最新刊期
卷 21 ， 期 3 ， 2017
- 摘要：Land Use and Land Cover change (LUCC) is a main research subject of global environmental change and sustainable development. LUCC is a complex and dynamic process that involves both natural and human systems. Land use simulation models are powerful tools for understanding the driving forces of LUCC, for supporting urban planning and decision making, and for providing important information to evaluate the ecosystem effects of LUCC. Land use dynamic systems are difficult to predict through traditional " top–down” models. The Cellular Automata (CA) model and Agent-Based Model (ABM) are " bottom–up” approaches that have attracted increasing attention as powerful modeling tools in simulating land use patterns and evolution processes. In a CA model, each cell has a finite number of states, which can represent land use or land cover types. Changes in individual cells are defined by transition rules and generate the macro pattern of land use changes. The CA model has outstanding advantages in simulating the natural driving factors of land use dynamics. However, the influences of human factors are difficult to represent in a CA model. ABM, by contrast, can reflect the decisions and behaviors of individuals, such as government, investors, and residents. In an ABM, agents can move and interact with each other and with the environment. These local interactions generate certain land use patterns on the global scale. Thus, the two models have distinct advantages in modeling land use dynamics. Currently, the development of the CA and ABM models has achieved several important breakthroughs. This paper summarizes the recent progress in land use simulation models from the perspective of theory and methodology, including scale sensitivity, CA transition and ABM behavior rules, and coupled CA and ABM models. This paper also outlines the applications of these models in virtual city simulation and theoretical verification, realistic city simulation and scenario prediction, multitype land use and cover simulation, and decision support. However, the current land use models have obvious limitations on some crucial issues, such as fine and large-scale simulation. Hence, this paper discusses these problems and proposes the inclusion of three-dimensional modeling, big data, and rule-mining for fine simulation, as well as large-scale simulation and knowledge transfer, in future studies.关键词：Land Use/Land Cover change;progress modelling and simulation;Cellular Automata (CA);multi-agent system (ABM);scale
- 673
- |
- 319
- |
- 44
发布时间：2021-06-07 -
摘要：Soil moisture measurements are important to hydrology, climatology, and agriculture. Global Navigation Satellite System reflectometry (GNSS-R) is a new and powerful tool for sensing soil moisture. Different methods have been proposed for the reception and processing of reflected signals, such as multiantenna and single-antenna patterns. Considerable efforts have been made on ground-based and airborne observations. Meanwhile, a number of space-borne missions have also been implemented. During the suitability analysis of soil moisture remote sensing via GNSS-R technique, three key factors have to be determined—the specular reflection point, spatial pixel size, and sensing depth in the soil—regardless of the extraction methods of the reflected signals or the types of the observation platform. However, no relatively comprehensive explanations are available in the current literature. Theoretical analysis and formula derivation are conducted to systematically and quantitatively determine the extent of soil detection in three dimensions from the abovementioned aspects. First, the geographical position of the specular reflection point on the WGS84 ellipsoid surface is determined with an iterative algorithm. The effect of elevation between the real ground surface and the ellipsoid is discussed. Second, the first Fresnel zone is defined as the spatial pixel of the GNSS-R technique. The size of the pixel is regarded as the spatial resolution based on the concept of the equi-delay ellipse. Thirdly, the penetration distance of GNSS signals in soil is expressed. The concept of sensing depth is proposed on penetration distance and is based on the theory of microwave remote sensing. This concept makes soil moisture detection more describable and comparable. To further analyze the feasibility of soil moisture remote sensing with GNSS-R, the results of two application scenarios are shown: (1) A ground-based GPS measurement was performed in Marshall, Colorado, US, from the Plate Boundary Observatory. This measurement corresponds to the single-antenna pattern. The relative location of the specular reflection points, average area of the first Fresnel ellipse clusters, and sensing depth of the time-series soil moisture are analyzed. (2)An airborne GNSS-R experiment was conducted in Zhengzhou to obtain soil moisture content. This measurement corresponds to the multiantenna pattern. The spatial distribution of estimated soil moisture with a certain resolution based on flight tracks and relevant sensing depth are manifested. The results of the ground-based GPS measurement and airborne GNSS-R experiment are reasonable and convincing. Given that this study determined the extent of detected soil in three dimensions, this work provides theoretical basis for the precision evaluation of retrieved soil moisture. BeiDou mainly differs from GPS in the carrier frequency for remote sensing using GNSS reflected signals. Therefore, the results of this study provide references for future development of the BeiDou-R technique in China.关键词：GNSS-R;soil moisture;specular reflection point;resolution;sensing depth- 286
