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Geospatial Assessment of Long-Term Changes (1937-2019) in Mangrove Vegetation and Shoreline Dynamics of Godavari Estuary, East Coast of India

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2023年第6期51卷 1309-1327页

作者： Roy, Snigdha Dev Trivedi, Shivam Sch Planning & Architecture Dept Phys Planning 4 Block BIndraprastha Estate New Delhi 110002 India Indian Space Res Org Reg Remote Sensing Ctr South Natl Remote Sensing Ctr ISITE Campus Bengaluru 560037 Karnataka India

Godavari mangroves are the second largest mangrove ecoregion of India located on its eastern coast. Mangroves perform major ecological functions including shoreline stabilization and coastal protection. They have undergone serious alterations, largely induced by human activities resulting in increased vulnerability to coastal flooding. A reliable database on mangroves distribution and temporal changes is essential for their effective management and conservation. Present study analyzes the long-term changes in mangroves, as well as the geomorphological changes along the coast of Godavari Estuary over a span of 82 years (1937-2019), using topographical maps, remotely sensed multi-temporal satellite imageries for five time periods. Results indicated that the rate of land uses conversions of agricultural fields, degraded areas, mudflats and mangrove vegetation into aquaculture significantly increased during the late 1990s. About 40 percent of it was constructed on mangrove areas between 1980 and 2019. The area under aquaculture was estimated to be 6,388.62 ha in 2000, almost doubling-up to 11,924.67 ha in 2019. Mangrove cover in this region increased by 15.95 percent between 2000 and 2019 (including 11.9 percent in Coringa Wildlife Sanctuary) due to increased protection, consequent regeneration and accretion. Shift in Hope Island spit was observed in north and northwest directions, gradually growing nearly 3.84 km. High-resolution declassified satellite data were found very helpful for long-term mangrove mapping and analyzing geomorphological changes;enabling a better understanding of these regions to help decision makers/planners to find better solutions to existing coastal issues. The paper concludes with some recommendations to restore and conserve the existing mangrove cover in the study area.

关键词： Mangroves Godavari Aquaculture Declassified imagery image segmentation object-based image analysis

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AN object-LEVEL MULTI-SOURCE TRANSFER LEARNING METHOD INTEGRATING OPTICAL AND SAR FEATURES: A CASE STUDY OF GAOFEN, ZIYUAN, AND SENTINEL-1 SATELLITES

AN OBJECT-LEVEL MULTI-SOURCE TRANSFER LEARNING METHOD INTEGR...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Qin, Xingli Wu, Bingfang Zeng, Hongwei Zhang, Miao Tian, Fuyou Cao, Yupei Liu, Yazhou Chinese Acad Sci Aerosp Informat Res Inst Key Lab Remote Sensing & Digital Earth Beijing 100101 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Shandong Land Grp Digital Technol Co Ltd Zaozhuang 277000 Peoples R China

ISBN: (纸本)9798350360332;9798350360325

In remote sensing earth observation applications, there is often a trade-off between automated processing and improved observation accuracy. A key issue in this contradiction is the underutilization of rich knowledge embedded in historical remote sensing data. To address this, this paper proposes an object-level multi-source transfer learning method (OMSTL) that effectively leverages the knowledge in historical remote sensing data for unsupervised land object discrimination. This method effectively integrates multi-source heterogeneous features and represents land object knowledge, enabling reliable knowledge transfer. Multi-source transfer learning utilizes knowledge from remote sensing images at different times and locations, significantly enhancing transfer stability. The method's effectiveness and potential were demonstrated through experiments using five optical remote sensing images (GF-1B, GF-1D, GF-6, GF-7, ZY-3) and their corresponding SAR images (Sentinel-1).

关键词： Unsupervised Classification Domain Adaptation Multi-Source Transfer Learning object-based image analysis Remote Sensing Big Data

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Optimizing Feature Selection for Solar Park Classification: Approaches with OBIA and Machine Learning 24th

Optimizing Feature Selection for Solar Park Classification: ...

