Download or read book Use of Preclassification Image Masking to Improve the Accuracy of Wetland Mapping Undertaken in Support of Statewide Land Cover Classification written by David E. Nagel and published by . This book was released on 1995 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Water resources Investigations Report written by and published by . This book was released on 1997 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Combining Satellite Data with Ancillary Data to Produce a Refined Land use land cover Map written by J. S. Stewart and published by . This book was released on 1998 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Assessment of Alternative Methods for Stratifying Landsat TM Data to Improve Land Cover Classification Accuracy Across Areas with Physiographic Variation written by Jana S. Stewart and published by . This book was released on 1994 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Effect of Spatial and Spectral Resolution on Automated Wetland Classification written by Juliet Marie Landa and published by . This book was released on 1998 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Analysis of Spaceborne Synthetic Aperture Radar Images to Assist in Statewide Land Cover Mapping and Long term Ecological Research written by Jonathan Ward Chipman and published by . This book was released on 1996 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Knowledge based Approach of Satellite Image Classification for Urban Wetland Detection written by Xiaofan Xu and published by . This book was released on 2014 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has been a technical challenge to accurately detect urban wetlands with remotely sensed data by means of pixel-based image classification. This is mainly caused by inadequate spatial resolutions of satellite imagery, spectral similarities between urban wetlands and adjacent land covers, and the spatial complexity of wetlands in human-transformed, heterogeneous urban landscapes. Knowledge-based classification, with great potential to overcome or reduce these technical impediments, has been applied to various image classifications focusing on urban land use/land cover and forest wetlands, but rarely to mapping the wetlands in urban landscapes. This study aims to improve the mapping accuracy of urban wetlands by integrating the pixel-based classification with the knowledge-based approach. The study area is the metropolitan area of Kansas City, USA. SPOT satellite images of 1992, 2008, and 2010 were classified into four classes -- wetland, farmland, built-up land, and forestland -- using the pixel-based supervised maximum likelihood classification method. The products of supervised classification are used as the comparative base maps. For our new classification approach, a knowledge base is developed to improve urban wetland detection, which includes a set of decision rules of identifying wetland cover in relation to its elevation, spatial adjacencies, habitat conditions, hydro-geomorphological characteristics, and relevant geostatistics. Using ERDAS Imagine software's knowledge classifier tool, the decision rules are applied to the base maps in order to identify wetlands that are not able to be detected based on the pixel-based classification. The results suggest that the knowledge-based image classification approach can enhance the urban wetland detection capabilities and classification accuracies with remotely sensed satellite imagery

Download or read book Advanced Machine Learning Algorithms for Canadian Wetland Mapping Using Polarimetric Synthetic Aperture Radar PolSAR and Optical Imagery written by Masoud Mahdianpari and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Wetlands are complex land cover ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remote sensing data are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. The ability to extract useful information from satellite imagery greatly affects the accuracy and reliability of the final products. This is of particular concern for mapping complex land cover ecosystems, such as wetlands, where complex, heterogeneous, and fragmented landscape results in similar backscatter/spectral signatures of land cover classes in satellite images. Accordingly, the overarching purpose of this thesis is to contribute to existing methodologies of wetland classification by proposing and developing several new techniques based on advanced remote sensing tools and optical and Synthetic Aperture Radar (SAR) imagery. Specifically, the importance of employing an efficient speckle reduction method for polarimetric SAR (PolSAR) image processing is discussed and a new speckle reduction technique is proposed. Two novel techniques are also introduced for improving the accuracy of wetland classification. In particular, a new hierarchical classification algorithm using multi-frequency SAR data is proposed that discriminates wetland classes in three steps depending on their complexity and similarity. The experimental results reveal that the proposed method is advantageous for mapping complex land cover ecosystems compared to single stream classification approaches, which have been extensively used in the literature. Furthermore, a new feature weighting approach is proposed based on the statistical and physical characteristics of PolSAR data to improve the discrimination capability of input features prior to incorporating them into the classification scheme. This study also demonstrates the transferability of existing classification algorithms, which have been developed based on RADARSAT-2 imagery, to compact polarimetry SAR data that will be collected by the upcoming RADARSAT Constellation Mission (RCM). The capability of several well-known deep Convolutional Neural Network (CNN) architectures currently employed in computer vision is first introduced in this thesis for classification of wetland complexes using multispectral remote sensing data. Finally, this research results in the first provincial-scale wetland inventory maps of Newfoundland and Labrador using the Google Earth Engine (GEE) cloud computing resources and open access Earth Observation (EO) collected by the Copernicus Sentinel missions. Overall, the methodologies proposed in this thesis address fundamental limitations/challenges of wetland mapping using remote sensing data, which have been ignored in the literature. These challenges include the backscattering/spectrally similar signature of wetland classes, insufficient classification accuracy of wetland classes, and limitations of wetland mapping on large scales. In addition to the capabilities of the proposed methods for mapping wetland complexes, the use of these developed techniques for classifying other complex land cover types beyond wetlands, such as sea ice and crop ecosystems, offers a potential avenue for further research.

