检索结果-内蒙古大学图书馆

Integration of historical map and aerial imagery to characterize long-term land-use change and landscape dynamics: An object-based analysis via Random Forests

引用

ECOLOGICAL INDICATORS 2018年第Part1期95卷 595-605页

作者： Liu, Dan Toman, Elizabeth Fuller, Zane Chen, Gang Londo, Alexis Zhang, Xuesong Zhao, Kaiguang Ohio State Univ Sch Environm & Nat Resources Environm Sci Grad Program Columbus OH 43210 USA Univ N Carolina Dept Geog & Earth Sci Lab Remote Sensing & Environm Change Charlotte NC 28223 USA Univ Maryland Pacific Northwest Natl Lab Joint Global Change Res Inst College Pk MD 20740 USA Univ Maryland Earth Syst Sci Interdisciplinary Ctr College Pk MD 20740 USA Ohio State Univ Sch Environm & Nat Resources Ohio Agr Res & Dev Ctr Wooster OH 44691 USA

Tracking Earth's past helps us to move from hindsight to foresight in seeking landscape sustainability, a pursuit aided by modern mapping capabilities but hindered by a dearth of historical landscape information. To fill the data gap and exemplify the use of old maps for land-use change sciences, we combined an old paper-based US civil war map and modem aerial photos to derive land-use history and landscape dynamics at fine scales for a region near Chancellorsville, USA, from 1867 to 2014. We also tested how advanced algorithms object-based image analysis and Random Forests (RF)-could aid in data processing. Automatic classification of the scanned 1867 paper map proved difficult, but its manual digitization could benefit from object-based image segmentation. Classifying digital aerial images was more accurate via the object-based than pixel-based method, but only if the images were segmented appropriately. In the object-based classification, spectral-based features were much more important and useful than shape/geometry features for land-cover discrimination, as ranked by RF. During the 147 years, 32% of the region changed in land type. Settlement and roads increased in extent by 1850% and 691%, respectively, and woodland decreased by 19%. These changes fragmented the landscape, altered the hydrological regime, and affected river morphology. The utility of old maps exemplified here provides an impetus for leveraging extant old maps or historical records to support land-use and global change research. Our study also connotes the importance of preserving and geotagging current non-traditional data, such as photos, videos, and citizen science data, that can serve as a baseline to document future landscape change.

关键词： Historical map Ancient map Military map Landscape ecology Landscape change Environmental management object-based image analysis Segmentation Random Forests Land-use change

来源：评论

学校读者我要写书评

暂无评论

Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 134卷 151-159页

作者： Gilbertson, Jason Kane Kemp, Jaco van Niekerk, Adriaan Univ Stellenbosch Dept Geog & Environm Studies Stellenbosch South Africa Univ Western Australia Sch Plant Biol 35 Stirling Hwy Crawley WA 6009 Australia

This study evaluates the potential of pan-sharpening multi-temporal Landsat 8 imagery for the differentiation of crops in a Mediterranean climate. Five Landsat 8 images covering the phenological stages of seven major crops types in the study area (Cape Winelands, South Africa) were acquired. A statistical pan-sharpening algorithm was used to increase the spatial resolution of the 30 m multispectral bands to 15 m. The pan-sharpened images and original multispectral bands were used to generate two sets of input features at 30 and 15 m resolutions respectively. The two sets of spatial variables were separately used as input to decision trees (DTs), k-nearest neighbour (k-NN), support vector machine (SVM), and random forests (RF) machine learning classifiers. The analyses were carried out in both the object based image analysis (OBIA) and pixel-based image analysis (PBIA) paradigms. For the OBIA experiments, three image segmentation scenarios were tested (good, over and under segmentation). The PBIA experiments were carried out at 30 m and 15 m resolutions. The results show that pan-sharpening led to dramatic (,-15%) improvements in classification accuracies in both the PBIA and OBIA approaches. Compared to the other classifiers, SVM consistently produced superior results. When applied to the pah-sharpened imagery SVM produced an overall accuracy of nearly 96% using OBIA, while PBIA's overall accuracy was 1.63% lower. We conclude that pan-sharpening Landsat 8 imagery is highly beneficial for classifying agricultural fields whether an object- or pixel-based approach is used. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Pan-sharpening Machine learning object-based image analysis Supervised classification Crop type mapping

