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Monitoring Land-Use/Land-Cover Changes at a Provincial Large Scale Using an object-Oriented Technique and Medium-Resolution Remote-Sensing images

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REMOTE SENSING 2018年第12期10卷 2012-2012页

作者： Luo, Kaisheng Li, Bingjuan Moiwo, Juana P. Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomat Engn Nanjing 210044 Jiangsu Peoples R China Chinese Acad Sci Inst Soil Sci Nanjing 210008 Peoples R China Univ Chinese Acad Sci Nanjing 210008 Peoples R China Njala Univ Sch Technol Dept Agr Engn Freetown Sierra Leone

An object-based image analysis (OBIA) technique is replacing traditional pixel-based methods and setting a new standard for monitoring land-use/land-cover changes (LUCC). To date, however, studies have focused mainly on small-scale exploratory experiments and high-resolution remote-sensing images. Therefore, this study used OBIA techniques and medium-resolution Chinese HJ-CCD images to monitor LUCC at the provincial scale. The results showed that while woodland was mainly distributed in the west, south, and east mountain areas of Hunan Province, the west had the largest area and most continuous distribution. Wetland was distributed mainly in the northern plain area, and cultivated land was distributed mainly in the central and northern plains and mountain valleys. The largest impervious surface was the Changzhutan urban agglomerate in the northeast plain area. The spatial distribution of land cover in Hunan Province was closely related to topography, government policy, and economic development. For the period 2000-2010, the areas of cultivated land transformed into woodland, grassland, and wetland were 183.87 km(2), 5.57 km(2), and 70.02 km(2), respectively, indicating that the government-promoted ecologically engineered construction was yielding some results. The rapid economic growth and urbanization, high resource development intensity, and other natural factors offset the gains made in ecologically engineered construction and in increasing forest and wetland areas, respectively, by 229.82 km(2) and 132.12 km(2) from 2000 to 2010 in Hunan Province. The results also showed large spatial differences in change amplitude (LUCCA), change speed (LUCCS), and transformation processes in Hunan Province. The Changzhutan urban agglomerate and the surrounding prefectures had the largest LUCCA and LUCCS, where the dominant land cover accounted for the conversion of some 189.76 km(2) of cultivated land, 129.30 km(2) of woodland, and 6.12 km(2) of wetland into impervious surfac

关键词： HJ-CCD images object-based image analysis change monitoring provincial scale

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Emerging Sensor Platforms Allow for Seagrass Extent Mapping in a Turbid Estuary and from the Meadow to Ecosystem Scale

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REMOTE SENSING 2021年第18期13卷 3681-3681页

作者： Krause, Johannes R. Hinojosa-Corona, Alejandro Gray, Andrew B. Burke Watson, Elizabeth Drexel Univ Dept Biodivers Earth & Environm Sci Philadelphia PA 19104 USA Ctr Invest Cient & Educ Super Ensenada Dept Geol Ensenada 22860 BC Mexico Univ Calif Riverside Dept Environm Sci Riverside CA 92521 USA

Seagrass meadows are globally important habitats, protecting shorelines, providing nursery areas for fish, and sequestering carbon. However, both anthropogenic and natural environmental stressors have led to a worldwide reduction seagrass habitats. For purposes of management and restoration, it is essential to produce accurate maps of seagrass meadows over a variety of spatial scales, resolutions, and at temporal frequencies ranging from months to years. Satellite remote sensing has been successfully employed to produce maps of seagrass in the past, but turbid waters and difficulty in obtaining low-tide scenes pose persistent challenges. This study builds on an increased availability of affordable high temporal frequency imaging platforms, using seasonal unmanned aerial vehicle (UAV) surveys of seagrass extent at the meadow scale, to inform machine learning classifications of satellite imagery of a 40 km(2) bay. We find that object-based image analysis is suitable to detect seasonal trends in seagrass extent from UAV imagery and find that trends vary between individual meadows at our study site Bahia de San Quintin, Baja California, Mexico, during our study period in 2019. We further suggest that compositing multiple satellite imagery classifications into a seagrass probability map allows for an estimation of seagrass extent in turbid waters and report that in 2019, seagrass covered 2324 ha of Bahia de San Quintin, indicating a recovery from losses reported for previous decades.