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24th International Conference on Computational Science and Its Applications (ICCSA)

作者： Ladisa, Claudio Capolupo, Alessandra Tarantino, Eufemia Politecn Bari Dept Civil Environm Land Construct & Chem DICATEC Via Orabona 4 I-70125 Bari Italy

ISBN: (纸本)9783031652813;9783031652820

In the context of climate change mitigation, the crucial role of solar energy as an inexhaustible and renewable source is underlined by the widespread integration of photovoltaic (PV) panels. To keep track of the rapid expansion of extensive PV systems (such as solar parks), it is important to keep monitoring their quantity, distribution, and impact. Despite the many complications involved in mapping PV systems, stemming from the diversity of materials and field layouts, many researchers have focused on ways to improve this process through Remote Sensing techniques aided by Machine Learning and Deep Learning. In this study, multiple object-based image (OBIA) classification models were created for mapping large solar parks, using open-source data (Sentinel-2) and computational tools such as Google Earth Engine (GEE) and RStudio. The case study was conducted in Pavagada Solar Park, India, which is the third largest solar park in the world spanning over 153 km(2). For training, validation, and testing purposes two zones within the park were identified. Data were collected for four seasonal periods;a classification model was developed for each season while validating them. The most accurate model was then implemented in the test area. The selection of features for the Random Forest (RF) classifier was done using the Recursive Feature Elimination (RFE) method, with the main objective of eliminating nonrelevant features while preserving only the significant ones. This analysis showed a substantial improvement in accuracy, thereby validating the effectiveness of the RFE algorithm in feature selection. This is particularly true in the autumn when a maximum Overall Accuracy (OA) of 88.72% was achieved.

关键词： object-based image analysis Sentinel 2 mediumresolution satellite images photovoltaic panels

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MAPPING STONY RISE LANDFORMS WITH REMOTE SENSING AND GEOPHYSICAL DATA USING A MACHINE LEARNING APPROACH

MAPPING STONY RISE LANDFORMS WITH REMOTE SENSING AND GEOPHYS...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Fraser, Shaye Soto-Berelov, Mariela Holden, Lucas Son, Robert Hew Webb, John Jones, Simon RMIT Univ STEM Coll Melbourne Vic Australia La Trobe Univ Discipline Ecol & Environm Melbourne Vic Australia

ISBN: (纸本)9798350360332;9798350360325

Elevated areas on basalt lava flows, locally known as stony rises, are volcanic landforms located in the Victorian Volcanic Plain (VVP) in southeastern Australia. Stony rises are recognized as having high geological, ecological, and cultural values. Currently, the mapping of stony rises is carried out on a study-by-study basis, via traditional field surveys. Mapping of such features could be performed more rapidly and at a landscape level by utilizing remotely sensed datasets such as LiDAR, satellite and aerial imagery, and geophysical data, and applying detection techniques such as machine learning. This study incorporates an ensemble of remotely sensed data and predictor variables to characterize stony rise morphology through a machine learning approach that uses an object based Random Forest. We were able to successfully detect stony rises across various lava flows in the VVP. Future geomorphological studies should aim to incorporate the use of spectral and geophysical predictor variables for large area landform detection and analysis.

关键词： Stony rises object-based image analysis random forests remote sensing machine learning

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Machine learning based object-level crop classification of PlanetScope data at South India Basin

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EARTH SCIENCE INFORMATICS 2023年第1期16卷 91-104页

作者： Bhavana, D. Likhita, N. Madhumitha, G. V. Ratnam, D. Venkata Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Vaddeswaram 522502 Andhra Pradesh India

Crop classification is of great significance as it opens doors to agricultural inventory and research related to crop mapping, crop yield, and economic analysis. In most studies to date, openly accessible satellites like Landsat, Sentinel, and MODIS are generally used. These satellites suffer from low spatial resolution as well as high revisit rates. Further, the classification in these studies is carried out at pixel level, which is not reliable as per-pixel spectral analysis often gives ambiguous results. Thus, this study proposes the use of high-resolution temporal PlanetScope imagery with an average spatial resolution of 3.7 m for object-based image analysis (OBIA) at the Southern Indian basin at the River Krishna. This area is abundant with a variety of crops like paddy, chilli, maize, lily, colocasia, curry leaves, mint, bhindi, banana, betel leaves, and sugarcane. The latest available ground truth data was from 2017 and hence, this has been used. Despite this data being a year older than the one used in a previous study, good and commendable accuracies were still produced by this work. The machine learning (ML) modelling and evaluation was carried out in Python while Google Earth Engine (GEE) was used as the primary platform for feature extraction, dataset preparation, and visualization of the final crop-classified image. For the purpose of object-based classification, this paper puts forward Support Vector Machines (SVM). To provide a comparative analysis to justify the performance of modelled SVM, several other ML algorithms like Convolution Neural Networks (CNN), Random Forest (RF), Artificial Neural Network (ANN), and Bayes Classifier were then modelled for this methodology in this study area. The results show that SVM performed best, with a high accuracy of 94.3%. All other algorithms modelled showed less accuracy comparatively, but were still above 75%.

