检索结果-内蒙古大学图书馆

Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa

引用

SCIENCE OF THE TOTAL ENVIRONMENT 2022年 803卷 150139-150139页

作者： Gxokwe, Siyamthanda Dube, Timothy Mazvimavi, Dominic Univ Western Cape Inst Water Studies Dept Earth Sci Private Bag X17 ZA-7535 Cape Town South Africa

Although significant scientific research strides have been made in mapping the spatial extents and ecohydrological dynamics of wetlands in semi-arid environments, the focus on small wetlands remains a challenge. This is due to the sensing characteristics of remote sensing platforms and lack of robust data processing techniques. Advancements in data analytic tools, such as the introduction of Google Earth Engine (GEE) platform provides unique opportunities for improved assessment of small and scattered wetlands. This study thus assessed the capabilities of GEE cloud-computing platform in characterising small seasonal flooded wetlands, using the new generation Sentinel 2 data from 2016 to 2020. Specifically, the study assessed the spectral separability of different land cover classes for two different wetlands detected, using Sentinel-2 multi-year composite water and vegetation indices and to identify the most suitable GEE machine learning algorithm for accurately detecting and mapping semi-arid seasonal wetlands. This was achieved using the object based Random Forest (RF), Support Vector Machine (SVM), classification and Regression Tree (CART) and Naive Bayes (NB) advanced algorithms in GEE. The results demonstrated the capabilities of using the GEE platform to characterize wetlands with acceptable accuracy. All algorithms showed superiority, in mapping the two wetlands except for the NB method, which had lowest overall classification accuracy. These findings underscore the relevance of the GEE platform, Sentinel-2 data and advanced algorithms in characterizing small and seasonal semi-arid wetlands. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Limpopo River Basin object-based classification Machine learning algorithm Wetland mapping Wetland condition

来源：评论

学校读者我要写书评

暂无评论

Hierarchical object-based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery

引用

REMOTE SENSING 2016年第2期8卷 97-97页

作者： Demarchi, Luca Bizzi, Simone Piegay, Herve European Commiss Inst Environm & Sustainabil Water Resources Unit Joint Res Ctr Via E Fermi 2749 I-21027 Ispra VA Italy Univ Lyon UMR CNRS EVS 5600 ISIG Platform Site ENS Lyon15 Parvis Rene Descartes F-69362 Lyon France

In this paper, we present a new, semi-automated methodology for mapping hydromorphological indicators of rivers at a regional scale using multisource remote sensing (RS) data. This novel approach is based on the integration of spectral and topographic information within a multilevel, geographic, object-based image analysis (GEOBIA). Different segmentation levels were generated based on the two sources of Remote Sensing (RS) data, namely very-high spatial resolution, near-infrared imagery (VHR) and high-resolution LiDAR topography. At each level, different input object features were tested with Machine Learning classifiers for mapping riverscape units and in-stream mesohabitats. The GEOBIA approach proved to be a powerful tool for analyzing the river system at different levels of detail and for coupling spectral and topographic datasets, allowing for the delineation of the natural fluvial corridor with its primary riverscape units (e.g., water channel, unvegetated sediment bars, riparian densely-vegetated units, etc.) and in-stream mesohabitats with a high level of accuracy, respectively of K = 0.91 and K = 0.83. This method is flexible and can be adapted to different sources of data, with the potential to be implemented at regional scales in the future. The analyzed dataset, composed of VHR imagery and LiDAR data, is nowadays increasingly available at larger scales, notably through European Member States. At the same time, this methodology provides a tool for monitoring and characterizing the hydromorphological status of river systems continuously along the entire channel network and coherently through time, opening novel and significant perspectives to river science and management, notably for planning and targeting actions.