- |
- 142
- |
- 10
发布时间：2021-06-07 -
摘要：The rapid decrease in Arctic sea ice makes normalized commercial shipping through Arctic passages possible. The accuracy of Sea-Ice Concentration (SIC) data is a crucial basis for Arctic shipping. Filed SIC data, however, is difficult to acquire. Passive microwave (PM) satellite is an efficient tool for obtaining large-scale SIC. Unfortunately, satellite SIC in the Arctic can only be evaluated with limited field observation data. Ship-based sea ice concentration observations (OBS-SIC) have been collected in the Antarctic to evaluate PM satellite sea-ice concentration (PM-SIC) (Worby, et al., 1999). In this paper, seven PM-SIC datasets that were released by Bremen University, NSIDC, and EUMETSAT were compared and assessed using ship-based OBS-SIC during the 5th CHINARE Arctic Northeast Passage cruise from July to September 2012. A total of 604 OBS-SIC pairs that were obtained from approximately 20 days of the cruise is evaluated. We selected another 604 SIC pairs from PM-SIC datasets based on the same OBS-SIC latitude and longitude. To avoid bias from daily sea ice changes and different spatial resolutions, the daily mean PM-SIC and OBS-SIC for comparison is calculated using a method that is based on a similar evaluation work in Antarctica (Beitsch, et al., 2015). MODIS images are also used to evaluate the sea ice distribution near the continent, narrow strait, and island. Results show that the seven satellite datasets have a similar pattern of large sea ice distribution, but have dissimilar patterns near the continent, island, and strait. MASAM successfully detected the small ice floe area near Poluostrov Taymyr on July 25, 2012, and near Ostrov Vrangelya on September 1, 2012, whereas other methods failed to do so. Latitude mean comparisons demonstrate that the seven PM-SIC have highly similar abilities to detect that the grid was completely water or sea ice, but highly differed in the detected percentage of sea ice in the ice grid. Quantitative evaluation via OBS-SIC comparison indicatesthatAMSR2/ASI, AMSR2/Bootstrap, SSMIS/ASI, and SSMIS/Bootstrap performed well, whereas SSMIS/NT and MASAM performedbadly.AMSR2/ASI has the lowest bias of 1% and root-mean-square error (RMSE) of 11%. However, SSMIS/NT largely underestimates the SIC with a mean bias of –15% and RMSE of 21%. AMSR2/ASI has a higher spatial resolution than the well-performing group. More importantly, it is updated near real time with only a delay of one day. High resolution and timely updates are the most important factors for operational ice service, which make AMSR2/ASI the best choice as areal-time shipping guide. High-resolution MASAM (4 km) can detect small sea ice distribution near the continent and narrow strait. Therefore, it is the most suitable for sea ice area and should be used for further studies in special regions. However, near-real-time higher-resolution AMSR2/ASI (6.25 km)has a smaller bias and RMSE with OBS-SIC. Hence, it is the best dataset for SIC quantity studies and real-time shipping guide.关键词：arctic northeast passage;passive microwave;sea ice concentration;ship-based observation;data assessment- 387
- |
- 156
- |
- 16
发布时间：2021-06-07 -
摘要：Thermal radiation directionality is a key factor that affects the accurate retrieval of land surface temperature from remote sensing images. To understand the characteristics and law of thermal radiation directionality of a natural forest background, multi angular observations of directional brightness temperatures (DBT) were conducted in both summer and autumn on herbaceous vegetation and partially bare soil in a natural forest in northeast China. A FLIR T420 thermal imager was used to capture DBTon the east–west and north–south planes at 10:30 AM and 14:30 PM of each day. The View Zenith Angle (VZA) changed from –50° to 50°. Then, time effects on DBT were removed with a linear correction method during observations using continuous thermal images that were taken by FLIR i7 in the nadir direction. (1) The brightness temperature of then atural forest background had a visible directional affect, which is related to the ground vegetation structure and cannot be omitted. (2) In the summer, the forest background was mostly covered by herbaceous vegetation and shaded by dense tree crowns, which account for the small variations in small DBT (1—1.5 ℃). The highest DBT was observed at VZA of 40° in the east for 10:30 AM (solar zenith angle (SZA), 30.5±0.5°;solar azimuthal angle (SAA), 149±1°) and in the west for 14:30 PM (SZA, 43.5±1.5°; SAA, 247.5±1.5°). (3) In autumn, the forest background was covered by a 10-cm thick litter layer. DBT is 1 ℃ higher than those in the summer. The maximum DBT appeared at VZA of 50° in the east for 10:30 AM (SZA, 57.5±0.5°; SAA, 169±2°) and in the west for 14:30 PM (SZA, 73±1°; SAA, 231±1°). (4) Although forest age influenced DBT, the seasonal effects were more significant. The directional effects of the brightness temperature of the forest background may contribute to pixel-scale DBT, which should not be neglected in future studies.关键词：thermal radiation directionality;natural forest;under storey;time effect- 312