关键词： Crop classification object-based image analysis Machine learning PlanetScope Google Earth Engine Support Vector Machine

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Morphometric analysis and change detection in Yamuna riverbed in Delhi

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INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT 2024年第2期22卷 239-251页

作者： Kadu, Shilpa B. Dey, Jaydip Kumar, M. Suresh Vijay, Ritesh CSIR Ctr Strateg Urban Management Natl Environm Engn Res Inst Nagpur Maharashtra India Acad Sci & Innovat Res AcSIR Phys Sci Ghaziabad India

Morphometry is the process that deals with mathematical analysis and calculations of the various land surface dimensions. In this study, morphometry analysis and changes in riverbeds due to anthropogenic activities have been studied using geospatial techniques for a stretch of the Yamuna river in Delhi. The parametric values related to morphometry, such as low permeability and high surface run-off were determined through Remote Sensing, which is helpful to understand the characteristics of the area. The study reveals that morphometric parameters and land surface properties are closely interrelated and are dependent on each other. analysis has lightened up that over the year river span has been reduced from 5.15 to 4.23 sq. km and there is a trend of reduction and alteration of the channel in upcoming days. The morphometric conditions also indicate a concern of flood during monsoon. This study also focuses on the adverse effects of anthropogenic activities on the Yamuna riverbed morphology. There is a need for restriction on haphazard human activities, effective planning and management for sustainable development of the Yamuna riverbed in Delhi. The study suggests further research on aquatic ecology, water quality, and life expectancy of the river due to immense anthropogenic pressure.

关键词： Anthropogenic activities morphometry object-based image analysis remote sensing and geographical information system Yamuna river

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Seawall detection in Florida coastal area from high-resolution imagery using machine learning and OBIA

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JOURNAL OF APPLIED REMOTE SENSING 2022年第1期16卷

作者： Paudel, Sanjaya Su, Hongbo Khatri, Sanju Nagarajan, Sudhagar Florida Atlantic Univ Dept Civil Environm & Geomat Engn Boca Raton FL 33431 USA Florida Atlantic Univ Dept Geosci Boca Raton FL 33431 USA

A methodology and framework were presented to detect the seawalls accurately and efficiently in low coastal areas and were then evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected by the Florida Department of Transportation were processed using eCognition Developer software for multiresolution segmentation and classification of objects. Two classification approaches, pixel-based image analysis and the object-based image analysis method, were applied for image classification. However, pixel-based classification was discarded for having less accuracy in output. Three techniques within object-based classification-machine learning (ML) technique, knowledge-based (KB) technique, and ML followed by KB technique were used to compare the most efficient method of classification. While performing the ML technique, three algorithms, such as random forest, support vector machine, and decision tree, were applied to test the best algorithm. Of all the approaches used, the combination of ML and a KB method was able to map the seawall effectively with an overall accuracy of 94%. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： object-based image analysis seawall detection machine learning

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Mapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 imagery from Google Earth Engine

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GEOMATICS NATURAL HAZARDS & RISK 2022年第1期13卷 1867-1897页

作者： Liu, Jiyu Freudenberger, David Lim, Samsung Univ New South Wales Sch Civil & Environm Engn Sydney NSW Australia Australian Natl Univ Fenner Sch Environm & Soc Canberra ACT Australia Univ New South Wales Kirby Inst Biosecur Program Sydney NSW Australia

In Australia, fire has become part of the natural ecosystem. Severe fires have devastated Australia's unique forest ecosystems due to the global climate change. In this study, we integrated a multi-resolution segmentation method and a hierarchical classification framework based on expert-based knowledge to classify the burned areas and land-uses in Kangaroo Island, South Australia. Using an object-based image classification framework that combines colour and shape features from input layers, we demonstrated that the objects segmented from the multi-source data lead to a higher accuracy in classification with an overall accuracy of 90.2% and a kappa coefficient of 85.2%. On the other hand, the single source data from post-fire Landsat-8 imagery showed an overall accuracy of 87.4% which is also statistically acceptable. According to our experiment results, more than 30.44% of the study area was burned during the 2019-2020 'Black-Summer' fire season in Australia. Among the burned areas, high severity accounted for 12.14%, moderate severity for 11.48%, while low severity was 6.82%. For unburned areas, farmland accounted for 45.52% of the study area, of which about one-third was affected by the disturbances other than fire. The remaining area consists of 19.42% unaffected forest, 3.48% building and bare land, and 1.14% water. The comparison analysis shows that our object-based image classification framework takes full advantage of the multi-source data and generates the edges of burned areas more clearly, which contributes to the improved fire management and control.

