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object-based correction of LiDAR DEMs using RTK-GPS data and machine learning modeling in the coastal Everglades

ENVIRONMENTAL MODELLING & SOFTWARE 2019年 112卷 179-191页

作者： Cooper, Hannah M. Zhang, Caiyun Davis, Stephen E. Troxler, Tiffany G. East Carolina Univ Dept Geog Planning & Environm A-204 Brewster Greenville NC 27858 USA Florida Atlantic Univ Dept Geosci 777 Glades Rd Boca Raton FL 33431 USA Everglades Fdn 18001 Old Cutler Rd Palmetto Bay FL 33157 USA Florida Int Univ Southeast Environm Res Ctr 11200 SW 8th St Miami FL 33199 USA

Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) are frequently applied in modeling coastal environments. We present an object-based correction approach for accurate and precise DEMs by integrating LiDAR point data, aerial imagery, and Real Time Kinematic-Global Positioning Systems. Four machine learning techniques (Random Forest, Support Vector Machine, k-Nearest Neighbor, and Artificial Neural Network) were compared with the commonly used bias-correction method. The Random Forest object-based model produced best predictions for two study areas: Nine Mile (Mean Bias Error (MBE) reduced 0.18 to - 0.02 m, Root Mean Square Error (RMSE) reduced 0.22 to 0.08 m) and Flamingo (MBE reduced 0.17 to 0.02 m, RMSE reduced 0.24 to 0.10 m). A Monte Carlo model was developed to combine errors into the object-based machine learning corrected DEMs, and uncertainty maps spatially revealed the likelihood of error. The object-based correction approach provides an attractive alternative to the bias-correction method.

关键词： LiDAR object-based image analysis Machine learning Monte Carlo DEMs Coastal wetlands

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object-based image analysis OF POST-TSUNAMI HIGH-RESOLUTION SATELLITE imageS FOR MAPPING THE IMPACT OF TSUNAMI DISASTER

OBJECT-BASED IMAGE ANALYSIS OF POST-TSUNAMI HIGH-RESOLUTION ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Koshimura, Shunichi Kayaba, Shintaro Gokon, Hideomi Tohoku Univ Grad Sch Engn Sendai Miyagi 980 Japan Miyagi TV Broadcast Corp Miyazaki Japan

ISBN: (纸本)9781457710056

The authors developed a method of object-based satellite image analysis using high-resolution post-tsunami satellite image to detect and map tsunami impact. The method is applied to QuickBird 4 band pan-sharpened composite image acquired in Banda Aceh, Indonesia, and the ground objects are classified into six;vegetation, water, soil, building, road and debris, for mapping the impact of the 2004 Sumatra-Andaman earthquake tsunami.

关键词： tsunami damage detection object-based image analysis optical satellite image

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Identifying Soil Erosion Processes in Alpine Grasslands on Aerial imagery with a U-Net Convolutional Neural Network

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REMOTE SENSING 2020年第24期12卷 4149-4149页

作者： Samarin, Maxim Zweifel, Lauren Roth, Volker Alewell, Christine Univ Basel Dept Math & Comp Sci Spiegelgasse 1 CH-4051 Basel Switzerland Univ Basel Dept Environm Sci Bernoullistr 30 CH-4056 Basel Switzerland

Erosion in alpine grasslands is a major threat to ecosystem services of alpine soils. Natural causes for the occurrence of soil erosion are steep topography and prevailing climate conditions in combination with soil fragility. To increase our understanding of ongoing erosion processes and support sustainable land-use management, there is a need to acquire detailed information on spatial occurrence and temporal trends. Existing approaches to identify these trends are typically laborious, have lack of transferability to other regions, and are consequently only applicable to smaller regions. In order to overcome these limitations and create a sophisticated erosion monitoring tool capable of large-scale analysis, we developed a model based on U-Net, a fully convolutional neural network, to map different erosion processes on high-resolution aerial images (RGB, 0.25-0.5 m). U-Net was trained on a high-quality data set consisting of labeled erosion sites mapped with object-based image analysis (OBIA) for the Urseren Valley (Central Swiss Alps) for five aerial images (16 year period). We used the U-Net model to map the same study area and conduct quality assessments based on a held-out test region and a temporal transferability test on new images. Erosion classes are assigned according to their type (shallow landslide and sites with reduced vegetation affected by sheet erosion) or land-use impacts (livestock trails and larger management affected areas). We show that results obtained by OBIA and U-Net follow similar linear trends for the 16 year study period, exhibiting increases in total degraded area of 167% and 201%, respectively. Segmentations of eroded sites are generally in good agreement, but also display method-specific differences, which lead to an overall precision of 73%, a recall of 84%, and a F1-score of 78%. Our results show that U-Net is transferable to spatially (within our study area) and temporally unseen data (data from new years) and is therefore a method