- |
- 229
- |
- 3
发布时间：2021-06-07 -
摘要：Image change detection uses an algorithm that is based on multiscale analysis and Markov Random Field (MRF) model. The algorithm is widely used owing to time–frequency wavelet characteristics and the good expression to spatial correlation of the MRF model. To address the significant loss of high-frequency information during noise reduction and pixel independence in change detection of multiscale remote sensing images, this paper proposes an unsupervised change detection algorithm based on the combined dual-tree complex wavelet transform (DT-CWT) and MRF. The algorithm can be divided into the following steps. First, to enhance detail expression and objective image edges, the difference image is decomposed on a multiscale by DT-CWT. Second, the change characteristics in high-frequency regions are extracted by setting the high-frequency components of the first layer to zero and performing MRF segmentation to the other levels. Third, the high- and low-frequency sub-bands of each layer are reconstructed, and the maximum a posteriori probability is estimated by the Iterative Condition Model (ICM) based on the K-means segmentation algorithm, which fully obtains the correlation between pixels. Finally, the segmentation results of each layer are fused to obtain the mask of the final change detection result. To verify the effectiveness and stability of the proposed algorithm, the DT-CWT–Bayes, MRF–Bayes, DWT-MRF–Bayes methods, and the proposed algorithm are comparatively tested and analyzed. The contrast experiment proves that compared with the other methods, the proposed method produces change detection results for edges that subjectively look smoother and more delicate with less noise. In addition, as shown in the table of evaluation indices of the four change detection methods, the proposed method has the least total number of errors and the highest accuracy rate. Thus, the proposed method balances the reduction of tiny spots and noise and the retention of high frequency information. Moreover, the proposed method has high precision for change detection and predominant robust properties. The proposed algorithm fully uses the multidirectional expression, anisotropy, and multiscale properties of DT-CWT, which helps the expression and analysis of image information. In addition, the extraction of the change characteristics in high-frequency regions based on the segmentation algorithm of ICM better balances between noise reduction and retention of high frequency information. Finally, the final iteration and segmentation based on the MRF segmentation algorithm determines the correlation between pixels with considerably reduced false alarm rates while avoiding the influence of registration error. However, the proposed algorithm takes more time due to the existence of some iterative processes.关键词：change detection;DT-CWT;MRF;multi-scale decomposition- 265
- |
- 394
- |
- 2
发布时间：2021-06-07 -
摘要：The high-precision determination of aerosol models is crucial for analyzing the environmental impact of aerosols and for the remote sensing of Aerosol Optical Depth (AOD). However, the determination of aerosol type remains difficult, hence severely restricting highly accurate AOD retrieval and the application of aerosol optical products in environmental monitoring. A high-precision method for estimating aerosol models is proposed in this paper. The determination of aerosol type plays a vital role in the analysis of aerosol optical properties and is also an essential part of highly accurate AOD retrieval. Conventional methods, which utilize aerosol optical properties to determine aerosol types, are based on the relationship between the AOD of a single band and different aerosol types. However, due to the complex absorption and scattering properties of aerosols, it is difficult to obtain highly accurate aerosol types from AOD data with a single band. Hence, multiband AODs were introduced to enhance the accuracy of determining aerosol types. None the less, the current methods for estimating aerosol types with multiband AODs are iterative with complex and slow calculation processes. A new method to determine aerosol type with multiband AOD data was proposed to overcome the existing difficulties of current methods. Aerosol type is determined based on a lookup table, which is built using the forward simulation of relations between the particle numbers of different aerosol types and multiband AOD data based on Mie scattering theory. AOD at the wavebands of 440, 670, 870, and 1020 nm are adopted to determine aerosol type. The method was used to estimate aerosol types with multiband AOD. To evaluate the effectiveness of the proposed method, multiband AODs are simulated and applied in the validation experiment. Dust, water-soluble, and soot aerosol types are estimated with high precision. The effects of multiband AOD error on aerosol type determination are also analyzed. Results show that the proposed method can determine aerosol types with high stability. Compared with the current real-time determination method of aerosol types, the approach proposed in this paper is fast and can be used to estimate aerosol type from pixel-scale satellite data with multiband AODs. Furthermore, this method can improve the inversion accuracy of aerosol optical thickness, as well as promote the application of aerosol optical products in environmental pollution monitoring.关键词：aerosol type;Aerosol Optical Depth (AOD);Mie-scattering;look-up table method- 320
