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"Kill Two Birds with One Stone": Urban Tree Species classification Using Bi-Temporal Pleiades Images to Study Nesting Preferences of an Invasive Bird

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REMOTE SENSING 2017年第9期9卷

作者： Le Louarn, Marine Clergeau, Philippe Briche, Elodie Deschamps-Cottin, Magali Aix Marseille Univ IRD LPED Marseille France Museum Natl Hist Nat UMR CESCO 43 Rue BuffonCP135 F-75005 Paris France

This study presents the results of object-based classifications assessing the potential of bi-temporal Pleiades images for mapping broadleaf and coniferous tree species potentially used by the ring-necked parakeet Psittacula krameri for nesting in the urban area of Marseille, France. The first classification was performed based solely on a summer Pleiades image (acquired on 28 July 2015) and the second classification based on bi-temporal Pleiades images (a spring image acquired on 24 March 2016 and the summer image). An ensemble of spectral and textural features was extracted from both images and two machine-learning classifiers were used, Random Forest (RF) and Support Vector Machine (SVM). Regardless of the classifiers, model results suggest that classification based on bi-temporal Pleiades images produces more satisfying results, with an overall accuracy 11.5-13.9% higher than classification using the single-date image. Textural and spectral features extracted from the blue and the NIR bands were consistently ranked among the most important features. Regardless of the classification scheme, RF slightly outperforms SVM. RF classification using bi-temporal Pleiades images allows identifying 98.5% of the tree species used by the ring-necked parakeet for nesting, highlighting the promising value of remote sensing techniques to assess the ecological requirements of fauna in urban areas.

关键词： urban trees Pleiades random forest support vector machine object-based classification Psittacula krameri

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Evaluation of object-based Greenhouse Mapping Using WorldView-3 VNIR and SWIR Data: A Case Study from Almeria (Spain)

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REMOTE SENSING 2021年第11期13卷

作者： Aguilar, Manuel A. Jimenez-Lao, Rafael Aguilar, Fernando J. Univ Almeria Dept Engn Carretera Sacramento S-N Almeria 04120 Spain Univ Almeria Res Ctr CIAIMBITAL Carretera Sacramento S-N Almeria 04120 Spain

Plastic covered greenhouse (PCG) mapping via remote sensing has received a great deal of attention over the past decades. The WorldView-3 (WV3) satellite is a very high resolution (VHR) sensor with eight multispectral bands in the visible and near-infrared (VNIR) spectral range, and eight additional bands in the short-wave infrared (SWIR) region. A few studies have already established the importance of indices based on some of these SWIR bands to detect urban plastic materials and hydrocarbons which are also related to plastics. This paper aims to investigate the capability of WV3 (VNIR and SWIR) for direct PCG detection following an object-based image analysis (OBIA) approach. Three strategies were carried out: (i) using object features only derived from VNIR bands (VNIR);(ii) object features only derived from SWIR bands (SWIR), and (iii) object features derived from both VNIR and SWIR bands (All Features). The results showed that the majority of predictive power was attributed to SWIR indices, especially to the Normalized Difference Plastic Index (NDPI). Overall, accuracy values of 90.85%, 96.79% and 97.38% were attained for VNIR, SWIR and All Features strategies, respectively. The main PCG misclassification problem was related to the agricultural practice of greenhouse whitewash (greenhouse shading) that temporally masked the spectral signature of the plastic film.

关键词： WorldView-3 object-based classification greenhouse mapping SWIR bands plastic materials Normalized Difference Plastic Index (NDPI)

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Long-Time Interval Satellite Image Analysis on Forest-Cover Changes and Disturbances around Protected Area, Zeya State Nature Reserve, in the Russian Far East

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REMOTE SENSING 2021年第7期13卷

作者： Khatancharoen, Chulabush Tsuyuki, Satoshi Bryanin, Semyon V. Sugiura, Konosuke Seino, Tatsuyuki Lisovsky, Viktor V. Borisova, Irina G. Wada, Naoya Univ Tokyo Grad Sch Agr & Life Sci Dept Global Agr Sci 1-1-1Yayoi Tokyo 1138654 Japan Branch Russian Acad Sci Far Eastern Inst Geol & Nat Management Relochny Lane Blagoveshchensk 675000 Amur Region Russia Toyama Univ Ctr Far Eastern Studies Gofuku 3190 Toyama 9308555 Japan Univ Tsukuba Fac Life & Environm Sci 1-1-1 Ten Noudai Tsukuba Ibaraki 3058572 Japan Zeya State Nat Reserve Stroiteley St 71 Zeya 676246 Amur Region Russia Russian Acad Sci Far Eastern Branch Bot Garden Inst Amur Branch Ignat Evskoe Shosse Str2nd Kilometer Blagoveshchensk 675000 Amur Region Russia