关键词： hydromorphology object-based classification LiDAR data VHR imagery machine learning riverscape units hierarchical classification

来源：评论

学校读者我要写书评

暂无评论

The possibilities to support ZB GIS®database update using object-based image analysis in ecognition developer software

引用

Geodezija ir Kartografija 2014年第3期40卷 122-132页

The process of updating a spatial database is a necessary part of database management in order to keep the stored information of an acceptable quality. The first step of database update requires change detection. Many methods have been suggested to detect changes, mostly pixel-based. Recently, with the spread of very high resolution images and object-based image analysis, object-based methods were developed, too. This article presents object-based change detection method for update of the vector database ZB GIS®, that is a geometric base of the Slovak National infrastructure of spatial information, using orthophoto of the area of interest. The method stages include the following: segmentation of orthophoto using the geometry of database objects and further according to spectral and spatial information, classification according to ZB GIS®, defining reclassification rules between two classes. The proposed method was tested in two localities – Malženice (agricultural landscape) and Chopok-Jasná (mountainous landscape) reaching the overall accuracy of classification 87.12% and 84.55%, respectively. The main limitation of the method is that it can be applied only for polygonal objects.

关键词： remote sensing change detection object-based image analysis object-based classification spatial database database update

来源：评论

学校读者我要写书评

暂无评论

Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France

引用

REMOTE SENSING 2022年第18期14卷 4437-4437页

作者： Xie, Guanyao Niculescu, Simona IUEM UBO Lab LETG Brest Geomer UMR 6554 CNRS F-29200 Brest France Univ Western Brittany Dept Geog 3 Rue Archives F-29238 Brest France

Crop supply and management is a global issue, particularly in the context of global climate change and rising urbanization. Accurate mapping and monitoring of specific crop types are crucial for crop studies. In this study, we proposed: (1) a methodology to map two main winter crops (winter wheat and winter barley) in the northern region of Finistere with high-resolution Sentinel-2 data. Different classification approaches (the hierarchical classification and the classical direct extraction), and classification methods (pixel-based classification (PBC) and object-based classification (OBC)) were performed and evaluated. Subsequently, (2) a further study that involved monitoring the phenology of the winter crops was carried out, based on the previous results. The aim is to understand the temporal behavior from sowing to harvesting, identifying three important phenological statuses (germination, heading, and ripening, including harvesting). Due to the high frequency of precipitation in our study area, crop phenology monitoring was performed using Sentinel-1 C-band SAR backscatter time series data using the Google Earth Engine (GEE) platform. The results of the classification showed that the hierarchical classification achieved a better accuracy when it is compared to the direct extraction, with an overall accuracy of 0.932 and a kappa coefficient of 0.888. Moreover, in the hierarchical classification process, OBC reached a better accuracy in cropland mapping, and PBC was proven more suitable for winter crop extraction. Additionally, in the time series backscatter coefficient of winter wheat, the germination and ripening (harvesting) phases can be identified at VV and VH/VV polarizations, and heading can be identified in both VV and VH polarizations. Secondly, we were able to detect the germination phase of winter barley in VV and VH, ripening with both polarizations and VH/VV, and finally, heading in VV and VH polarizations.

关键词： winter crops mapping winter crops phenology machine learning hierarchical classification object-based classification pixel-based classification Google Earth Engine (GEE) Sentinel-1 Sentinel-2

来源：评论

学校读者我要写书评

暂无评论

Estimating long-term LULC changes in an agriculture-dominated basin using CORONA (1970) and LISS IV (2013-14) satellite images: a case study of Ramganga River, India

引用

ENVIRONMENTAL MONITORING AND ASSESSMENT 2019年第4期191卷 217-217页

作者： Gurjar, Suresh Kr Tare, Vinod Indian Inst Technol Kanpur Dept Civil Engn Kanpur 208016 Uttar Pradesh India

The basin of Ramganga River, a major tributary of Ganga, has seen rapid industrialization, urbanization, and agricultural growth in modern times, especially during and after the 1970s, with consequent changes in Land Use and Land Cover (LULC) of the basin. object-based classification of seldom-used CORONA images (for 1970) for historical LULC scenario and LISS IV (Resourcesat-2) images (for 2013-14) for the present scenario of the basin was performed with high overall accuracies of 93% and 87% and Kappa coefficients of 0.89 and 0.82 respectively. object-based classification using the eCognition software tool combined with manual classification of the images revealed significant decrease in total forest cover (similar to 485km(2)) and riverbed (similar to 498km(2)) LULC classes along with correspondingly increased agriculture (similar to 898km(2)) and built-up (similar to 313km(2)) classes in the basin. Further, though Ramganga reservoir (spread over similar to 70km(2)), operated since in 1974, has increased the water area by similar to 1295% in Afzalgarh sub-basin of the Ramganga Basin, the overall area of water class decreased by 1.64% in the basin, probably due to loss of ponds in agricultural lands. An upward shift of about 42km in the confluence point of Ramganga and Gangan Rivers was also observed, which may be attributed to the course shift of Ramganga in a south-west direction.

