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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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An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris

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REMOTE SENSING 2016年第9期8卷 778-778页

作者： Richardson, Jeffrey J. Moskal, L. Monika Univ Washington Sch Environm & Forest Sci Seattle WA 98195 USA

Large woody debris (LWD) plays a critical structural role in riparian ecosystems, but it can be difficult and time-consuming to quantify and survey in the field. We demonstrate an automated method for quantifying LWD using aerial LiDAR and object-based image analysis techniques, as well as a manual method for quantifying LWD using image interpretation derived from LiDAR rasters and aerial four-band imagery. In addition, we employ an established method for estimating the number of individual trees within the riparian forest. These methods are compared to field data showing high accuracies for the LWD method and moderate accuracy for the individual tree method. These methods can be integrated to quantify the contemporary and recruitable LWD in a river system.

关键词： LiDAR object-based image analysis riparian forests

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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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Modeling the impact of large-scale transportation infrastructure development on land cover

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2018年第1期10卷 26-42页

作者： Efthymiou, Dimitrios Antoniou, Constantinos Siora, Emmanouela Argialas, Demetre Natl Tech Univ Athens Sch Rural & Surveying Engn Zografos Greece

Transportation infrastructure and urban development are tightly connected and interact with each other. The objective of this paper is to measure the impact of large-scale transportation infrastructure locations on land cover. Spatial econometric models that link the impact of transportation infrastructure onto the urban evolution have been used with panel data (multiple time points). Land cover information was derived individually from two Landsat TM images (1984 and 2010) by applying a semi-automatic object-based image analysis (OBIA) approach, resulting in six generalized thematic categories. Data from multiple databases have been collected and cross-referenced. The study area is the Athens metropolitan area, Greece, where, during the last few decades, crucial transportation infrastructure has been developed and significant urbanization has been observed. Moreover, a fixed effects model has been developed for a study area, providing additional information for the differences across municipalities. The results of this research provide evidence toward the quantification of the impact of major road transportation infrastructure in urban development and can have applications in other areas that have not been yet fully developed.

关键词： Spatial econometric models object-based image analysis transportation infrastructure Athens

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Potential of multitemporal Gaofen-1 panchromatic/multispectral images for crop classification: case study in Xinjiang Uygur Autonomous Region, China

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JOURNAL OF APPLIED REMOTE SENSING 2015年第1期9卷 096035-1-096035-15页

作者： Hao, Pengyu Wang, Li Niu, Zheng Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Gaofen-1 panchromatic/multispectral (GF-1 PMS) data have both high spatial and temporal resolutions, and this research aims at evaluating the potential of GF-1 PMS data for crop classification. Three PMS images (at days 110, 192, and 274) were acquired in Manas County of Xinjiang. The images were first segmented and all objects were then visually interpreted based on ground reference data. Some indices and textual features were then extracted at the object level. Subsequently, the Jeffries-Matusita (JM) distance was employed to estimate the class separability among all pair-wise comparisons of each time period. Afterward, a random forest algorithm was used to calculate importance scores of all features and classify crop types for every possible image combination. Additionally, to evaluate the influence of feature number on classification accuracy, features were added one by one based on the importance of scores. The result showed that GF-1 PMS images with high-spatial resolution had the potential to identify the boundary of the crop fields. Relatively high JM distance (above 1.5) and classification accuracy (above 90%) indicated that day 192 image contributed the most to the crop identification in the study area. For multi-image combinations, days 110 to 192 combination can achieve high overall accuracy (around 93%) and more images cannot substantially improve the classification performance. As for features, normalized difference vegetation index and near infrared (NIR) band had the highest importance scores and textual features contributed to distinguishing tree from crop land. Finally, classification accuracy increased together with the augmentation of feature number when only a few features were used. After accuracies reached saturation points, however, more features only slightly improved the classification performance. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： random forest object-based image analysis feature importance Jeffries-Matusita distance classification accuracy cotton maize wheat

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Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated image Classification in the Monitoring of Ecological Recovery

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DRONES 2019年第3期3卷 1-16页

作者： Buters, Todd Belton, David Cross, Adam Curtin Univ Sch Mol & Life Sci ARC Ctr Mine Site Restorat Kent St Bentley WA 6102 Australia Curtin Univ Sch Earth & Planetary Sci Spatial Sci Bentley WA 6102 Australia