Boreal forest areas in the Russian Far East contained very large intact forests. This particular area is considered one of the most productive and diverse forests in the boreal biome of the world, and it is also home to many endangered species. Zeya State Nature Reserve is located at the southern margin of the boreal forest area in the Russian Far East and has rich fauna and flora. However, the forest in the region faced large-scale forest fires and clearcutting for timber recently. The information of disturbances is rarely understood. This study aimed to explore the effects of disturbance and forest dynamics around the reserve. Our study used two-year overlaid Landsat images from Landsat 5 Thematic Mapper (TM) and 8 Operational Land Imager (OLI), to generate forest-cover-change maps of 1988-1999, 1999-2010, and 2010-2016. In this paper, we analyze the direction of forest successional stages, to demonstrate the effectiveness of this protected area in terms of preventing human-based deforestation on the vegetation indices. The vegetation indices included the normalized burn ratio (NBR), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI). The study provided information on the pattern of forest-cover change and disturbance area around the reserve. The NDWI was used to differentiate between water and non-water areas. The mean values of NBR and NDVI were calculated and determine the forest successional stages between burn, vegetation recovery, grass, mixed forest, oak forest, and birch and larch forest. The accuracy was assessed by using field measurements, field photos, and high-resolution images as references. Overall, our classification results have high accuracy for all three periods. The most disturbed area occurred during 2010-2016. The reserve was highly protected, with no human-disturbance activity. However, large areas from fire disturbance were found (137 km(2)) during 1999-2010. The findings also show a large ar

关键词： object-based classification deforestation wildfire Amur region remote sensing

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The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning

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ECOLOGICAL INDICATORS 2022年 139卷

作者： Morin, Elie Herrault, Pierre-Alexis Guinard, Yvonnick Grandjean, Frederic Bech, Nicolas Univ Poitiers Lab Ecol & Biol Interact UMR CNRS 7267 5 Rue Albert Turpin F-86022 Poitiers France Univ Strasbourg Lab Image Ville Environm UMR CNRS 7362 3 Rue Argonne F-67000 Strasbourg France Grand Poitiers Communaute Urbaine F-86000 Poitiers France

Urban landscapes are rapid changing ecosystems with diverse urban forms that impede the movement of organisms. Therefore, designing and modelling ecological networks to identify biodiversity reservoirs and their corridors are crucial aspects of land management in terms of population persistence and survival. However, the land cover/use maps used for landscape connectivity modelling can lack information in such a highly complex environment. In this context, remote sensing approaches are gaining interest for the development of accurate land cover/use maps. We tested the efficiency of an object-based classification using open-source projects and free images to identify vegetation strata at a very fine scale and evaluated its contribution to landscape connectivity modelling. We compared different spatial and thematic resolutions from existing databases and object based image analyses in three French cities. Our results suggested that this remote sensing approach produced reliable land cover maps to differentiate artificial areas, tree vegetation and herbaceous vegetation. Land cover maps enhanced with the remote sensing products substantially changed the structural connectivity indices, showing an improvement up to four times the proportion of herbaceous and tree vegetation. In addition, functional connectivity indices evaluated for several forest species were mainly impacted for medium dispersers in quantitative (metrics) and qualitative (corridors) estimations. Thus, the combination of this reproductible remote sensing approach and landscape connectivity modelling at a very fine scale provides new insights into the characterisation of ecological networks for conservation planning.

关键词： object-based classification Landscape connectivity modelling Graph theory Urban Dispersal Very high resolution

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Možnosti objektově-orientované klasifikace pro určování vybraných biotopů nad horní hranicí lesa v Krkonošském národním parku.

Možnosti objektově-orientované klasifikace pro určován...