来源：评论

学校读者我要写书评

暂无评论

Detection of potato beetle damage using remote sensing from small unmanned aircraft systems

引用

JOURNAL OF APPLIED REMOTE SENSING 2017年第2期11卷 026013-1-026013-10页

作者： Hunt, E. Raymond, Jr. Rondon, Silvia I. USDA ARS Beltsville Agr Res Ctr Hydrol & Remote Sensing Lab Beltsville MD 20705 USA Oregon State Univ Hermiston Agr Res & Extens Ctr Hermiston OR USA

Colorado potato beetle (CPB) adults and larvae devour leaves of potato and other solanaceous crops and weeds, and may quickly develop resistance to pesticides. With early detection of CPB damage, more options are available for precision integrated pest management, which reduces the amount of pesticides applied in a field. Remote sensing with small unmanned aircraft systems (sUAS) has potential for CPB detection because low flight altitudes allow image acquisition at very high spatial resolution. A five-band multispectral sensor and up-looking incident light sensor were mounted on a six-rotor sUAS, which was flown at altitudes of 60 and 30 m in June 2014. Plants went from visibly undamaged to having some damage in just 1 day. Whole-plot normalized difference vegetation index (NDVI) and the number of pixels classified as damaged (0.70 <= NDVI <= 0.80) were not correlated with visible CPB damage ranked from least to most. Area of CPB damage estimated using object-based image analysis was highly correlated to the visual ranking of damage. Furthermore, plant height calculated using structure- from-motion point clouds was related to CPB damage, but this method required extensive operator intervention for success. object-based image analysis has potential for early detection based on high spatial resolution sUAS remote sensing. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： Solanum tuberosum Colorado potato beetle Leptinotarsa decemlineata multirotor drone spectral vegetation indices object-based image analysis structure-from-motion point clouds

来源：评论

学校读者我要写书评

暂无评论

Machine learning and multi-sensor data fusion for mapping lithology: A case study of Kowli-kosh area, SW Iran

引用

ADVANCES IN SPACE RESEARCH 2021年第10期68卷 3992-4015页

作者： Shayeganpour, Samira Tangestani, Majid H. Gorsevski, Pece V. Shiraz Univ Fac Sci Dept Earth Sci Shiraz Iran Bowling Green State Univ Dept Geog Bowling Green OH 43403 USA

Lithological maps derived from traditional pixel-based image analysis are widely established in remote sensing. Recent development of new segmentation-based approaches such as object-based image analysis (OBIA) has led to significant improvements of accuracy in land cover classifications. However, additional enhancements have been reported by coupling OBIA with machine learning (ML) techniques. ML which automates computer algorithms and model building, enables learning, reasoning, and decision making through analysis and interpretation of data patterns and structures. This study is an attempt for evaluating and comparing the performances of four ML classifiers, including support vector machine (SVM), normal Bayes (NB), k-nearest neighbor (k-NN), and random forest (RF). This approach was tested with medium resolution imagery using an object-based classification procedure in a metamorphic complex, SW Iran. The multi-band input datasets were assembled form individual and multi-sensor layers from ASTER, Landsat 8 OLI, Sentinel-1 and Sentinel-2A. Results revealed that combined data sets obtain more reasonable classes for lithology, and combination of ASTER + simulated panchromatic of Sentinel-2A data shows the most efficient results. We also showed that, in general, RF and SVM algorithms were the most advantageous classifiers when using an object-based image analysis approach. When integrating the data sets aiming improvement of spatial resolution, the maximum accuracies of RF, SVM, k-NN and NB algorithms increased from 90% to 98%, 81% to 97%, 52 to 75%, and 40% to 64%, respectively. We also revealed that the integration of Sentinel-1 and Sentinel-2A data was promising and allowed for additional extraction of rock relief. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Lithology Machine learning Classification object-based image analysis Sentinel ASTER

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Classification of semiurban landscapes from very high-resolution satellite images using a regionalized multiscale segmentation approach

引用

JOURNAL OF APPLIED REMOTE SENSING 2017年第3期11卷

作者： Kavzoglu, Taskin Erdemir, Merve Yildiz Tonbul, Hasan Gebze Tech Univ Dept Geomat Engn Gebze Turkey

