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High-latitude wetland mapping using multidate and multisensor Earth observation data: a case study in the Northwest Territories

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JOURNAL OF APPLIED REMOTE SENSING 2020年第3期14卷

作者： Merchant, Michael Haas, Claudia Schroder, Julien Warren, Rebecca Edwards, Rebecca Ducks Unlimited Canada Natl Boreal Program Edmonton AB Canada Govt Northwest Terr Yellowknife NT Canada

The main objective in our study was to derive an accurate wetland inventory of the Dinaga Wek'ehodi region, Northwest Territories, while also enhancing our previously established wetland mapping workflow. Our methods used multidate optical and radar satellite imagery and fused these data with ArcticDEM topographic variables. Additionally, few studies to date have assessed the ArcticDEM for wetland mapping;our research helps fill this critical gap in the literature. A machine-learning, object-based approach was employed to classify the fused data stacks and included both mean and standard deviation image-object feature extractions. In this study, 18 random forest models were tested, each including various sensor inputs and feature extractions. The highest accuracy was achieved using a fusion of optical, radar, and ArcticDEM data and included both the mean and standard deviation of image objects (88.17% overall accuracy and kappa 0.858). Vegetated wetlands had producer accuracies ranging from 74% to 86%, whereas open water was 92%. Feature importance rankings indicated that 16 of the top 20 variables were derived from optical data, three from radar, and one from the ArcticDEM. The results of our study will be used to assist governments and other interested parties in advancing conservation initiatives for this significant high-latitude region. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： fusion Arctic digital elevation model object-based image analysis wetlands

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Novel insights into prehistoric land use at Stonehenge by combining electromagnetic and invasive methods with a semi-automated interpretation scheme

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JOURNAL OF ARCHAEOLOGICAL SCIENCE 2022年 143卷

作者： De Smedt, Philippe Garwood, Paul Chapman, Henry Deforce, Koen De Grave, Johan Hanssens, Daan Vandenberghe, Dimitri Univ Ghent Fac Biosci Engn Dept Environm Res Grp Soil Spatial Inventory Tech Ghent Belgium Univ Ghent Fac Arts & Philosophy Dept Archaeol Ghent Belgium Univ Birmingham Sch Hist & Cultures Dept Class Ancient Hist & Archaeol Birmingham England Royal Belgian Inst Nat Sci Brussels Belgium Univ Ghent Dept Geol Fac Sci Lab Mineral & Petrol Ghent Belgium

Geophysical survey methods have led to high-resolution mapping of subsurface remnants of ancient landscapes at continually expanding spatial scales. Yet, particularly when applied across entire archaeological landscapes, spanning hundreds of hectares, resultant datasets provide little direct information about inhabitation, environments or change over time. Focusing on a 2.5 km(2) area around Stonehenge, we show how geophysical soil survey, when combined with targeted sampling and excavation, can enable reliable empirically-grounded identification of complex activity traces. Particular focus lies on anthropogenic dug pits, identification and interpretation of which are vital in European earlier prehistoric archaeology due to their close connection with inhabitation and ceremonial practices. By integrating frequency domain electromagnetic and invasive datasets, and using a semi-automated interpretation scheme, we identified previously unknown concentrations of large pits (with diameters > 2.4 m) among several thousand smaller pits and natural features across the Stonehenge landscape. Excavations of a subset of identified features demonstrate that, in this area, our investigative methodology is 66% accurate for identifying large anthropogenic pits. Our results have significant implications for understanding Stonehenge and its landscape setting, revealing elusive forms of Mesolithic to later Bronze Age land use that -even within the world's most intensively researched archaeological landscape -have gone unrecognized until now. These findings underscore both the crucial role of archaeological excavation as an essential basis for reliable interpretation of geophysical data, as well as the perils of inductive visual interpretation of features' morphologies and their spatial configuration in non-invasive survey data.

关键词： Landscape archaeology Archaeological prospection Near-surface geophysics Electromagnetic induction object-based image analysis Prehistory Pits

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Mapping LULC types in the Cerrado-Atlantic Forest ecotone region using a Landsat time series and object-based image approach: A case study of the Prata River Basin, Mato Grosso do Sul, Brazil