- |
- 213
- |
- 3
发布时间：2021-06-07 -
摘要：The extraction of building outlines from high spatial resolution imagery is a key element of numerous geospatial applications and has been addressed by various approaches. However, the final extraction results are always irregular or inaccurate owing to the boundary regularization algorithm and variability of building shape. This paper proposes a new method for the regularization and vectorization of two dimensional building outlines from high spatial resolution imagery. To accomplish this task, we utilize image segmentation to detect the two main orientations (θ and θ+90°) of the building blobs. To effectively refine boundaries, we then divide the boundary points, which were obtained clockwise, into the first principal, second principal, and unknown orientation classes. Then, we use least square template matching to precisely position the edge to reduce accuracy loss. Finally, the building outlines are generated by connecting the corner points with intersecting adjacent lines. In addition to the omission buildings, experimental results confirm the ability of the presented system to effectively and steadily extract building outlines with an overall average correctness of 89%, completeness of 98%, shape accuracy of 87%, and quality of 85%. This method can be widely used in various applications. Specifically, our method can work with a relatively low-accuracy image segmentation. Therefore, it can be applied for the vector quantization of large-area building outlines. However, our method only focuses on building outlines and does not consider the internal structure of the building. In the future, more attention should be givento solve this issue.关键词：remote sensing image;building vectorization;building outline;α-expansion;template matching;regularization- 285
- |
- 159
- |
- 12
发布时间：2021-06-07 -
摘要：Massive data and diversity characteristics exert higher demands on the retrieval of remote sensing images. The feature extraction algorithm is the most outstanding factor that influences the performance of retrieval methods. Traditional feature extraction methods cause the problem of semantic gap, which occurs when the low-level feature does not perfectly reflect or match the purpose of retrieval. BOVW and multilayer cluster analysis have been proposed to solve semantic gap. However, the usability of these methods is limited given their dependence on classification or cluster algorithms and artificiality. A semi supervised deep-learning method was proposed in this paper. This method combines Sparse Auto encoder (SA) and the principle of Convolutional Neural Networks (CNNs). The method involves four steps: first, the remote sensing images are pretreated with the ZCA whitening method. Second, the feature dictionary is extracted using SA, an algorithm that deals with nonannotated data. Subsequently, the feature dictionary is utilized in image convolution following the principle of CNNs, which imitates the neural net of organisms and decreases the number of features. Average pooling is conducted after image convolution. Both convolution and pooling are implemented to reduce model complexity, thus calculating distance faster. Third, the soft max classifier categorizes remote sensing images into five classes. Lastly, the remote sensing image retrieval is sorted based on the Euclidean distance between the query image and database in the same category as the query image. Experimental results based on high-resolution remote sensing images demonstrate that the proposed method is effective and is more accurate than the methods that are based on color and texture features. Image classification before sorting speeds up retrieval by 27.6%. In addition, the semi-supervised deep learning algorithm is stable when the number of returned images increases. Given that the number of neurons in the hidden SA layer and the region size of pooling primarily affect retrieval results, this study conducted several optimization experiments on these two parameters. Moreover, the algorithm in this research performed well when the number of images in the data set and the retrieval accuracy on a larger data set increased, which are meaningful for the retrieval of massive remote sensing images. The semisupervised deep learning algorithm decreases the time of image annotation, which is an exhausting job. Unlike traditional methods that extracts the features of color, texture, and shape, the method in this study directly extracts the feature dictionary from image elements with good accuracy. Moreover, the efficiency of the proposed method is guaranteed by convolution and pooling, which reduce the feature dimension. Furthermore, our experiment proves that this algorithm performs well in retrieving high-resolution remote sensing images.关键词：remote sensing image retrieval;deep learning;sparse auto encoder;convolutional neural networks;Softmax classifier- 343
