatial distribution of NPP reveals distinct variations from high to low encompassing the geolocations of the Tianshan Mountains, northern and southern Xinjiang Province and corresponding with mid-mountain meadow, typical grassland, desert grassland, alpine meadow, and saline meadow grassland types. This study contributes to improving RS-based NPP estimations for grazed land and provides a more accurate data to support the scientific management of fragile grassland ecosystems in Xinjiang.
基金supported by the National Natural Science Foundation of China(U1435220)
文摘How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.
文摘Indian coast harbors richly diverse and critical coastal habitats like coral reefs and mangroves. Mangroves form one of the most important ecosystems of coastal and marine areas. It safeguards the ecology of the coastal areas and provides livelihood opportunities to the fishermen and pastoral families living in these areas. In real sense, mangrove is the Kalpvriksh (divine tree which fulfills all the desires) for the coastal communities. The restoration and plantation of mangroves have received a lot of attentions worldwide. To assess the impact of mangrove plantation activities and to monitor the mangrove regeneration and restoration in various villages, a joint study under the Integrated Coastal Zone Management Project (ICZMP) was taken up by Gujarat Ecology Commission (GEC) and Bhaskaracharya Institute for Space Applications and Geo-Informatics (BISAG) in the Gulf of Kachchh, Gujarat State.?The major objective of this study was to monitor the increase in mangrove cover in coastal areas of Gulf of Kachchh using the Indian Remote Sensing Satellite data of 2005, 2011 and 2014. The mangrove regeneration was monitored using multi-temporal Indian Remote Sensing Satellite (IRS) LISS-III and LISS-IV digital data covering Gulf of Kachchh region. The multi-temporal IRS LISS-III data covering Gulf of Kachchh of October-2005, November-2011 and LISS-IV data of April-2014 was analyzed. The mangrove density and mangrove area in different talukas was estimated based on the analysis of IRS LISS-III digital data. The mangroves have been delineated based on the pink colour observed on satellite images and the area was estimated in the Geographic Information System (GIS) environment. The taluka or block-level mangrove areas were estimated and changes in the areas were monitored during the period of six years from 2005 to 2011. It was observed that the areas where mangrove regeneration activities were carried out with active participation of Community Based Organizations (CBOs), mangrove density as well as mangrove area have substantially increased in the Gulf of Kachchh region.
基金Foundation： Chinese Liaoning Province Education Bureau General Science Research Project （No. L2010226） Chinese Education Ministry Humanities and Social Sciences Key Research Base Project （No.08JJD790142）+1 种基金 Chinese Liaoning Province Education Bureau Innovation Team Project （No. 2007T095） Chinese Special Funds for Major State Basic Research Project （No. 2007CB714406）.
文摘Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem. Nonlinearity and effects from land cover type are two main problems in NPP scaling. In this paper, the contextural approach based on mixed pixels and support vector machine （SVM） algorithm are used to make the scaling model from the fine resolution （TM） to the coarse resolution （MODIS）. Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed, and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished. The result shows that the correlation between Rj_coereted of the correction factor for scale effect and 1-Fmiddle dessity grassland estimated by SVM regression model is higher （R2=0.81）. Before the correction for scale effect, the correlation between NPPMODIS and NPPTM is lower （R2=0.69; RMSE=3.47）, while the correlation between NPPTM and corrected NPPMODIS_corrected is higher （R2=0.84; RMSE= 1.87）. Therefore, NPP corrected for scale effect has been greatly improved in both correlation and error.
文摘NOAA-AVHRR data have been more and more used by scientists because of its short temporal resolution,large scope, inexpensive cost and broad wave bands. On macro and middle scale of vegetation remote sensing, NOAAAVHRR possesses an advantage when compared with other satellites. However, because NOAA-AVHRR also problem of low resolution, data distortion and geometrical distortion, in the area of application of NOAA-AVHRR in largescale vegetation - mapping, the accuracy of vegetation classification should be improved. This paper discuss the feasibilityof integrating the geographic data in GIS(Geographical Information System) and remotely sensed data in GIS. Under theenvironment of GIS, temperature, precipitation and elevation, which serve as main factors affecting vegetation growth,were processed by a mathematical model and qualified into geographic image under a certain grid system. The geographicimage were overlaid to the NOAA-AVHRR data which had been compressed and processed. In order to evaluate the usefulness of geographic data for vegetation classification, the area under study was digitally classified by two groups of interpreter: the proposed methodology using maximum likelihood classification assisted by the geographic database and a conventional maximum likelihood classification only. Both result were compared using Kappa statistics. The indices to both theproposed and the conventional digital classification methodology were 0. 668(yew good) and 0. 563(good), respetively.The geographic database rendered an improvement over the conventional digital classification. Furthermore, in this study,some problems related to multi-sources data integration are also discussed.
文摘One of the main concerns of physical planning is the proper designation of suitable sites for feasible and sustainable land use. A main importance of such issue is that it withdraws attention to the necessity of adopting a multidisciplinary approach to the zoning and site selection problem. Egypt has a top priority objective to develop Sinai Peninsula and to create new sustainable and attracting communities that should ensure a stable, economic and sustainable environment in vast desert zones. Due to the difficulty in solving a zoning problem in a desert, the use of remote sensing and Geographic Information System (GIS) was to explore the desert potentials in the region. Five sub-models were created for five themes using Spatial Multicriteria Analysis (SMCA) and used as inputs to the final suitability model. These themes are: land resources, land stability, accessibility, cost of construction and land protection. A GIS-based model was designed following a sustainable development approach. Economic, social and environmental factors were introduced in the model to identify and map land suitable zones for urban development using Analytical Hierarchy Process (AHP). The suitability index map for urban development was produced by weighted overlay of the five sub-models themes. The most suitable zones for urban development in Sinai Peninsula amounted to 5327 square kilometers representing 17% of total area, whereas high suitable zones reached 40% indicating a high suitability of Sinai Peninsula lands for residing new urban communities.