关键词： CORONA LISS IV Channel-shift object-based classification Ramganga LULC

来源：评论

学校读者我要写书评

暂无评论

Woody Plant Encroachment: Evaluating Methodologies for Semiarid Woody Species classification from Drone Images

引用

REMOTE SENSING 2022年第7期14卷 1665-1665页

作者： Olariu, Horia G. Malambo, Lonesome Popescu, Sorin C. Virgil, Clifton Wilcox, Bradford P. Texas A&M Univ Dept Ecol & Conservat Biol College Stn TX 77598 USA

Globally, native semiarid grasslands and savannas have experienced a densification of woody plant species-leading to a multitude of environmental, economic, and cultural changes. These encroached areas are unique in that the diversity of tree species is small, but at the same time the individual species possess diverse phenological responses. The overall goal of this study was to evaluate the ability of very high resolution drone imagery to accurately map species of woody plants encroaching on semiarid grasslands. For a site in the Edwards Plateau ecoregion of central Texas, we used affordable, very high resolution drone imagery to which we applied maximum likelihood (ML), support vector machine (SVM), random forest (RF), and VGG-19 convolutional neural network (CNN) algorithms in combination with pixel-based (with and without post-processing) and object-based (small and large) classification methods. based on test sample data (n = 1000) the VGG-19 CNN model achieved the highest overall accuracy (96.9%). SVM came in second with an average classification accuracy of 91.2% across all methods, followed by RF (89.7%) and ML (86.8%). Overall, our findings show that RGB drone sensors are indeed capable of providing highly accurate classifications of woody plant species in semiarid landscapes-comparable to and even greater in some regards to those achieved by aerial and drone imagery using hyperspectral sensors in more diverse landscapes.

关键词： deep learning machine learning drones woody encroachment semiarid pixel-based classification object-based classification phenology Texas Edwards Plateau VGG-19

来源：评论

学校读者我要写书评

暂无评论

Combining Remote Sensing and a Geographic Information System to Map and Assess the Accessibility of Invasive Alien Species Forest Stands: Case of Acacia mearnsii on Reunion Island

引用

FORESTS 2023年第10期14卷 2030-2030页

作者： Dalouman, Helene Bley Broust, Francois Tran, Annelise French Agr Res Ctr Int Dev CIRAD UPR BioWooEB F-97743 St Denis France Univ Montpellier French Agr Res Ctr Int Dev CIRAD BioWooEB F-34398 Montpellier France French Agr Res Ctr Int Dev CIRAD UMR TETIS F-34398 Montpellier France Univ Montpellier Natl Res Inst Agr Food & Environm INRAE French Natl Ctr Sci Res CNRS French Agr Res Ctr Int Dev CIRADTETISAgroParisTe F-34398 Montpellier France

Acacia mearnsii, an invasive alien species, is the main resource identified for the energy transition initiated on Reunion Island. It is, therefore, critical to identify the existent forest stands within the island's large tropical forest area and to determine which of these stands are accessible for logging operations. In this study, a very high-spatial-resolution image was used to map forest (Acacia mearnsii, Acacia heterophylla, Cryptomeria japonica) and non-forest land cover. To identify the accessible forest stands, we developed a geographic information system (GIS) processing chain, accounting for barriers, the distribution of slopes, and the location of forest roads. User-defined parameters, such as the slope threshold and maximal distance to forest roads, allow different scenarios to be explored. The classification yields an overall accuracy of 88% and a Kappa index of 0.86 and highlights the areas occupied by the different types of Acacia. By applying the GIS processing chain for a standard scenario, the results show that only 19% of the initial study area of 637 ha of Acacia mearnsii patches is suitable for exploitation. In conclusion, our results demonstrate how remote sensing (RS) and GIS can be combined to provide valuable tools for forest managers to design management plans for invasive alien forest species.