关键词： Fire severity classification burned area identification object-based image analysis Landsat 8 google earth engine

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Feature Extraction in Urban Areas Using UAV Data 1

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2nd International Conference on Unmanned Aerial Systems in Geomatics (UASG)

作者： Sharma, Surendra Kumar Kumar, Minakshi Maithani, Sandeep Kumar, Pramod Indian Inst Remote Sensing Urban & Reg Studies Dept Dehra Dun Uttarakhand India Indian Inst Remote Sensing Photogrammetry & Remote Sensing Dept Dehra Dun Uttarakhand India

ISBN: (数字)9783031193095

ISBN: (纸本)9783031193118;9783031193095;9783031193088

As the rapid development is being focused in the urban area, there is a need for the utilisation of a system for updating this profile immediately. The usage of Uncrewed Aerial Vehicle (UAV) for mapping purposes is one of the current technologies being used in recent years. UAVs are widely used in a variety of domains due to their low price, ability to deliver very high resolution data, and ability to fly at low altitudes without being constrained by overcast weather. Typically, data extraction methods for UAVs are still quite limited, and traditional approaches are still used. For mapping applications, orthoimage features are often manually recognised and digitised using visual interpretation skills. Unfortunately, these approaches are time-consuming, costly, and repetitive. Pixel-based classification approach is frequently utilised to help extract low-level features, in which the image is categorised only based on spectral characteristics. The drawback of this approach is that the pixels in the overlapping region are misclassified as a result of class confusion. Moreover, pixel based classification performs very poorly in high resolution images. The object-based image analysis (OBIA) classification technique has large potential for automatic data extraction from Very High Resolution (VHR) images. OBIA techniques start with segmentation of image followed by classification and feature extraction using contextual information and rule base. In this study, an attempt is made to assess the capability of OBIA for detailed classification of highly dense urban areas mapped by UAV with a VHR imagery of the order better than 5 cm. The image is segmented using multiresolution image segmentation with a suitable scale, compactness and smoothness to form homogeneous image objects. Various parameters (spectral, texture, context and elevation) are computed for the VHR UAV images. Rules are formulated to extract and categorise urban features specifically for roads and buildings. Th

关键词： Unmanned aerial vehicle Feature extraction object-based image analysis Multiresolution segmentation

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Establishing distribution maps and structural analysis of seagrass communities based on high-resolution remote sensing images and field surveys: a case study at Nam Yet Island, Truong Sa Archipelago, Vietnam

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LANDSCAPE AND ECOLOGICAL ENGINEERING 2022年第3期18卷 405-419页

作者： Dang Hoi Nguyen Trung Dung Ngo Viet Dung Vu Quan Vu Viet Du Vietnam Russian Trop Ctr Inst Trop Ecol 63 Nguyen Van Huyen Str Hanoi Vietnam Vietnam Russian Trop Ctr Coast Branch 30 Nguyen Thien Thuat Str Nha Trang City Vietnam Vietnam Natl Univ Univ Sci 334 Nguyen Trai Str Hanoi Vietnam

Seagrasses are among the most important ecosystems of continental and island coastal waters globally. Analyses of the species diversity, structure, and distribution mapping of seagrass communities provide a scientific basis for seagrass conservation and development and protection of the marine environment. The application of remote sensing, especially using very high-resolution remote sensing data, in mapping seagrass distribution confers high accuracy and detailed results. The distribution of seagrass communities and benthic biomes typical of Nam Yet Island, Truong Sa Archipelago, Vietnam was mapped using object-based classification of Pleiades satellite imagery with four spectral bands (red, blue, green, and near-infrared) and the Green-Blue Normalized Difference Vegetation Index, combined with handheld positioning devices (total field samples 100, overall accuracy 91%, kappa coefficient = 0.88). Besides, by sampling and measurement in the field, two seagrass species, Thalassia hemprichii and Halophila ovalis were identified with the same structure, density, and biomass in the northern and southern parts of the island. The application of high-resolution remote sensing images combined with field surveys contributes to improving the accuracy of seagrass distribution maps as well as benthic biome ones for shallow sea areas. Establishing a seagrass distribution map and assessing the diversity of seagrass is the basis for the conservation and development of the current seagrass community at Nam Yet Island.

关键词： Benthic habitat Green-blue normalized difference vegetation index Pleiades object-based image analysis Thalassia hemprichii Halophila ovalis

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