Monitoring is a crucial component of ecological recovery projects, yet it can be challenging to achieve at scale and during the formative stages of plant establishment. The monitoring of seeds and seedlings, which represent extremely vulnerable stages in the plant life cycle, is particularly challenging due to their diminutive size and lack of distinctive morphological characteristics. Counting and classifying seedlings to species level can be time-consuming and extremely difficult, and there is a need for technological approaches offering restoration practitioners with fine-resolution, rapid and scalable plant-based monitoring solutions. Unmanned aerial vehicles (UAVs) offer a novel approach to seed and seedling monitoring, as the combination of high-resolution sensors and low flight altitudes allow for the detection and monitoring of small objects, even in challenging terrain and in remote areas. This study utilized low-altitude UAV imagery and an automated object-based image analysis software to detect and count target seeds and seedlings from a matrix of non-target grasses across a variety of substrates reflective of local restoration substrates. Automated classification of target seeds and target seedlings was achieved at accuracies exceeding 90% and 80%, respectively, although the classification accuracy decreased with increasing flight altitude (i.e., decreasing image resolution) and increasing background surface complexity (increasing percentage cover of non-target grasses and substrate surface texture). Results represent the first empirical evidence that small objects such as seeds and seedlings can be classified from complex ecological backgrounds using automated processes from UAV-imagery with high levels of accuracy. We suggest that this novel application of UAV use in ecological monitoring offers restoration practitioners an excellent tool for rapid, reliable and non-destructive early restoration trajectory assessment.

关键词： ecological restoration object-based image analysis rehabilitation remote sensing monitoring

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Quantifying the Long-Term Expansion and Dieback of Spartina Alterniflora Using Google Earth Engine and object-based Hierarchical Random Forest Classification

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 9781-9793页

作者： Yan, Dandan Li, Jingtai Yao, Xiuying Luan, Zhaoqing Nanjing Forestry Univ Coll Biol & Environm Coinnovat Ctr Sustainable Forestry Southern China Nanjing 210037 Peoples R China

The overwhelming spread of Spartina alterniflora (S. alterniflora) (smooth cordgrass) over recent decades has put many native plant communities and coastal environments at risk. Therefore, long-term monitoring of S. alterniflora dynamics is necessary to better understand and manage the invasion of the species. However, it is difficult to map Spartina saltmarshes in China on an annual or multiyear epoch basis. To address this issue, we developed a classification approach integrating Google Earth Engine (GEE) and object-based hierarchical random forest (RF) classification, and we applied this approach to quantify the expansion and dieback of S. alterniflora at Dafeng Milu National Nature Reserve, Jiangsu, China during 1993-2020. Results showed that the area of S. alterniflora expanded from 24.48 ha in 1993 to 1564.96 ha in 2010. However, after ecological hydrological engineering and an increase in Elaphures davidianus (Pere David's deer) numbers in 2011, the S. alterniflora area decreased significantly to 944.28 ha in 2020. During 2011-2020, the S. alterniflora area decreased substantially at a rate of 67 ha per year and by 86% in one area studied in Dafeng Milu National Nature Reserve. In 2020, the 944.28 ha of S. alterniflora in the reserve was mainly distributed in mudflats by the sea. Overall, these results show that it is feasible to identify S. alterniflora using the GEE platform and object-based hierarchical RF classification;moreover, this approach could improve understanding and management of this invasion species.

关键词： Earth Remote sensing Artificial satellites Radio frequency Wetlands Forestry Sea measurements Dafeng milu national nature reserve Google Earth Engine landsat object-based image analysis random forest (RF) spartina alterniflora

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A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2008年第4期63卷 399-408页

作者： Durieux, Laurent Lagabrielle, Erwann Nelson, Andrew IRD US Espace F-34093 Montpellier France Cirad & Univ Reunion UMR 53 PVBMT F-97410 St Pierre France Inst Environm & Sustainabil Joint Res Ctr I-21020 Ispra VA Italy

Urban sprawl has been identified as one of the most negative effects of global population growth on the environment and biodiversity. Frequent monitoring of urban sprawl is needed to limit the impact of this ongoing phenomenon. This paper proposes precise monitoring of building construction using an object-based classification methodology applied to Spot 5 images with a 2.5 m resolution. An application at a regional scale on Reunion Island in the Indian Ocean shows that this building extraction methodology has limitations in the production of reference urban maps because of difficulties in defining the shape and the number of buildings compared to classical photo-interpretation of aerial photography. However, these results are of great value for planning in urban sprawl areas where up-to-date information is lacking because of the rapid pace of house construction and residential development. (C) 2008 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Spot 5 Reunion Island integrated urban sprawl management object-based image analysis

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Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California

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REMOTE SENSING 2014年第8期6卷 7276-7302页

作者： Liau, Yan-Ting Univ Oklahoma Ctr Spatial Anal Norman OK 73019 USA

Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing at high costs are required. In this study, I applied a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects on a digital orthophoto quadrangles with 1 m spatial resolution. Step one and step two were for image segmentation optimized for delineation of tree density, which involved global Otsu's method followed by the random walker algorithm. Step three was for detailed species delineations, which were derived from multiresolution segmentation, in two test areas. Step one and step two were able to delineating tree density segments and label species association robustly, compared to previous hierarchical frameworks. However, step three was limited by less image information to produce detailed, reasonable image objects with optimal scale parameters for species labeling. This hierarchical vegetation framework has potential to develop baseline data for evaluating climate change impacts on vegetation at lower cost using widely available data and a personal laptop.

关键词： object-based image analysis image segmentation optimized for delineation of tree density very high resolution imagery species association labeling the Z values of Moran's I

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