Download or read book Subpixel Mapping for Remote Sensing Images written by Peng Wang and published by CRC Press. This book was released on 2022-12-15 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Subpixel mapping is a technology that generates a fine resolution land cover map from coarse resolution fractional images by predicting the spatial locations of different land cover classes at the subpixel scale. This book provides readers with a complete overview of subpixel image processing methods, basic principles, and different subpixel mapping techniques based on single or multi-shift remote sensing images. Step-by-step procedures, experimental contents, and result analyses are explained clearly at the end of each chapter. Real-life applications are a great resource for understanding how and where to use subpixel mapping when dealing with different remote sensing imaging data. This book will be of interest to undergraduate and graduate students, majoring in remote sensing, surveying, mapping, and signal and information processing in universities and colleges, and it can also be used by professionals and researchers at different levels in related fields.

Download or read book Development Improvement and Assessment of Image Classification and Probability Mapping Algorithms written by Qing Wang and published by . This book was released on 2018 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remotely sensed imagery is one of the most important data sources for large-scale and multi-temporal agricultural, forestry, soil, environmental, social and economic applications. In order to accurately extract useful thematic information of the earth surface from images, various techniques and methods have been developed. The methods can be divided into parametric and non-parametric based on the requirement of data distribution, or into global and local based on the characteristics of modeling global trends and local variability, or into unsupervised and supervised based on whether training data are required, and into design-based and model-based in terms of the theory based on which the estimators are developed. The methods have their own disadvantages that impede the improvement of estimation accuracy. Thus, developing novel methods and improving the existing methods are needed. This dissertation focused on the development of a feature-space indicator simulation (FSIS), the improvement of geographically weighted sigmoidal simulation (GWSS) and k-nearest neighbors (kNN), and their assessment of land use and land cover (LULC) classification and probability (fraction) mapping of percentage vegetation cover (PVC) in Duolun County, Xilingol League, Inner Mongolia, China. The FSIS employs an indicator simulation in a high-dimensional feature space and expends derivation of indicator variograms from geographic space to feature space that leads to feature space indicator variograms (FSIV), to circumvent the issues existing in traditional indicator simulation in geostatistics. The GWSS is a stochastic and probability mapping method and considers a spatially nonstationary sample data and the local variation of an interest variable. The improved kNN, called Optimal k-nearest neighbors (OkNN), searches for an optimal number of nearest neighbors at each location based on local variability, and can be used for both classification and probability mapping. Three methods were validated and compared with several widely used approaches for LULC classification and PVC mapping in the study area. The datasets used in the study included a Landsat 8 image and a total of 920 field plots. The results obtained showed that 1) Compared with maximum likelihood classification (ML), support vector machine (SVM) and random forest (RF), the proposed FSIS classifier led to statistically significantly higher classification accuracy of six LULC types (water, agricultural land, grassland, bare soil, built-up area, and forested area); 2) Compared with linear regression (LR), polynomial regression (PR), sigmoidal regression (SR), geographically weighted regression (GWR), and geographically weighted polynomial regression (GWPR), GWSS did not only resulted in more accurate estimates of PVC, but also greatly reduced the underestimations and overestimation of PVC for the small and large values respectively; 3) Most of the red and near infrared bands relevant vegetation indices had significant contributions to improving the accuracy of mapping PVC; 4) OkNN resulted in spatially variable and optimized k values and higher prediction accuracy of PVC than the global methods; and 5) The range parameter of FSIVs was the major factor that spatially affected the classification accuracy of LULC types, but the FSIVs were less sensitive to the number of training samples. Thus, the results answered all six research questions proposed.