关键词： seagrass unmanned aerial vehicle (UAV) object-based image analysis planet machine learning turbid estuary

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Accuracy Assessment Measures for object Extraction from Remote Sensing images

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REMOTE SENSING 2018年第2期10卷 303-303页

作者： Cai, Liping Shi, Wenzhong Miao, Zelang Hao, Ming Qufu Normal Univ Sch Geog & Tourism Rizhao 276826 Peoples R China Minist Land & Resource Key Lab Coastal Zone Exploitat & Protect Nanjing 210024 Jiangsu Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Hong Kong Peoples R China Cent S Univ Sch Geosci & Infophys Changsha 410012 Hunan Peoples R China Cent S Univ Key Lab Metallogen Predict Nonferrous Met & Geol Changsha 410012 Hunan Peoples R China China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Peoples R China

object extraction from remote sensing images is critical for a wide range of applications, and object-oriented accuracy assessment plays a vital role in guaranteeing its quality. To evaluate object extraction accuracy, this paper presents several novel accuracy measures that differ from the norm. First, area-based and object number-based accuracy assessment measures are given based on a confusion matrix. Second, different accuracy assessment measures are provided by combining the similarities of multiple features. Third, to improve the reliability of the object extraction accuracy assessment results, two accuracy assessment measures based on object detail differences are designed. In contrast to existing measures, the presented method synergizes the feature similarity and distance difference, which considerably improves the reliability of object extraction evaluation. Encouraging results on two QuickBird images indicate the potential for further use of the presented algorithm.

关键词： object-based image analysis accuracy assessment feature similarity distance difference

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THREE-DECADE changes of reef cover in Pulau Layang-Layang, Malaysia using multitemporal Landsat images

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MARINE ENVIRONMENTAL RESEARCH 2024年 197卷 106454页

作者： Han, Daniella Hsu Tsyr James, Daniel Waheed, Zarinah Phua, Mui-How Univ Malaysia Sabah UMS Borneo Marine Res Inst Kota Kinabalu 88400 Sabah Malaysia UMS Fac Trop Forestry Kota Kinabalu 88400 Sabah Malaysia UMS Fac Sci & Nat Resources Small Isl Res Ctr Kota Kinabalu 88400 Sabah Malaysia

Over the years, coral reefs in the South China Sea have degraded and faced severe threats from rapid development, coral bleaching, and Crown -of -Thorns Starfish (COTS) outbreak. There is limited knowledge relating to the effects of anthropogenic disturbances and natural events on the coral reefs of Pulau Layang-Layang. This study aims to assess reef cover changes by utilizing Landsat satellite images spanning from 1989 to 2022. Using the object -based image analysis method, this study classified the reef cover into three categories: coral, rock and rubble, and sand. The supervised classification had an overall accuracy of 86.41 -87.38 % and Tau 's coefficients of 0.80 -0.81. The results showed island development and construction of artificial bird sanctuary have led to an increase in coral cover. Furthermore, it was illustrated that the impact of COTS outbreaks in 2010 and 2020 differed significantly, with the latter showing no signs of recovery. Our study underscores the importance of timely intervention to mitigate the spread of COTS. This study provides insights into the resilience and vulnerability of these ecosystems in the face of various stressors.

关键词： Spratly islands Reef cover Crown-of-thorns starfish Anthropogenic disturbance object-based image analysis

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OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing images

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REMOTE SENSING OF ENVIRONMENT 2024年 304卷

作者： Guo, Houcai Ye, Dingqi Xu, Hanzeyu Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy Cent South Univ Sch Geosci & Info Phys Changsha 410083 Peoples R China Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China