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2020年第2期192卷 1-15页

作者： da Cunha, Elias Rodrigues Santos, Celso Augusto Guimaraes da Silva, Richarde Marques Bacani, Vitor Matheus Teodoro, Paulo Eduardo Panachuki, Eloi de Souza Oliveira, Naelmo Univ Fed Paraiba Dept Geosci Joao Pessoa Paraiba Brazil Univ Fed Paraiba Dept Civil & Environm Engn BR-58051900 Joao Pessoa Paraiba Brazil Univ Fed Mato Grosso do Sul Aquidauana MS Brazil Univ Fed Mato Grosso do Sul Dept Geog Tres Lagoas MS Brazil Univ Fed Mato Grosso do Sul Dept Agron Chapadao Do Sul MS Brazil Univ Estadual Mato Grosso do Sul Dept Agron Aquidauana MS Brazil

In the last 30 years, the growth of the agriculture and livestock industries in the Cerrado biome has caused severe changes in land use and land cover (LULC), and areas previously occupied by native vegetation are changing to agricultural monocultures (e.g., soybean or corn) and/or pastures. Thus, the objective of this study was to analyze the LULC changes for the years 1986, 1999, 2007, and 2016 based on Landsat time series and object-based image analysis (OBIA) for the Prata River Basin. Twelve LULC classes were mapped: riparian forest, cerrado, swampy grasslands, wetlands, semideciduous forest, pasture, agriculture, fallow agricultural land, barren land, eucalyptus, water bodies, and burnt area. The classifications presented results with an overall accuracy of more than 93% and a kappa coefficient of 0.92. In 2007, the pasture class had the highest increase in area (48.5%), with a total area of 118.32 km(2) of Cerrado biome vegetation converted to pasture, and the classes banhado, riparian forest, swampy grasslands, and cerrado had the greatest reductions in area (41.58%, 29.67%, 25.44%, and 21.63%, respectively). More precisely, the wetlands class underwent the greatest decrease under the advancement of pasture in the studied period (- 36.2%). These changes are due to factors favorable to agropastoral practices, such as a flat relief and soil with good agricultural suitability. Graphical abstract

关键词： Land use land cover change Remote sensing object-based image analysis Deforestation Cerrado biome

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Monitoring conifer cover: Leaf-off lidar and image-based tracking of eastern redcedar encroachment in central Nebraska

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REMOTE SENSING OF ENVIRONMENT 2020年 248卷 111961-111961页

作者： Filippelli, Steven K. Vogeler, Jody C. Falkowski, Michael J. Meneguzzo, Dacia M. Colorado State Univ Nat Resource Ecol Lab Ft Collins CO 80523 USA US Forest Serv USDA Forest Inventory & Anal St Paul MN 55108 USA

Eastern redcedar (Juniperus virginiana L.) encroachment on the Great Plains has led to decreases in biodiversity, water availability, and grazing land while increasing the risk of catastrophic wildfires. Quantifying the current spatial distribution of eastern redcedar can aid land management efforts to combat encroachment, and extending this information over time can improve our understanding of the patterns and drivers of encroachment. We compared several remote sensing methods for mapping percent conifer cover to develop an approach that would be applicable for monitoring eastern redcedar encroachment across the Great Plains to support management goals. Leaf-off lidar was filtered through a novel approach using normalized return intensity and local canopy density to remove residual points pertaining to deciduous trees, which enabled us to calculate percent conifer cover. A sample of the conifer cover derived from leaf-off lidar was then used to test passive imagery-based methods that could be applied over a greater spatiotemporal extent. These imagery-based methods included Spatial Wavelet analysis applied to very high-resolution imagery and random forest regression modeling with Landsat 8 and Sentinel-2 based predictive layers generated in Google Earth Engine from a single image, seasonal composites, and harmonic regression coefficients produced from an annual time series. Spatial Wavelet analysis provided high accuracy (similar to 5% RMSE) in areas where conifer cover was less than 10%, but accuracy rapidly decreased with increases in observed cover such that this method had a very low overall accuracy (42.5% RMSE). Landsat 8 and Sentinel-2 based predictors yielded similar performance to each other in models of conifer cover (9.7 to 13.3% RMSE), but seasonal composites from either sensor provided higher predictive power than either use of a single winter image or harmonic regression coefficients. According to recent estimates from the Forest Inventory and Analys

关键词： Eastern redcedar Conifer cover Great Plains Woody encroachment Leaf-off lidar LandTrendr Harmonic regression Temporal composite object-based image analysis Trees outside forests Woodland Forest monitoring Google earth engine

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A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection based on Multi-sensor Remote Sensing images

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REMOTE SENSING 2020年第2期12卷 205-205页

作者： Wang, Moyang Tan, Kun Jia, Xiuping Wang, Xue Chen, Yu China Univ Min & Technol NASG Key Lab Land Environm & Disaster Monitoring Xuzhou 221116 Jiangsu Peoples R China East China Normal Univ Minist Educ Key Lab Geog Informat Sci Shanghai 200241 Peoples R China Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia

Information extraction from multi-sensor remote sensing images has increasingly attracted attention with the development of remote sensing sensors. In this study, a supervised change detection method, based on the deep Siamese convolutional network with hybrid convolutional feature extraction module (OB-DSCNH), has been proposed using multi-sensor images. The proposed architecture, which is based on dilated convolution, can extract the deep change features effectively, and the character of "network in network" increases the depth and width of the network while keeping the computational budget constant. The change decision model is utilized to detect changes through the difference of extracted features. Finally, a change detection map is obtained via an uncertainty analysis, which combines the multi-resolution segmentation, with the output from the Siamese network. To validate the effectiveness of the proposed approach, we conducted experiments on multispectral images collected by the ZY-3 and GF-2 satellites. Experimental results demonstrate that our proposed method achieves comparable and better performance than mainstream methods in multi-sensor images change detection.

关键词： multi-sensor image change detection siamese neural network dilated convolution object-based image analysis

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Rapid urban flood damage assessment using high resolution remote sensing data and an object-based approach

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GEOMATICS NATURAL HAZARDS & RISK 2020年第1期11卷 906-927页

作者： Jimenez-Jimenez, Sergio Ivan Ojeda-Bustamante, Waldo Ontiveros-Capurata, Ronald Ernesto Marcial-Pablo, Mariana de Jesus Natl Inst Forestry Agr & Livestock Res Mexico CEN Mexico City 04010 DF Mexico Mexican Coll Irrigat Engineers COMEII Cuernavaca Morelos Mexico CONACYT Mexican Inst Water Technol IMTA Jiutepec Mexico

Torrential rainfall can generate landslides, flash floods, and debris flows which might become disasters, causing loss of life and damage to property and infrastructure. To respond opportunely to hydrometeorological hazards, it is necessary to assess, rapidly and accurately, damage to the affected area. This is commonly done through time-consuming reconnaissance visits to obtain detailed field information. This paper proposes a methodology which uses: i) high resolution satellite and RGB images from unmanned aerial vehicles (UAV), ii) digital elevation models (DEM), and iii) object-based image analysis (OBIA) for rapid urban flood damage assessment and estimation of the number of houses washed away, or with a total or partial roof collapse, by comparing pre- and post-event data. The case study was Tropical Storm Earl in 2016 that affected the town of Chicahuaxtla, Puebla, Mexico, due to the overflow of the Zempoloantongo River that cuts through the town causing several loss of life and severe property damage. The results indicate that the three-pronged approach proposed herein is able to discriminate changes before and after the event and improve image classification of washed-away or destroyed houses. The overall accuracy of the proposed automatic classification obtained with UAV data had a value of 97.4%. Structural damage was not assessed in this study.

关键词： Remote sensing object-based image analysis OBIA RGB image valley-emphasis method

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Evaluation of crop mapping on fragmented and complex slope farmlands through random forest and object-oriented analysis using unmanned aerial vehicles

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GEOCARTO INTERNATIONAL 2020年第12期35卷 1293-1310页

作者： Lee, Re-Yang Chang, Kuo-Chen Ou, Deng-Yuan Hsu, Chia-Hui Feng Chia Univ Dept Land Management Taichung Taiwan Natl Taiwan Normal Univ Dept Geog Taipei Taiwan

Conducting field research in Taiwan can be challenging because of the abundance of steep slopes. This study aimed to establish an automatic interpretation procedure applicable to exploring images of large-scale slope land taken using UAVs. The proposed method was compared with traditional field surveying and manual image interpretation techniques to determine the advantages and disadvantages of the proposed procedure in terms of efficiency. The object-based image analysis (OBIA) and texture features were first combined and the random forest (RF) classifier was then employed to interpret crop types. This study selected three sites of slope land and plains for experimentation. The obtained results indicated that the overall accuracy of the proposed classification method exceeded 91%, and the Kappa value was approximately 0.9 for all sites. In addition, interpretation of the proposed method was more efficient than that of the two traditional methods.