Download or read book Bibliographia cartographica written by and published by . This book was released on 1996 with total page 826 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hybrid Image Classification Technique for Land cover Mapping in the Arctic Tundra North Slope Alaska written by Debasish Chaudhuri and published by . This book was released on 2008 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Remotely sensed image classification techniques are very useful to understand vegetation patterns and species combination in the vast and mostly inaccessible arctic region. Previous researches that were done for mapping of land cover and vegetation in the remote areas of northern Alaska have considerably low accuracies compared to other biomes. The unique arctic tundra environment with short growing season length, cloud cover, low sun angles, snow and ice cover hinders the effectiveness of remote sensing studies. The majority of image classification research done in this area as reported in the literature used traditional unsupervised clustering technique with Landsat MSS data. It was also emphasized by previous researchers that SPOT/HRV-XS data lacked the spectral resolution to identify the small arctic tundra vegetation parcels. Thus, there is a motivation and research need to apply a new classification technique to develop an updated, detailed and accurate vegetation map at a higher spatial resolution i.e. SPOT-5 data. Traditional classification techniques in remotely sensed image interpretation are based on spectral reflectance values with an assumption of the training data being normally distributed. Hence it is difficult to add ancillary data in classification procedures to improve accuracy. The purpose of this dissertation was to develop a hybrid image classification approach that effectively integrates ancillary information into the classification process and combines ISODATA clustering, rule-based classifier and the Multilayer Perceptron (MLP) classifier which uses artificial neural network (ANN). The main goal was to find out the best possible combination or sequence of classifiers for typically classifying tundra type vegetation that yields higher accuracy than the existing classified vegetation map from SPOT data. Unsupervised ISODATA clustering and rule-based classification techniques were combined to produce an intermediate classified map which was used as an input to a Multilayer Perceptron (MLP) classifier. The result from the MLP classifier was compared to the previous classified map and for the pixels where there was a disagreement for the class allocations, the class having a higher kappa value was assigned to the pixel in the final classified map. The results were compared to standard classification techniques: simple unsupervised clustering technique and supervised classification with Feature Analyst. The results indicated higher classification accuracy (75.6%, with kappa value of .6840) for the proposed hybrid classification method than the standard classification techniques: unsupervised clustering technique (68.3%, with kappa value of 0.5904) and supervised classification with Feature Analyst (62.44%, with kappa value of 0.5418). The results were statistically significant at 95% confidence level."--Abstract from author supplied metadata.

Download or read book Google Earth Engine Applications written by Lalit Kumar and published by MDPI. This book was released on 2019-04-23 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

Download or read book Remote Sensing of Impervious Surfaces written by Qihao Weng and published by CRC Press. This book was released on 2007-10-03 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote sensing of impervious surfaces has matured using advances in geospatial technology so recent that its applications have received only sporadic coverage in remote sensing literature. Remote Sensing of Impervious Surfaces is the first to focus entirely on this developing field. It provides detailed coverage of mapping, data extraction,

Download or read book Land Use and Land Cover Change written by Eric F. Lambin and published by Springer Science & Business Media. This book was released on 2008-01-08 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent estimates on the rate of change of major land classes. Aggregated globally, multiple impacts of local land changes are shown to significantly affect central aspects of Earth System functioning. The book offers innovative developments and applications in the fields of modeling and scenario construction. Conclusions are also drawn about the most pressing implications for the design of appropriate intervention policies.

Download or read book The Canadian Wetland Classification System written by and published by . This book was released on 1987 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: A classification system for Canadian wetlands based on the collective expertise and research of scientists across Canada. The system is provisional and subject to revision in future editions.

Download or read book Remote Sensing Digital Image Analysis written by John A. Richards and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the widespread availability of satellite and aircraft remote sensing image data in digital form, and the ready access most remote sensing practitioners have to computing systems for image interpretation, there is a need to draw together the range of digital image processing procedures and methodologies commonly used in this field into a single treatment. It is the intention of this book to provide such a function, at a level meaningful to the non-specialist digital image analyst, but in sufficient detail that algorithm limitations, alternative procedures and current trends can be appreciated. Often the applications specialist in remote sensing wishing to make use of digital processing procedures has had to depend upon either the mathematically detailed treatments of image processing found in the electrical engineering and computer science literature, or the sometimes necessarily superficial treatments given in general texts on remote sensing. This book seeks to redress that situation. Both image enhancement and classification techniques are covered making the material relevant in those applications in which photointerpretation is used for information extraction and in those wherein information is obtained by classification.