Spatiotemporal fusion aims to improve both the spatial and temporal resolution of remote sensing images, thus facilitating time-series analysis at a fine spatial scale. However, there are several important issues that limit the application of current spatiotemporal fusion methods. First, most spatiotemporal fusion methods are based on pixel-level computation, which neglects the valuable shape information of ground objects. Moreover, many existing methods cannot accurately retrieve strong temporal changes between the available high-resolution image at base date and the predicted one. This study proposes an object-based Spatial Unmixing Model (OBSUM), which incorporates object-based image analysis and spatial unmixing, to overcome the two abovementioned problems. OBSUM consists of one preprocessing step and three fusion steps, i.e., object-level unmixing, object-level residual compensation, and pixel-level residual compensation. The performance of OBSUM was compared with seven representative spatiotemporal fusion methods at two agricultural sites. The experimental results demonstrated that OBSUM outperformed other methods in terms of both accuracy indices and visual effects over time-series. Furthermore, OBSUM also achieved satisfactory results in crop progress monitoring and crop mapping. Therefore, it has great potential to generate accurate and high-resolution timeseries observations for supporting various remote sensing applications.

关键词： Spatiotemporal fusion object-based image analysis Sentinel-2 Sentinel-3 Time-series Remote sensing

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object-based land-use/land-cover change detection using Landsat imagery: a case study of Ardabil, Namin, and Nir counties in northwest Iran

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2018年第7期190卷 376-376页

作者： Aslami, Farnoosh Ghorbani, Ardavan Univ Mohaghegh Ardabili Fac Humanities Dept Nat Geog Ardebil Iran Univ Mohaghegh Ardabili Fac Agr Technol & Nat Resources Dept Nat Resources Ardebil *** Iran

In this study, land-use/land-cover (LULC) change in the Ardabil, Namin, and Nir counties, in the Ardabil province in the northwest of Iran, was detected using an object-based method. Landsat images including Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), and Operational Land imager (OLI) were used. Preprocessing methods, including geometric and radiometric correction, and topographic normalization were performed. image processing was conducted according to object-based image analysis using the nearest neighbor algorithm. An accuracy assessment was conducted using overall accuracy and Kappa statistics. Results show that maps obtained from images for 1987, 2002, and 2013 had an overall accuracy of 91.76, 91.06, and 93.00%, and a Kappa coefficient of 0.90, 0.83, and 0.91, respectively. Change detection between 1987 and 2013 shows that most of the rangelands (97,156.6 ha) have been converted to dry farming;moreover, residential and other urban land uses have also increased. The largest change in land use has occurred for irrigated farming, rangelands, and dry farming, of which approximately 3539.8, 3086.9, and 2271.9 ha, respectively, have given way to urban land use for each of the studied years.

关键词： Remote sensing Land use/land cover Change detection object-based image analysis Landsat Ardabil province

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Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type based on High Spatial Resolution Aerial images

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REMOTE SENSING 2024年第1期16卷 31页

作者： Li, Shiou Fei, Xianyun Chen, Peilong Wang, Zhen Gao, Yajun Cheng, Kai Wang, Huilong Zhang, Yuanzhi Jiangsu Ocean Univ Sch Geomatics & Marine Informat Lianyungang 222002 Peoples R China Lianyungang Forestry Tech Guidance Stn Lianyungang 222005 Peoples R China Chinese Acad Sci Key Lab Lunar & Deep Space Explorat Natl Astron Observ Beijing 100101 Peoples R China Univ Chinese Acad Sci Sch Astron & Space Sci Beijing 100049 Peoples R China

The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of object-based image analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

关键词： object-based image analysis deep learning mountain vegetation type classification high spatial aerial remote sensing images

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Integrated Segmentation Approach with Machine Learning Classifier in Detecting and Mapping Post Selective Logging Impacts Using UAV imagery

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FORESTS 2022年第1期13卷 48页

作者： Kamarulzaman, Aisyah Marliza Muhmad Wan Mohd Jaafar, Wan Shafrina Abdul Maulud, Khairul Nizam Saad, Siti Nor Maizah Omar, Hamdan Mohan, Midhun Univ Kebangsaan Malaysia Inst Climate Change Earth Observat Ctr Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Civil Engn Bangi 43600 Selangor Malaysia Univ Teknol Mara Fac Architecture Planning & Surveying Dept Surveying Sci & Geomat Arau 02600 Perlis Malaysia Forest Res Inst Malaysia Kepong 52109 Selangor Malaysia Univ Calif Berkeley Dept Geog Berkeley CA 94709 USA

Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for sustainable forest management operations. The purpose of this study is to assess the indicators of selective logging impacts by detecting the individual stumps as the main indicators, evaluating the performance of classification methods to assess the impacts and identifying forest gaps from selective logging activities. The combination of forest inventory field plots and unmanned aerial vehicle (UAV) RGB and overlapped imaged were used in this study to assess these impacts. The study area is located in Ulu Jelai Forest Reserve in the central part of Peninsular Malaysia, covering an experimental study area of 48 ha. The study involved the integration of template matching (TM), object-based image analysis (OBIA), and machine learning classification-support vector machine (SVM) and artificial neural network (ANN). Forest features and tree stumps were classified, and the canopy height model was used for detecting forest canopy gaps in the post selective logging region. Stump detection using the integration of TM and OBIA produced an accuracy of 75.8% when compared with the ground data. Forest classification using SVM and ANN methods were adopted to extract other impacts from logging activities such as skid trails, felled logs, roads and forest canopy gaps. These methods provided an overall accuracy of 85% and kappa coefficient value of 0.74 when compared with conventional classifier. The logging operation also caused an 18.6% loss of canopy cover. The result derived from this study highlights the potential use of UAVs for efficient post logging impact analysis and can be used to complement conventional forest inventory practices.

关键词： selective logging impacts UAV object-based image analysis machine learning forest classification

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Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

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REMOTE SENSING 2019年第12期11卷 1409-1409页

作者： Maxwell, Aaron E. Strager, Michael P. Warner, Timothy A. Ramezan, Christopher A. Morgan, Alice N. Pauley, Cameron E. West Virginia Univ Dept Geol & Geog Morgantown WV 26506 USA West Virginia Univ Davis Coll Agr Nat Resources & Design Morgantown WV 26506 USA

Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km(2). We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user's and producer's accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly;however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents.

关键词： land cover land cover mapping object-based image analysis GEOBIA machine learning random forests National Agriculture imagery Program NAIP

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Combining Drones and Deep Learning to Automate Coral Reef Assessment with RGB imagery

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REMOTE SENSING 2023年第9期15卷 2238-2238页

作者： Giles, Anna Barbara Ren, Keven Davies, James Edward Abrego, David Kelaher, Brendan Southern Cross Univ Fac Sci & Engn Natl Marine Sci Ctr 2 Bay Dr Coffs Harbour NSW 2450 Australia Univ Melbourne Sch Phys Parkville Vic 3010 Australia

Coral reefs and their associated marine communities are increasingly threatened by anthropogenic climate change. A key step in the management of climate threats is an efficient and accurate end-to-end system of coral monitoring that can be generally applied to shallow water reefs. Here, we used RGB drone-based imagery and a deep learning algorithm to develop a system of classifying bleached and unbleached corals. imagery was collected five times across one year, between November 2018 and November 2019, to assess coral bleaching and potential recovery around Lord Howe Island, Australia, using object-based image analysis. This training mask was used to develop a large training dataset, and an mRES-uNet architecture was chosen for automated segmentation. Unbleached coral classifications achieved a precision of 0.96, a recall of 0.92, and a Jaccard index of 0.89, while bleached corals achieved 0.28 precision, 0.58 recall, and a 0.23 Jaccard index score. Subsequently, methods were further refined by creating bleached coral objects (>16 pixels total) using the neural network classifications of bleached coral pixels, to minimize pixel error and count bleached coral colonies. This method achieved a prediction precision of 0.76 in imagery regions with >2000 bleached corals present, and 0.58 when run on an entire orthomosaic image. Bleached corals accounted for the largest percentage of the study area in September 2019 (6.98%), and were also significantly present in March (2.21%). Unbleached corals were the least dominant in March (28.24%), but generally accounted for similar to 50% of imagery across other months. Overall, we demonstrate that drone-based RGB imagery, combined with artificial intelligence, is an effective method of coral reef monitoring, providing accurate and high-resolution information on shallow reef environments in a cost-effective manner.

关键词： UAV neural network Lord Howe Island coral bleaching climate change remote sensing object-based image analysis

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