- |
- 299
- |
- 19
发布时间：2021-06-07 -
摘要：Intelligent fusion of multi-source high resolution remote sensing images is attracting more attention due to its extensive applications. To satisfy application requirements of multi-source high resolution remote sensing image fusion, this paper studies " on-demand” intelligent fusion of multi-source remote sensing images. Particularly, this study focuses on intelligent fusion of different temporal high-resolution remote sensing images at different resolutions. This paper presents a novel intelligent fusion approach to achieve automatic image source selection as well as fusion method recommendation. This research applies the decision tree algorithm to high resolution remote sensing image fusion. The decision tree is trained to build the knowledge base of fusion rules, which is off-line updated. Each training sample consists of remote sensing images, fusion methods, and the evaluation results. Besides, to solve the problem of spectral distortion cause by hyperspherical color space transform, this paper proposes a fusion method renamed as Curvelet-HCS based on the second generation Curvelet transform and hyperspherical color space transform. The main idea of Curvelet-HCS is as follows. Curvelet-HCS decompose the image into low and high frequency components and employ different fusion rules for these two components. In detail, for the low frequency component, low frequency coefficients are computed as a weighted sum of low frequency coefficients of two fusing images, where weights are defined as functions of regional standard deviation and the local direction information entropy. Similarly, for the high frequency component, the high frequency coefficients are determined by the maximum. This research experiment the Curvelet-HCS method with different sources of satellite images, including the GF-1 and GF-2 satellite multispectral and panchromatic images. To evaluate the fusion performance of the proposed approach objectively, the fused images are quantitatively analyzed based on the mean, the standard deviation, the correlation coefficient, the information entropy and the average gradient. The results show that the image has a better visual effect after fusion, and the proposed fusion method achieves better performance compared to other fusion methods including Principal Component Analysis, Gramm-Schmidt, and Hyperspherical Color Sharpening. This thesis presents an intelligent fusion method by studying the relationship among the remote sensing images, the fusion methods and the evaluation results. This fusion approach improves the level of automation and intelligence of multi-resource high resolution remote sensing image fusion. It is a beneficial exploration for researching the intelligent on-demand service pattern. Also, this paper proposes a Curvelet-HCS fusion method which can fuse more than three bands of MS image at one time. The experimental results demonstrate that the proposed method is an effective method for fusing multispectral image and panchromatic image, and obtains good fused images.关键词：high resolution;remote sensing image;intelligent fusion;decision tree;fusion method;fusion rules- 255
- |
- 352
- |
- 10
发布时间：2021-06-07 -
摘要：The traditional balloon snake model is free from the constraints of orthogonal grids compared with the topology-adaptive snake model and thus extracts water boundaries from high-resolution remote sensing images with more accuracy. However, this method cannot detect and handle topological conflict caused by islands in water. To solve this problem, an improved topology-adaptive balloon snake model, which is based on the traditional balloon snake model, is proposed. First, an original contour inside the target water body is manually set. Then, self-intersection detection is conducted each time prior to contour expansion. If holes cause topological conflict, curve deformation will be performed until the iteration stops. The data selected for this experiment were obtained from the GF-1 observing satellite images of three kinds of lakes and rivers, thus realizing the one-off accurate extraction of complex water from remote sensing images. Based on the results of our experiment, the proposed method can efficiently, correctly, and completely extract the boundaries of deeply concave regions and islands.关键词：remote sensing image;river island;boundary extraction;improved Balloon Snake;topological conflict- 311
- |
- 238
- |
- 2
发布时间：2021-06-07 -