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作者： Jakešová, Lucie Charles University of Prague

Possibilities of object-based classification for selected biotopes above tree-line in the Krkonoše Mts. National Park detection Abstract The bachelor thesis is focused on the object-based classification of vegetation above the tree-line in the Krkonoše Mts. National Park using orthophoto with near infrared band and spatial resolution of 12.5 cm. Orthophoto was acquired in summer 2012. The classification legend was compiled by botanist of the national park. Software ENVI 5.1 was used for object-based classification using the field data. It provides two approaches to classification - Example-based and Rule-based. The overall accuracy of the best classification result was 75.97 % for 13 classes. Keywords: object-based classification, KRNAP, biotopes above tree-line, aerial optical scanner

关键词： object-based classification KRNAP biotopes above tree-line aerial optical scanner

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A comparison of automated land cover/use classification methods for a Texas bottomland hardwood system using lidar, spot-5, and ancillary data

A comparison of automated land cover/use classification meth...

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作者： Vernon, Zachary Isaac Texas A&M University

Bottomland hardwood forests are highly productive ecosystems which perform many important ecological services. Unfortunately, many bottomland hardwood forests have been degraded or lost. Accurate land cover mapping is crucial for management decisions affecting these disappearing systems. SPOT-5 imagery from 2005 was combined with Light Detection and Ranging (LiDAR) data from 2006 and several ancillary datasets to map a portion of the bottomland hardwood system found in the Sulphur River Basin of Northeast Texas. Pixel-based classification techniques, rulebased classification techniques, and object-based classification techniques were used to distinguish nine land cover types in the area. The rule-based classification (84.41% overall accuracy) outperformed the other classification methods because it more effectively incorporated the LiDAR and ancillary datasets when needed. This output was compared to previous classifications from 1974, 1984, 1991, and 1997 to determine abundance trends in the area’s bottomland hardwood forests. The classifications from 1974-1991 were conducted using identical class definitions and input imagery (Landsat MSS 60m), and the direct comparison demonstrates an overall declining trend in bottomland hardwood abundance. The trend levels off in 1997 when medium resolution imagery was first utilized (Landsat TM 30m) and the 2005 classification also shows an increase in bottomland hardwood from 1997 to 2005, when SPOT-5 10m imagery was used. However, when the classifications are re-sampled to the same resolution (60m), the percent area of bottomland hardwood consistently decreases from 1974-2005. Additional investigation of object-oriented classification proved useful. A major shortcoming of object-based classification is limited justification regarding the selection of segmentation parameters. Often, segmentation parameters are arbitrarily defined using general guidelines or are determined through a large number of parameter combinations. This

关键词： Bottomland hardwood forests object-based classification rule-based classification segmentation Book Thesis

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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

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REMOTE SENSING 2022年第18期14卷

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

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Interpretation of Forest Resources at the Individual Tree Level at Purple Mountain, Nanjing City, China, Using WorldView-2 Imagery by Combining GPS, RS and GIS Technologies

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REMOTE SENSING 2014年第1期6卷 87-110页

作者： Deng, Songqiu Katoh, Masato Guan, Qingwei Yin, Na Li, Mingyang Shinshu Univ Fac Agr Forest Measurement & Planning Lab Nagano 3994598 Japan Nanjing Forestry Univ Forest Resources & Environm Fac Nanjing 210037 Jiangsu Peoples R China Shinshu Univ Fac Agr Forest Environm & Ecol Lab Nagano 3994598 Japan

This study attempted to measure forest resources at the individual tree level using high-resolution images by combining GPS, RS, and Geographic Information System (GIS) technologies. The images were acquired by the WorldView-2 satellite with a resolution of 0.5 m in the panchromatic band and 2.0 m in the multispectral bands. Field data of 90 plots were used to verify the interpreted accuracy. The tops of trees in three groups, namely 10 cm, 15 cm, and 20 cm DBH (diameter at breast height), were extracted by the individual tree crown (ITC) approach using filters with moving windows of 3 x 3 pixels, 5 x 5 pixels and 7 x 7 pixels, respectively. In the study area, there were 1,203,970 trees of DBH over 10 cm, and the interpreted accuracy was 73.68 +/- 15.14% averaged over the 90 plots. The numbers of the trees that were 15 cm and 20 cm DBH were 727,887 and 548,919, with an average accuracy of 68.74 +/- 17.21% and 71.92 +/- 18.03%, respectively. The pixel-based classification showed that the classified accuracies of the 16 classes obtained using the eight multispectral bands were higher than those obtained using only the four standard bands. The increments ranged from 0.1% for the water class to 17.0% for Metasequoia glyptostroboides, with an average value of 4.8% for the 16 classes. In addition, to overcome the mixed pixels problem, a crown-based supervised classification, which can improve the classified accuracy of both dominant species and smaller classes, was used for generating a thematic map of tree species. The improvements of the crown- to pixel-based classification ranged from -1.6% for the open forest class to 34.3% for Metasequoia glyptostroboides, with an average value of 20.3% for the 10 classes. All tree tops were then annotated with the species attributes from the map, and a tree count of different species indicated that the forest of Purple Mountain is mainly dominated by Quercus acutissima, Liquidambar formosana and Pinus massoniana. The findings from t