关键词： deep learning semantic segmentation remote sensing object-based image analysis erosion mapping landslides livestock trails sheet erosion

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Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV imagery

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REMOTE SENSING 2020年第1期12卷 9-9页

作者： Tagle Casapia, Ximena Falen, Lourdes Bartholomeus, Harm Cardenas, Rodolfo Flores, Gerardo Herold, Martin Honorio Coronado, Euridice N. Baker, Timothy R. IIAP Direcc Invest Manejo Integral Bosque & Serv Eco S Av A Quinones Km 2-5 Iquitos 16007 Peru Wageningen Univ & Res Lab Geoinformat Sci & Remote Sensing Droevendaalsesteeg 3 NL-06708 PB Wageningen Netherlands IIAP Direcc Gest Conocimiento & Invest Informac Divers Av A Quinones Km 2-5 Iquitos 16007 Peru Univ Leeds Sch Geog Leeds LS2 9JT W Yorkshire England

Sustainable management of non-timber forest products such as palm fruits is crucial for the long-term conservation of intact forest. A major limitation to expanding sustainable management of palms has been the need for precise information about the resources at scales of tens to hundreds of hectares, while typical ground-based surveys only sample small areas. In recent years, small unmanned aerial vehicles (UAVs) have become an important tool for mapping forest areas as they are cheap and easy to transport, and they provide high spatial resolution imagery of remote areas. We developed an object-based classification workflow for RGB UAV imagery which aims to identify and delineate palm tree crowns in the tropical rainforest by combining image processing and GIS functionalities using color and textural information in an integrative way to show one of the potential uses of UAVs in tropical forests. Ten permanent forest plots with 1170 reference palm trees were assessed from October to December 2017. The results indicate that palm tree crowns could be clearly identified and, in some cases, quantified following the workflow. The best results were obtained using the random forest classifier with an 85% overall accuracy and 0.82 kappa index.

关键词： object-based image analysis unmanned aerial vehicles imagery crown delineation textural parameters palm tree identification

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Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests

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REMOTE SENSING 2020年第17期12卷 2865页

作者： Moe, Kyaw Thu Owari, Toshiaki Furuya, Naoyuki Hiroshima, Takuya Morimoto, Junko Univ Tokyo Grad Sch Agr & Life Sci Dept Forest Sci Tokyo 1138657 Japan Univ Forestry & Environm Sci Fac Forestry Yezin 15013 Naypyitaw Myanmar Univ Tokyo Chiba Forest Grad Sch Agr & Life Sci Kamogawa Chiba 2995503 Japan Forestry & Forest Prod Res Inst Hokkaido Res Ctr 7 Hitsujigaoka Sapporo Hokkaido 0628516 Japan Univ Tokyo Hokkaido Forest Grad Sch Agr & Life Sci Furano Hokkaido 0791563 Japan Hokkaido Univ Grad Sch Agr Sapporo Hokkaido 0608589 Japan

High-value timber species play an important economic role in forest management. The individual tree information for such species is necessary for practical forest management and for conservation purposes. Digital aerial photogrammetry derived from an unmanned aerial vehicle (UAV-DAP) can provide fine spatial and spectral information, as well as information on the three-dimensional (3D) structure of a forest canopy. Light detection and ranging (LiDAR) data enable area-wide 3D tree mapping and provide accurate forest floor terrain information. In this study, we evaluated the potential use of UAV-DAP and LiDAR data for the estimation of individual tree location and diameter at breast height (DBH) values of large-size high-value timber species in northern Japanese mixed-wood forests. We performed multiresolution segmentation of UAV-DAP orthophotographs to derive individual tree crown. We used object-based image analysis and random forest algorithm to classify the forest canopy into five categories: three high-value timber species, other broadleaf species, and conifer species. The UAV-DAP technique produced overall accuracy values of 73% and 63% for classification of the forest canopy in two forest management sub-compartments. In addition, we estimated individual tree DBH Values of high-value timber species through field survey, LiDAR, and UAV-DAP data. The results indicated that UAV-DAP can predict individual tree DBH Values, with comparable accuracy to DBH prediction using field and LiDAR data. The results of this study are useful for forest managers when searching for high-value timber trees and estimating tree size in large mixed-wood forests and can be applied in single-tree management systems for high-value timber species.