摘要：Polarimetric Synthetic Aperture Radar (PolSAR) occupies an important place in remote sensing because it provides richer information about the targets and earth surface compared with single-channel SAR systems. However, PolSAR data is contaminated by speckle noise due to the coherent imaging mechanism, which considerably affects the accuracy of target classification and recognition. Therefore, speckle-noise filtering of PolSAR images is a crucial pretreatment. Nonlocal(NL) means compute the weights between two pixels with similar surrounding neighborhoods (known as patches) instead of two individual pixels. Considering that patches contain structural information, the NL mean filter preserves repetitive structures and performs better than other filters. The key point of the NL algorithm is the similarity criterion setting or the patch weights. This paper proposes a technique to reduce speckle noise using NL means by combining structure and homogeneity similarity. First, image heterogeneity is measured based on the distance of K distribution and is further utilized to distinguish homogeneous and heterogeneous regions. In PolSAR imagery, backscattering from point targets is significantly different from that of distributed media. Strong backscattering from point targets is caused by strong elementary scatterers within a resolution cell. They lack the typical characteristics of speckle and are not random in nature. The preservation of signatures from strong point targets and man-made structures is desired for image interpretation and other applications. In this paper, various samples are collected based on scene heterogeneity. A threshold is utilized to preserve the point and line targets. Then, a new strategy is presented to adapt to the changes in the heterogeneity of the image, which sets the weights of the NL means that were implemented between patches based on the heterogeneity coefficient. Finally, the filtered image is computed. The obtained filter is compared with the refined Lee, mean shift, NLLee, and WisNLTV filters. The qualitative and quantitative aspects of the filters were compared. To compare the ability of the filters to maintain details, corresponding areas in the enlarged span images are shown after filtering with various methods. The proposed method is significantly better on the global and local scales than the existing methods. Moreover, results of H/A/α decomposition show that the proposed method effectively converges the same scattering mechanism and retains complicated scattering mechanisms. The quantitative assessment verifies the equivalent number of looks (a measure of noise reduction), the edge-preserving index, and polarization information preservation on real images. The proposed method has improved filtering performance. The concept of accounting for the heterogeneity coefficient within the NL means algorithm is implemented. The proposed method filters adaptively based on heterogeneity. In addition, comparative results confirm the advantages of the proposed algorithm on both speckle reduction and detail preservation.关键词：polarimetric SAR;speckle;filtering;nonlocal means;heterogeneity- 288
- |
- 323
- |
- 1
发布时间：2021-06-07 -
摘要：Land surface phenology is of great significance in the fields of global change response, agricultural management, and ecological applications. However, compared with the strong demand for vegetation phenology mapping for global and regional research and applications, the development of remote sensing vegetation phenology products is slower than that of other remote sensing parameters, such as Leaf Area Index (LAI) and Vegetation Index (VI). Although Enhanced Vegetation Index (EVI) and LAI are the most widely used vegetation parameters for remote sensing phenology extraction, this study aims to compare the remote sensing phenology products derived from MODerate-resolution Imaging Spectroradiometer (MODIS) EVI and global land surface satellite product LAI in Heihe River Basin. Moreover, this study aims to assess the difference inphenology information that was extracted from EVI and LAI time series. The validity and accuracy between the MODIS global Land Cover Dynamics product (MLCD) and Universal Multi-life-cycle Phenology Monitoring Method (UMPM) product in the Heihe River Basin were compared. Validity contains the missing product rate and stability. Accuracy was assessed using representative observation sites. The sites were the basis of the two proposed indications (the mean bias and mean absolute bias) for the evaluation of remote sensing phonological metrics. Furthermore, phenology information that was extracted from EVI and LAI products was compared. Then, the variances between ground observations and phenology products were discussed in detail. The validity of UMPM is better than that of MLCD as a whole. However, in sparse vegetation that is mainly composed of shrubs and grasslands, UMPM has more missing data and lower spatial-temporal stability because its precision value is lower than that of EVI. For maize, EVI is more suitable for extracting the start of growing season, whereas LAI performs better for extracting the peak of growing season. However, field observations focus on fruit development during the later period of maize growth, whereas remote sensing phenology detection is specific to leaf development. Both MLCD and UMPM are inconsistent with ground observations after the peak of the growing season. Either MLCD or UMPM has advantages in different vegetation types and various phenological developmental stages. Uniting multisource data can improve the accuracy and validity of remote sensing phenology products. In addition, owing to the coarse spatial resolution of current remote sensing phenology products, it inevitably includes other plants within one square kilometers, which may lead to variability in phenological developmental stages and a weak relationship between remote sensing data and ground observations. Improving the spatial resolution of remote sensing phenology products is significant in the future promotion of their application and development.关键词：remote sensing phenology product;the start of growing season;the end of growing season;validation;Heihe river basin- 322