关键词： Acacia mearnsii invasive alien species remote sensing object-based classification confusion index GIS forest management accessibility wood energy

来源：评论

学校读者我要写书评

暂无评论

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

引用

JOURNAL OF APPLIED REMOTE SENSING 2010年第1期4卷 041890-041890页

作者： Yang, He Ma, Ben Du, Qian Yang, Chenghai Mississippi State Univ Dept Elect & Comp Engn Geosyst Res Inst Mississippi State MS 39762 USA ARS USDA Kika de la Garza Subtrop Agr Res Ctr Weslaco TX 78596 USA

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.

关键词： urban land use land cover mapping hyperspectral imaging support vector machine pixel-based classification object-based classification

来源：评论

学校读者我要写书评

暂无评论

Crop classification in a Heterogeneous Arable Landscape Using Uncalibrated UAV Data

引用

REMOTE SENSING 2018年第8期10卷 1282-1282页

作者： Bohler, Jonas E. Schaepman, Michael E. Kneubuhler, Mathias Univ Zurich Remote Sensing Labs Dept Geog Winterthurerstr 190 CH-8057 Zurich Switzerland

Land cover maps are indispensable for decision making, monitoring, and management in agricultural areas, but they are often only available after harvesting. To obtain a timely crop map of a small-scale arable landscape in the Swiss Plateau, we acquired uncalibrated, very high-resolution data, with a spatial resolution of 0.05 m and four spectral bands, using a consumer-grade camera on an unmanned aerial vehicle (UAV) in June 2015. We resampled the data to different spatial and spectral resolutions, and evaluated the method using textural features (first order statistics and mathematical morphology), a random forest classifier for best performance, as well as number and size of the structuring elements. Our main findings suggest the overall best performing data consisting of a spatial resolution of 0.5 m, three spectral bands (RGBred, green, and blue), and five different sizes of the structuring elements. The overall accuracy (OA) for the full set of crop classes based on a pixel-based classification is 66.7%. In case of a merged set of crops, the OA increases by similar to 7% (74.0%). For an object-based classification based on individual field parcels, the OA increases by similar to 20% (OA of 86.3% for the full set of crop classes, and 94.6% for the merged set, respectively). We conclude the use of UAV to be most relevant at 0.5 m spatial resolution in heterogeneous arable landscapes when used for crop classification.

关键词： consumer-grade camera land cover very high resolution (VHR) random forest (RF) classifier object-based classification pixel-based classification small-scaled agricultural fields texture spatial feature

来源：评论

学校读者我要写书评

暂无评论

Optimal Envisat advanced synthetic aperture radar image parameters for mapping and monitoring Sahelian floodplains

引用

JOURNAL OF APPLIED REMOTE SENSING 2010年第1期4卷 043511-043511页

作者： Westra, Toon De Wulf, Robert Van Coillie, Frieke Crabbe, Sarah Univ Ghent Lab Forest Management & Spatial Informat Tech FOR B-9000 Ghent Belgium

Floodplains in the Sahel region of Africa are of exceptional socio-economical and ecological importance. Due to their large extent and highly dynamic nature, monitoring these ecosystems can only be performed by means of remote sensing. The capability of the Envisat Advanced Synthetic Aperture Radar (ASAR) sensor to capture radar backscattering at various incident angles and with different polarization combinations, provides opportunities for improved wetland mapping and monitoring. However, little is known of the optimal image parameters, i.e. incident angle, polarization combination, and acquisition time. Backscatter sigma(o) signatures of Land Use and Land Cover (LULC) classes in and around the Waza-Logone floodplain (Cameroon) were analyzed to determine these optimal image parameters. based on Jeffries-Matusita (JM) distances between all LULC classes it was determined that best separation was obtained with images acquired in the middle of the flooding cycle at a steep incident angle. Furthermore, separability of cross-polarized images was higher than for co-polarized images. The combination of two and three ASAR Alternating Polarization images with highest separability were used as input for a LULC classification. Two methods were evaluated: Pixel-based Maximum Likelihood and object-based Nearest Neighbour (NN) classification. Best results were obtained with the object-based approach.

关键词： SAR Envisat ASAR wetlands object-based classification

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：