关键词： high-value timber species single-tree management system object-based image analysis random forest classification unmanned aerial vehicle

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object-based image Procedures for Assessing the Solar Energy Photovoltaic Potential of Heterogeneous Rooftops Using Airborne LiDAR and Orthophoto

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REMOTE SENSING 2020年第2期12卷 223-223页

作者： Tiwari, Arti Meir, Isaac A. Karnieli, Arnon Ben Gurion Univ Negev Jacob Blaustein Inst Desert Res Remote Sensing Lab IL-85105 Beer Sheva Israel Ben Gurion Univ Negev Fac Engn Sci Struct Engn Dept IL-85105 Beer Sheva Israel

Available renewable energy resources play a vital role in fulfilling the energy demands of the increasing global population. To create a sustainable urban environment with the use of renewable energy in human habitats, a precise estimation of solar energy on building roofs is essential. The primary goal of this paper is to develop a procedure for measuring the rooftop solar energy photovoltaic potential over a heterogeneous urban environment that allows the estimation of solar energy yields on flat and pitched roof surfaces at different slopes and in different directions, along with multi-segment roofs on a single building. Because of the complex geometry of roofs, very high-resolution data, such as ortho-rectified aerial photography (orthophotos), and LiDAR data have been used to generate a new object-based algorithm to classify buildings. An overall accuracy index and a Kappa index of agreement (KIA) of 97.39% and 0.95, respectively, were achieved. The paper also develops a new model to create an aspect-slope map, which combines slope orientation with the gradient of the slope and uses it to demonstrate the collective results. This study allows the assessment of solar energy yields through defining solar irradiances in units of pixels over a specific time period. It might be beneficial in terms of more efficient measurements for solar panel installations and more accurate calculations of solar radiation for residents and commercial energy investors.

关键词： object-based image analysis segmentation LiDAR orthophoto aspect-slope map solar energy photovoltaic potential

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Feature-Level Fusion of Landsat 8 Data and SAR Texture images for Urban Land Cover Classification

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2019年第3期47卷 479-485页

作者： Mahmoudi, Fatemeh Tabib Arabsaeedi, Alireza Alavipanah, Seyed Kazem Shahid Rajaee Teacher Training Univ Fac Civil Engn Dept Geomat Tehran Iran Univ Tehran Dept Remote Sensing & GIS Fac Geog Tehran Iran

Each of the urban land cover types has unique thermal pattern. Therefore, thermal remote sensing can be used over urban areas for indicating temperature differences and comparing the relationships between urban surface temperatures and land cover types. On the other hand, synthetic-aperture radar (SAR) sensors are playing an increasingly important role in land cover classification due to their ability to operate day and night through cloud cover, and capturing the structure and dielectric properties of the earth surface materials. In this research, a feature-level fusion of SAR image and all bands (optical and thermal) of Landsat 8 data is proposed in order to modify the accuracy of urban land cover classification. In the proposed object-based image analysis algorithm, segmented regions of both Landsat 8 and SAR images are utilized for performing knowledge-based classification based on the land surface temperatures, spectral relationships between thermal and optical bands, and SAR texture features measured in the gray-level co-occurrence matrix space. The evaluated results showed the improvements of about 2.48 and 0.06 for overall accuracy and kappa after performing feature-level fusion on Landsat 8 and SAR data.

关键词： Textural features Feature-level fusion object-based image analysis Thermal remote sensing SAR data

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Vegetation patterns in a South American coastal wetland using high-resolution imagery

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JOURNAL OF MAPS 2019年第2期15卷 642-650页

作者： Gonzalez, Eliana Gonzalez Trilla, Gabriela San Martin, Laura Grimson, Rafael Kandus, Patricia Univ Nacl San Martin 3iA 25 Mayo & FranciaCampus Miguelete UNSAM San Martin Buenos Aires Argentina Consejo Nacl Invest Cient & Tecn Consejo Nacl Invest Cient & Tecn Godoy Cruz 2290C142FQB Buenos Aires DF Argentina

The aim of this study was to identify and characterize the main plant communities in a temperate coastal wetland using high-resolution imagery. We produced a map of Samborombon Bay at 1:25,000 scale using a WorldView-2 image. An object-based image analysis approach was chosen, and an unsupervised classification algorithm was applied. Overall classification accuracy was 81%, and the Kappa index was 78.1%. Six land cover types were mapped including four main natural monospecific plant communities. The lower intertidal area was dominated by mudflats without vegetation and stands of Bolboschoenus maritimus. The middle intertidal area was dominated by Sarcocornia ambigua, while, in the higher intertidal area, Sporobolus densiflorus and grasslands with Cortaderia selloana prevailed. We found four spatial patterns at a landscape scale, based on the presence and spatial distribution of the natural plant communities. This map represents a valuable tool for future studies on wetland environmental indicators.

