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Classifying Forest Structure of Red-Cockaded Woodpecker Habitat Using Structure from Motion Elevation Data De-Rived from sUAS Imagery

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DRONES 2022年第1期6卷 26-26页

作者： Lawrence, Brett Raven Environm Serv Inc Huntsville TX 77320 USA Texas A&M Univ Coll Geosci Dept Geog College Stn TX 77843 USA

Small unmanned aerial systems (sUAS) and relatively new photogrammetry software solutions are creating opportunities for forest managers to perform spatial analysis more efficiently and cost-effectively. This study aims to identify a method for leveraging these technologies to analyze vertical forest structure of red-cockaded woodpecker habitat in Montgomery County, Texas. Traditional sampling methods would require numerous hours of ground surveying and data collection using various measuring techniques. Structure from Motion (SfM), a photogrammetric method for creating 3-D structure from 2-D images, provides an alternative to relatively expensive LIDAR sensing technologies and can accurately model the high level of complexity found within our study area's vertical structure. DroneDeploy, a photogrammetry processing app service, was used to post-process and create a point cloud, which was later further processed into a Canopy Height Model (CHM). Using supervised, object-based classification and comparing multiple classifier algorithms, classifications maps were generated with a best overall accuracy of 84.8% using Support Vector Machine in ArcGIS Pro software. Appropriately sized training sample datasets, correctly processed elevation data, and proper image segmentation were among the major factors impacting classification accuracy during the numerous classification iterations performed.

关键词： Structure from Motion (SfM) small unmanned aerial system (sUAS) object-based classification supervised classification eCognition DroneDeploy photogrammetry

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classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four classification Algorithms

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REMOTE SENSING 2021年第17期13卷 3433页

作者： Aghababaei, Masoumeh Ebrahimi, Ataollah Naghipour, Ali Asghar Asadi, Esmaeil Verrelst, Jochem Shahrekord Univ Fac Nat Resources & Earth Sci Dept Range & Watershed Management Shahrekord *** Iran Univ Valencia Image Proc Lab IPL Parc Cient Paterna 46980 Spain

Plant Ecological Unit's (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran. We used images of Landsat-8 OLI that were pan-sharpened to 15 m to classify four PEU classes based on a random dataset collected in the field (40%). In the first stage, we applied the following classification algorithms to distinguish PEUs: Minimum Distance (MD), Maximum Likelihood classification (MLC), Neural Network-Multi Layer Perceptron (NN-MLP) and classification Tree Analysis (CTA) for pixel based method and object based method. Then, by using the most accurate classification approach, in the second stage auxiliary data (Principal Component Analysis (PCA)) was incorporated to improve the accuracy of the PEUs classification process. At the end, test data (60%) were used for accuracy assessment of the resulting maps. object-based maps clearly outperformed pixel-based maps, especially with CTA, NN-MLP and MD algorithms with overall accuracies of 86%, 72% and 59%, respectively. The MLC algorithm did not reveal any significant difference between the object-based and pixel-based analyses. Finally, complementing PCA auxiliary bands to the CTA algorithms offered the most successful PEUs classification strategy, with the highest overall accuracy (89%). The results clearly underpin the importance of object-based classification with the CTA classifier together with PCA auxiliary data to optimize identification of PEU classes.

关键词： object-based classification machine learning algorithms principal component analysis plant ecological units mapping

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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 1285-1285页

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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"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 916-916页

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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Development of an algorithm for identification of sown biodiverse pastures in Portugal

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EUROPEAN JOURNAL OF REMOTE SENSING 2023年第1期56卷

作者： Morais, Tiago G. Rodrigues, Nuno R. Gama, Ivo Domingos, Tiago Teixeira, Ricardo F. M. Univ Lisbon MARETEC Marine Environm & Technol Ctr LARSyS Inst Super Tecn Lisbon Portugal Terraprima Serv Ambientais Soc Unipessoal Samora Correia Portugal Univ Lisbon MARETEC Marine Environm & Technol Ctr LARSyS Inst Super Tecn Ave Rovisco Pais 1 P-1049001 Lisbon Portugal

Sown biodiverse pastures (SBP) are a pasture system developed in Portugal. Until 2014, farmers were supported in installing and maintaining SBP, but tracking their locations has been lacking. To survey the country, remote sensing tools with machine learning were used. Here, we developed the first algorithm that combines remote sensing data with machine learning algorithms to identify SBP areas. The algorithm combines Landsat-7 and night-light spectral data with terrain and bioclimatic data. Remotely sensed data offer higher spatial resolution compared to bioclimatic data and also cover interannual variability. Gradient-boosted decision trees (XGB) and artificial neural networks (ANN) were the machine learning methods used. The overall classification accuracy, on an independent validation dataset, was 94%, with 82% producer accuracy and 85% user accuracy. The total estimated area of SBP in the Portuguese region of Alentejo region was 1300 km2 in 2013, which is similar to the total known installed area (approximately 1000 km(2)). The estimated spatial distribution is in accordance with the known distribution at the municipal level. These results are a critical first step towards the future development of remote systems for assessing the state of SBP and for compliance checks of farmer commitments.

关键词： object-based classification remote sensing Landsat-7 XGB artificial neural network cross-validation >

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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 2133-2133页

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

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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

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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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