In object-based image analysis, obtaining representative image objects is an important prerequisite for a successful image classification. The major threat is the issue of scale selection due to the complex spatial structure of landscapes portrayed as an image. This study proposes a two-stage approach to conduct regionalized multiscale segmentation. In the first stage, an initial high-level segmentation is applied through a "broadscale," and a set of image objects characterizing natural borders of the landscape features are extracted. Contiguous objects are then merged to create regions by considering their normalized difference vegetation index resemblance. In the second stage, optimal scale values are estimated for the extracted regions, and multiresolution segmentation is applied with these settings. Two satellite images with different spatial and spectral resolutions were utilized to test the effectiveness of the proposed approach and its transferability to different geographical sites. Results were compared to those of image-based single-scale segmentation and it was found that the proposed approach outperformed the single-scale segmentations. Using the proposed methodology, significant improvement in terms of segmentation quality and classification accuracy (up to 5%) was achieved. In addition, the highest classification accuracies were produced using fine-scale values. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： object-based image analysis object-based classification very high-resolution satellite images multiscale segmentation landscape dynamics

来源：评论

学校读者我要写书评

暂无评论

analysis of Unmanned Aerial System-based CIR images in Forestry-A New Perspective to Monitor Pest Infestation Levels

引用

FORESTS 2015年第3期6卷 594-612页

作者： Lehmann, Jan Rudolf Karl Nieberding, Felix Prinz, Torsten Knoth, Christian Univ Munster Inst Landscape Ecol D-48149 Munster Germany Univ Munster Inst Geoinformat D-48149 Munster Germany

The detection of pest infestation is an important aspect of forest management. In the case of the oak splendour beetle (Agrilus biguttatus) infestation, the affected oaks (Quercus sp.) show high levels of defoliation and altered canopy reflection signature. These critical features can be identified in high-resolution colour infrared (CIR) images of the tree crown and branches level captured by Unmanned Aerial Systems (UAS). In this study, we used a small UAS equipped with a compact digital camera which has been calibrated and modified to record not only the visual but also the near infrared reflection (NIR) of possibly infested oaks. The flight campaigns were realized in August 2013, covering two study sites which are located in a rural area in western Germany. Both locations represent small-scale, privately managed commercial forests in which oaks are economically valuable species. Our workflow includes the CIR/NIR image acquisition, mosaicking, georeferencing and pixel-based image enhancement followed by object-based image classification techniques. A modified Normalized Difference Vegetation Index (NDVImod) derived classification was used to distinguish between five vegetation health classes, i.e., infested, healthy or dead branches, other vegetation and canopy gaps. We achieved an overall Kappa Index of Agreement (KIA) of 0.81 and 0.77 for each study site, respectively. This approach offers a low-cost alternative to private forest owners who pursue a sustainable management strategy.

关键词： autonomous flying beetle infection drone GIS NDVI object-based image analysis OBIA UAV

来源：评论

学校读者我要写书评

暂无评论

Optimal Combination of Classification Algorithms and Feature Ranking Methods for object-based Classification of Submeter Resolution Z/I-Imaging DMC imagery

引用

REMOTE SENSING 2015年第4期7卷 4651-4677页

作者： Canovas-Garcia, Fulgencio Alonso-Sarria, Francisco Univ Murcia Inst Univ Agua & Medio Ambiente E-30100 Murcia Spain Univ Tecn Particular Loja Dept Geol & Minas & Ingn Civil Loja Ecuador

object-based image analysis allows several different features to be calculated for the resulting objects. However, a large number of features means longer computing times and might even result in a loss of classification accuracy. In this study, we use four feature ranking methods (maximum correlation, average correlation, Jeffries-Matusita distance and mean decrease in the Gini index) and five classification algorithms (linear discriminant analysis, naive Bayes, weighted k-nearest neighbors, support vector machines and random forest). The objective is to discover the optimal algorithm and feature subset to maximize accuracy when classifying a set of 1,076,937 objects, produced by the prior segmentation of a 0.45-m resolution multispectral image, with 356 features calculated on each object. The study area is both large (9070 ha) and diverse, which increases the possibility to generalize the results. The mean decrease in the Gini index was found to be the feature ranking method that provided highest accuracy for all of the classification algorithms. In addition, support vector machines and random forest obtained the highest accuracy in the classification, both using their default parameters. This is a useful result that could be taken into account in the processing of high-resolution images in large and diverse areas to obtain a land cover classification.

关键词： random forest feature selection object-based image analysis Hughes effect photogrammetric camera classification

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：