- |
- 123
- |
- 5
发布时间：2021-06-07 -
摘要：Coastline change is a major indicator of alterations in the coastalecosystem and has been the focus of attention in marine economy. In this study, the spectral angle-distance similaritygrowth model was established to address the feasibility of automatic coastline extraction HJ-1B/infrared scanner (IRS) and the instability problem of the threshold value of traditional methods that use data at different phases. This model broadens the application and increases the value of IRS. The spectralangle-distance similarity growth modelutilizes the normalized radiation values of multispectralpixels as vector elements, which measure the anglesimilarity of different multidimensional vectors in the distance space. This model also analyzes the angle-distance similarity of water sample pixels and eight neighborhood pixels through iterative calculation. The water-land boundary was determined with the regional growth of water sample pixels via similarity constraint. The analysis of channel radiometric normalization showed that normalized channel values with reflectance and maximum quantization level reflect landmark change with time. The sample similarity analysis showed that the model was suitable forwhole-phase coastline extractionby taking two time standard deviation (0.01) of similar water bodies and other landmarksas the growth threshold (0.98), and that the general accuracy was more than 80%. Verification data analysis indicated that the threshold of the angle-distance similarity growth model was stable and was unaffected by phase. Comparing the extraction results of digital image edge detection methods, such as high-pass Filter, Roberts filter, Fast Fourier Transform (FFT), and Canny algorithm, revealed that these methods caused numerous broken stripes and spots in a complicated topography area. The extraction result of the waterlineshowed a mixture of numerous cracks and gaps, which hindered the further processing of the coastline. FFT was not applicable for coastline extraction through IRS given that the influence of stripe noises. Moreover, the coastline cannot be extracted through high-pass and Roberts filters, except for Canny algorithm, when the noise of IRS B4 was normal. However, Canny algorithm cannot be used when the noise was non-normal. These algorithms extracted coastlines with multi-inland border lines, which were difficult to remove. The effective removal of inland border lines from the extraction result was a difficult problem for these algorithms. By comparison, the angle-distance similarity model can be applied to coastline extraction viaIRS sensor, which was not sensitive to the noise sensor. It can also obtain desired results when the noise of B4 was non-normal. Furthermore, the subsequent processing was simple because no inland border lines interfered. This model enhances the application value of the IRS sensor in coastline extraction. The cloud cluster covered both land- and sea-occluded spectrum information, which led to ineffective coastline extraction. Therefore, data selection is necessary before application. The coastline, which is extracted from remote sensing images in this study, only included the instantaneous waterline and not the coastline in the strict sense. Artificial coastline and bedrock shoreline can be directly used as shoreline. However, the final extraction result of gravel coastlines, mangroves, and estuary coastlines need to be modified using tide and digital elevation model data.关键词：coastline;angle-distance similarity;IRS;remote sensing- 256
- |
- 181
- |
- 6
发布时间：2021-06-07 -
摘要：Although the precision management-partition method, which is based on grid sampling and spatial interpolation, is highly precise, It has poor timeliness and high costs. Thus, this study proposes a precise management-partition method based on remote sensing images. Hongxing farm fields in the northeast agricultural reclamation area are used as the research objects. High-resolution remote sensing images of bare soil and grid sampling data in the field are utilized as data sources. This study focuses onsite-specific management zone in atypical black soil area. Management is based on the significant correlation between the spectral reflectance characteristics of bare soil and the main physical and chemical properties of black soil. In addition, the zoning result is evaluated using the soil’s physical and chemical properties and physical physiological parameters of crops. The object-oriented segmentation and spatial statistical analysis methods are utilized in this study. The following conclusions are drawn: (1) The spatial variation of soil nutrients is significantly distinct in the typical black soil field. (2) The site-specific management zone, which is based on a bare soil image and highly accurate object-oriented segmentation, enhances the differences of soil nutrients, the normalized difference vegetation indexes between zones, and the consistency in zones. (3) The two images obtained on May 20th and April 1stare processed into single image and double image partitions. The ratios of inter zone to in zone variable coefficients is 1.42, 1.39, and 7.63, which prove that the result of the site-specific management zone that is based on double images is better than those based on a single image. (4) The precision management-partition method that is based on the bare soil image and segmentation technique has strong timeliness, low cost, and good accuracy. Results of the study provide the basis for field variable fertilization, development of intelligent precision agriculture, and sustainable agricultural development.关键词：bare soil;remote sensing images;precise-management zoning;object oriented;precision agriculture- 323