关键词： Unmanned aerial vehicle object-based image analysis texture random forest

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Tracking long-term floodplain wetland changes: A case study in the China side of the Amur River Basin

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2020年 92卷 102185-102185页

作者： Jia, Mingming Mao, Dehua Wang, Zongming Ren, Chunying Zhu, Qiande Li, Xuechun Zhang, Yuanzhi Chinese Acad Sci Northeast Inst Geog & Agroecol Key Lab Wetland Ecol & Environm Changchun 130102 Peoples R China Nanjing Univ Informat Sci & Technol Sch Marine Sci Nanjing 210044 Peoples R China Natl Earth Syst Sci Data Ctr China Beijing 100101 Peoples R China Nanjing Hydraul Res Inst State Key Lab Hydrol & Water Resources & Hydraul Nanjing 210029 Peoples R China Chinese Univ Hong Kong Fac Social Sci Hong Kong 999777 Peoples R China Chinese Univ Hong Kong Asia Pacific Studies Inst Hong Kong 999777 Peoples R China

Floodplain wetlands in the China side of the Amur River Basin (CARB) undergone consistent decreases because of both natural and anthropogenic drivers. Monitoring floodplain wetlands dynamics and conversions over longtime periods is thus fundamental to sustainable management and protection. Due to complexity and heterogeneity of floodplain environments, however, it is difficult to map wetlands accurately over a large area as the CARB. To address this issue, we developed a novel and robust classification approach integrating image compositing algorithm, objected-based image analysis, and hierarchical random forest classification, named COHRF, to delineate floodplain wetlands and surrounding land covers. based on the COHRF classification approach, 4622 Landsat images were applied to produce a 30-m resolution dataset characterizing dynamics and conversions of floodplain wetlands in the CARB during 1990-2018. Results show that (1) all floodplain land cover maps in 1990, 2000, 2010, and 2018 had high mapping accuracies (ranging from 90% +/- 0.001-97% +/- 0.005), suggesting that COHRF is a robust classification approach;(2) CARB experienced an approximately 25 % net loss of floodplain wetlands with an area declined from 8867 km(2) to 6630 km(2) during 1990-2018;(3) the lost floodplain wetlands were mostly converted into croplands, while, there were 111 km(2) and 256 km(2) of wetlands rehabilitated from croplands during periods of 2000-2010 and 2010-2018, respectively. To our knowledge, this study is the first attempt that focus on delineating floodplain wetlands at a large-scale and produce the first 30-m spatial resolution dataset demonstrating long-term dynamics of floodplain wetlands in the CARB. The COHRF classification approach could be used to classify other ecosystems readily and robustly. The resultant dataset will contribute to sustainable use and conservation of wetlands in the Amur River Basin and provide essential information for related researches.

关键词： Floodplain wetlands object-based image analysis Random forest Amur River basin Google earth engine

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Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

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CANADIAN JOURNAL OF FOREST RESEARCH 2020年第10期50卷 1012-1024页

作者： Wang, Meimei Lin, Jiayuan Southwest Univ Sch Geog Sci Minist Educ Chongqing Jinfo Mt Field Sci Observat & Res Stn K Chongqing 400715 Peoples R China Southwest Univ Sch Geog Sci Chongqing Engn Res Ctr Remote Sensing Big Data Ap Chongqing 400715 Peoples R China

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R-2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

关键词： individual tree height unmanned aerial vehicle point cloud structure from motion object-based image analysis

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Supervised wetland classification using high spatial resolution optical, SAR, and LiDAR imagery

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JOURNAL OF APPLIED REMOTE SENSING 2020年第2期14卷

作者： Amani, Meisam Mandavi, Sahel Berard, Olivier Wood Environm & Infrastruct Solut St John NF Canada Pk Canada Agcy Quebec City PQ Canada

Wetlands are among the most valuable natural resources, being highly beneficial to both the environment and humans. Therefore, it is very important to map and monitor wetlands. Although various remote sensing datasets, including optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR) imagery, have been widely applied to classify wetlands, it is still required to discuss the advantages/limitations of each of these datasets and suggest the best remote sensing methodology for wetland mapping. Thus, the Terra Nova National Park, located in Newfoundland, Canada, was initially selected as the study area to develop a supervised classification method along with object-based image analysis. To this end, different remote sensing-based scenarios were investigated using individual optical, SAR, and LiDAR datasets, as well as their various combinations. In addition, for achieving the highest accuracy, the effects of segmentation scales and the tuning parameters of the random forest (RF) classifier were examined. The results showed that a combination of optical, SAR, and LiDAR images with the segmentation scale of 150, the RF depth of 20, and the RF minimum sample number of 5 provided the highest classification accuracy with the overall accuracy of 87.2%. Moreover, based on the results, approximately 21% and 79% of the study area are covered by wetlands and nonwetlands, respectively. The proposed methodology shows an optimum scenario for future wetland classification tasks and can assist stakeholders in the effective management of wetlands and establishment of necessary policies. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： wetland remote sensing light detection and ranging synthetic aperture radar object-based image analysis Canada

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