关键词： Coastal marshes plant communities Worldview-2 Samborombon Bay object-based image analysis tidal plain

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Using the random forest algorithm to integrate hydroacoustic data with satellite images to improve the mapping of shallow nearshore benthic features in a marine protected area in Jamaica

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GISCIENCE & REMOTE SENSING 2019年第7期56卷 1065-1092页

作者： McLaren, Kurt McIntyre, Karen Prospere, Kurt Univ West Indies Dept Life Sci Kingston 7 Jamaica Lund Univ Ctr Geog Informat Syst Dept Phys Geog & Ecosyst Sci Solvegatan 12 S-22362 Lund Sweden

Hydroacoustic and optical remote sensing have been commonly used to map shallow nearshore benthic features. However, the number, type, scale, and accuracy of the mapping products that can be obtained from the two sensors differ;as such, there can be limited agreement between their mapping products. These differences can be further accentuated if the hydroacoustic data are interpolated to produce a map. Interpolation introduces spatial uncertainty and reduces map accuracy. Consequently, maps generated from the two sensors may provide dissimilar spatial and temporal representations of the same benthic features. We therefore compared the performance of a random forest regression (RFr) and a universal kriging (UK) interpolation method and a post-classification enhancement that can be used to increase the accuracy and complementarity of benthic habitat maps derived from hydroacoustic data. First, we used single beam echosounder (SBES) survey bathymetry data from the Bluefields Bay marine protected area (MPA) in western Jamaica (13.82 km(2) in size), to create a bathymetric surface model (BSM), from which rugosity and bathymetric position index (BPI) maps were generated. Next, the RFr was used to create submerged aquatic vegetation (SAV) percentage cover maps from the SBES SAV cover data by predicting cover at un-sampled locations. Predictors included auxiliary data such as depth, BPI, survey points coordinates and radiometrically corrected, deglinted and water column corrected image reflectance index values from each of the following: WorldView-2, Geoeye-1 and Landsat 8. Additionally, a SAV map was created using the UK. The most accurate SAV cover thresholds were identified and were used to create binary maps from the RFr and UK maps. A rugosity derived coral reef map was then added to the binary maps. The resulting benthic habitat maps had comparable accuracies and class coverage to benthic maps classified from GeoEye-1 and WorldView-2 images using pixel and object-base

关键词： interpolation echosounders object-based image analysis pixel-based image analysis Caribbean

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Uncertainty analysis for object-based Change Detection in Very High-Resolution Satellite images Using Deep Learning Network

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REMOTE SENSING 2020年第15期12卷 2345-2345页

作者： Song, Ahram Kim, Yongil Han, Youkyung Seoul Natl Univ Dept Civil & Environm Engn 1 Gwanak Ro Seoul 08826 South Korea Kyungpook Natl Univ Sch Convergence & Fus Syst Engn Sangju 37224 South Korea

object-based image analysis (OBIA) is better than pixel-based image analysis for change detection (CD) in very high-resolution (VHR) remote sensing images. Although the effectiveness of deep learning approaches has recently been proved, few studies have investigated OBIA and deep learning for CD. Previously proposed methods use the object information obtained from the preprocessing and postprocessing phase of deep learning. In general, they use the dominant or most frequently used label information with respect to all the pixels inside an object without considering any quantitative criteria to integrate the deep learning network and object information. In this study, we developed an object-based CD method for VHR satellite images using a deep learning network to denote the uncertainty associated with an object and effectively detect the changes in an area without the ground truth data. The proposed method defines the uncertainty associated with an object and mainly includes two phases. Initially, CD objects were generated by unsupervised CD methods, and the objects were used to train the CD network comprising three-dimensional convolutional layers and convolutional long short-term memory layers. The CD objects were updated according to the uncertainty level after the learning process was completed. Further, the updated CD objects were considered as the training data for the CD network. This process was repeated until the entire area was classified into two classes, i.e., change and no-change, with respect to the object units or defined epoch. The experiments conducted using two different VHR satellite images confirmed that the proposed method achieved the best performance when compared with the performances obtained using the traditional CD approaches. The method was less affected by salt and pepper noise and could effectively extract the region of change in object units without ground truth data. Furthermore, the proposed method can offer advantages associated with

关键词： change detection object-based image analysis very high-resolution satellite images deep learning network

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