关键词： 3S technology forest resource measurement individual tree crown approach object-based classification Purple Mountain

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classification of Tundra Vegetation in the Krkonose Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

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EUROPEAN JOURNAL OF REMOTE SENSING 2017年第1期50卷 29-46页

作者： Kupkova, Lucie Cervena, Lucie Sucha, Renata Jakesova, Lucie Zagajewski, Bogdan Brezina, Stanislav Albrechtova, Jana Charles Univ Prague Dept Appl Geoinformat & Cartog Fac Sci Albertov 6 Prague 12843 2 Czech Republic Univ Warsaw Fac Geog & Reg Studies Dept Geoinformat Cartog & Remote Sensing Krakowskie Przedmiescie 30 Warsaw Poland Krkonose Mt Natl Pk Adm Dobrovskeho 3 Vrchlabi Czech Republic Charles Univ Prague Dept Expt Plant Biol Fac Sci Vinicna 5 Prague 2 Czech Republic

The aim of this study was to evaluate and compare suitability of aerial hyperspectral data (AISA Dual and APEX sensors) and Sentinel-2A data for classification of tundra vegetation cover in the Krkonose Mts. National Park. We compared classification results (accuracy, maps) of pixel-based (Maximum Likelihood, Suport Vector Machine and Neural Net) and object-based approaches. The best classification results (overall accuracy 84.3%, Kappa coefficient = 0.81) were achieved for AISA Dual data using per-pixel SVM classifier for 40 PCA bands. The best classification results of APEX though were only 1.7 percentage points lower. To get comparable results for Sentinel-2A classification legend had to be simplified. With the simplified legend the accuracy using MLC classifier reached 77.7%.

关键词： Tundra vegetation The Krkonose Mountains Per-pixel classification object-based classification Hyperspectral data Sentiel-2A

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Multi-Source Remote Sensing Data for Wetland Information Extraction: A Case Study of the Nanweng River National Wetland Reserve

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SENSORS 2024年第20期24卷 6664页

作者： Yu, Hao Li, Shicheng Liang, Zhimin Xu, Shengnan Yang, Xin Li, Xiaoyan Jilin Jianzhu Univ Modern Ind Coll Changchun 130118 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Peoples R China Chang Guang Satellite Technol Co Ltd Res Dept Changchun 130102 Peoples R China Jilin Univ Coll Earth Sci Changchun 130012 Peoples R China

Wetlands play a vital role in regulating the global carbon cycle, providing biodiversity, and reducing flood risks. These functions maintain ecological balance and ensure human well-being. Timely, accurate monitoring of wetlands is essential, not only for conservation efforts, but also for achieving Sustainable Development Goals (SDGs). In this study, we combined Sentinel-1/2 images, terrain data, and field observation data collected in 2020 to better understand wetland distribution. A total of 22 feature variables were extracted from multi-source data, including spectral bands, spectral indices (especially red edge indices), terrain features, and radar features. To avoid high correlations between variables and reduce data redundancy, we selected a subset of features based on recursive feature elimination (RFE) and Pearson correlation analysis methods. We adopted the random forest (RF) method to construct six wetland delineation schemes and incorporated multiple types of characteristic variables. These variables were based on remote sensing image pixels and objects. Combining red-edge features, terrain data, and radar data significantly improved the accuracy of land cover information extracted in low-mountain and hilly areas. Moreover, the accuracy of object-oriented schemes surpassed that of pixel-level methods when applied to wetland classification. Among the three pixel-based schemes, the addition of terrain and radar data increased the overall classification accuracy by 7.26%. In the object-based schemes, the inclusion of radar and terrain data improved classification accuracy by 4.34%. The object-based classification method achieved the best results for swamps, water bodies, and built-up land, with relative accuracies of 96.00%, 90.91%, and 96.67%, respectively. Even higher accuracies were observed in the pixel-based schemes for marshes, forests, and bare land, with relative accuracies of 98.67%, 97.53%, and 80.00%, respectively. This study's methodology can pr

关键词： sentinel mission multi-source data pixel-based classification object-based classification land cover

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