- |
- 128
- |
- 6
发布时间：2021-06-07 -
摘要：The mapping of large-scale wetlands involves time-series coarse spatial resolution remote sensing data and pixel-based methods, such as the decision tree and threshold techniques. However, few studies use low spatial-resolution images(such as moderate-resolution imaging spectroradiometer (MODIS)) and object-oriented methods to extract information from large-area wetlands. Although spatial resolution has some disadvantages, the coarse spatial resolution image has high time resolution, considerable spectral information, and low cost. Therefore, the high temporal characteristics of coarse image and object-oriented method can be used to extract wetland information over a large area, such as basins and continents. In this study, the object-oriented method and time-series MODIS Enhanced Vegetation Index (EVI) data are utilized to map the wetland of the Dongting Lake Basin. The time-series MODIS EVI images are smoothed using the double logistic function fitting method of TIMESAT software package, which is based on MATLAB. Meanwhile, the phenology indices are calculated from the time-series MODIS EVI data. Subsequently, the best combination of images and optimal segmentation scale are determined with the JBh distance and Johnson index. Wetland mapping is then verified using a random tree classifier based on the segmented images. In addition, validation data are derived from the visual interpretation of Landsat 8 images, Google Earth, and land-use data. To verify the classification effect of the object-oriented classification method on coarse spatial resolution images, the pixel-based method is also utilized to classify the best combination of images and is then compared with the upper method. The phenology of various ground cover types is obviously different, which indicates that they can be used to distinguish different land types, especially vegetation types. Given the image combination of the critical periods (DOY113, DOY145, DOY173, DOY193, DOY241, and DOY289) of vegetation growth and phenology (start of season and length of season), we can determine theJBh(9.143) and JM distances to meet research needs. An optimal segmentation scale parameter (15 pixels of MODIS) is obtained using the Johnson index. Based on object-oriented classification method, the overall accuracy and Kappa coefficient of the random tree classifier are 78.84% and 0.71 respectively. Compared with the object-oriented method, a pixel-based classification method with random tree classifier achieves a lower overall accuracy and Kappa coefficient of 73.05% and 0.67, respectively. In traditional pixel-based analysis, the surrounding pixels contribute a substantial proportion of signals. The object-oriented method analysis utilizes objects instead of pixels, which effectively reduces signals from surrounding pixels by integrating neighborhood information. The object-oriented classification method also reduces the " saltand pepper” effect of mapping heterogeneous landscapes and enhances the accuracy of the analysis. However, large pixels of water still exist, thus causing existing mutual fault points (368 and 228). A total of 237 and 316 pixels of sedge and reed are classified to water and 113 and 128 pixels are classified to mudflat. However, 683 and 502 pixels of paddy are misclassified into dry land and forest. We also obtained higher user accuracy for the whole wetland through the object-oriented classification technique than pixel-based classification method. Both the user and producer accuracies improved to approximately 4.56% and 6.21%, respectively. The user and producer accuracy of wetland categories were approximately 74.75%—88.03% and 78.68%—84.36%, respectively, based on the object-oriented method, which considerably increased compared with pixel-based method except for the user accuracy of the sedge wetland. On the one hand, this finding can be attributed to strong heterogeneity and mixed pixels. On the other hand, the influence of human activity, disturbance, crop prices, and national policy caused an increasingly broken patch of sedge field, thus causing misclassification. The combination of time-series MODIS EVI data and object-oriented method effectively extract wetland information on a watershed scale. It provides a new method and technique for wetland mapping on a large scale, even in a continental range.关键词：time series data;MODIS EVI data;object-oriented method;Random tree;wetland;Dongting Lake Basin- 386
- |
- 247
- |
- 34
发布时间：2021-06-07
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0