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An assessment of Hawaiian dry forest condition with fine resolution remote sensing

FOREST ECOLOGY AND MANAGEMENT 2008年第7期255卷 2524-2532页

作者： Morales, Rodolfo Martinez Miura, Tomoaki Idol, Travis Univ Hawaii Dept Nat Resources & Environm Management Honolulu HI 96822 USA

Remote sensing offers the potential to spatially map forest cover quickly and reliably for inventory purposes. We developed a new image analysis approach using an integrated methodology of "object-based" image classification techniques and field-based measurements to quantify forest cover in a degraded dry forest ecosystem on the leeward side of the Island of Hawaii. This new approach explicitly recognized the transitional areas between tree crowns and tree shades (tree shadows) as a unique class and fully utilized them for the quantification of canopy cover. object-oriented classification of Ikonos-2 satellite images allowed delineation of tree shades and crowns and the transitional areas between them from objects with similar reflectance and size that were surrounding the trees. These included patches of fountain (Pennisetum setaceum) and kikuyu (Pennisetum clandestinum) grass, lava outcrops and lava-grass mixtures. Crown-shade transitions were clearly differentiated in spite of their wide range of spectral values and reflectance similarities with areas of lava-grass mixture. Segments representing tree shades and dark lava outcrops were also classified into their respective classes even if they were contiguous. The image estimates of canopy cover using the tree shade plus transition classes were linearly related with field estimates of canopy cover (R-2 = 0.86 and slope = 0.976). based on this relationship, dry forest cover throughout the 2627-ha area was estimated at 7.7 +/- 1.9%. An immediate application of this new approach is to select and delineate areas with higher canopy cover in order to concentrate ecological restoration and conservation efforts. Published by Elsevier B.V.

关键词： dry forest Hawaii transitional area object-based classification Ikonos-2

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Mapping the rapid development of photovoltaic power stations in northwestern China using remote sensing

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ENERGY REPORTS 2022年 8卷 4117-4127页

作者： Xia, Zilong Li, Yingjie Chen, Ruishan Sengupta, Dhritiraj Guo, Xiaona Xiong, Bo Niu, Yilong Nanjing Univ Sch Geog & Ocean Sci Nanjing 210023 Jiangsu Peoples R China Michigan State Univ Ctr Syst Integrat & Sustainabil Dept Fisheries & Wildlife E Lansing MI 48823 USA Michigan State Univ Environm Sci & Policy Program E Lansing MI 48823 USA Shanghai Jiao Tong Univ Sch Design Shanghai 200241 Peoples R China East China Normal Univ State Key Lab Estuarine & Coastal Res Shanghai 200241 Peoples R China East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China

Many leading countries are boosting renewables, especially solar energy, as a major way to mitigate future energy crises and climate change. Particularly, in China, the number and scale of photovoltaic (PV) power stations have grown unprecedentedly in the last decade. There is an urgent need to monitor the PV power development in order to accurately estimate national renewable potentials and understand the ecological impacts. However, there are few efforts towards providing spatially explicit and time-series datasets of PV development at the regional and national scales. To fill the gap, this study proposes an integrated remote sensing approach for PV power stations mapping by combining image segmentation and object-based classification (ISOC) techniques. We took five northwestern provinces of China as an illustration and produced 30-m medium-resolution PV power station distribution maps from 2007 to 2019. Our analysis shows that the total area of PV power stations in the five provinces increased to 722 km(2) in 2019, with producer, user and overall accuracies of 86%, 100% and 93%. Of the 309 PV station clusters (hereafter, PV parks), the top 7% largest ones account for 61% of the total area of PV power stations, indicating that PV power stations in the Northwest tend to be developed in the form of large-scale centralized PV parks. The land used for PV power stations was mainly converted from four land cover types: Gobi Desert, sandy land, sparse grassland, and moderate grassland. The central government policy on facilitating clean energy played a major role in driving the rapid expansion of PV parks across the country. The methodology and results of this study will help policymakers, researchers, and practitioners to develop corresponding industrial standards and environmental regulations to mitigate the potential environmental impacts, and promote more sustainable development of solar energy in China and beyond. (c) 2022 The Author(s). Published by Elsevier Ltd. T

关键词： Renewable energy Carbon neutrality Climate actions Landsat object-based classification

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

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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

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Per-Field Irrigated Crop classification in Arid Central Asia Using SPOT and ASTER Data

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REMOTE SENSING 2010年第4期2卷 1035-1056页

作者： Conrad, Christopher Fritsch, Sebastian Zeidler, Julian Ruecker, Gerd Dech, Stefan Univ Wurzburg Remote Sensing Unit D-97074 Wurzburg Germany German Remote Sensing Data Ctr DFD German Aerosp Ctr DLR D-82234 Oberpfaffenhofen Wessling Germany

The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5-5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15-30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.

关键词： object-based classification segmentation tasselled cap Uzbekistan irrigated agriculture multi-sensor

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Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image

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JOURNAL OF VOLCANOLOGY AND GEOTHERMAL RESEARCH 2017年第Oct.1期345卷 109-124页

作者： Li, Long Solana, Carmen Canters, Frank Kervyn, Matthieu China Univ Min & Technol Sch Environm Sci & Spatial Informat Daxue Rd 1 Xuzhou 221116 Peoples R China Vrije Univ Brussel Earth Syst Sci Dept Geog Pl Laan 2 B-1050 Brussels Belgium Univ Portsmouth Sch Earth & Environm Sci Burnaby BldgBurnaby Rd Portsmouth PO1 3QL Hants England Vnje Univ Brussel Dept Geog Cartog & GIS Res Grp Pl Laan 2 B-1050 Brussels Belgium

Mapping lava flows using satellite images is an important application of remote sensing in volcanology. Several volcanoes have been mapped through remote sensing using a wide range of data, from optical to thermal infrared and radar images, using techniques such as manual mapping, supervised/unsupervised classification, and elevation subtraction. So far, spectral-based mapping applications mainly focus on the use of traditional pixel-based classifiers, without much investigation into the added value of object-based approaches and into advantages of using machine learning algorithms. In this study, Nyamuragira, characterized by a series of >20 overlapping lava flows erupted over the last century, was used as a case study. The random forest classifier was tested to map lava flows based on pixels and objects. Image classification was conducted for the 20 individual flows and for 8 groups of flows of similar age using a Landsat 8 image and a DEM of the volcano, both at 30-meter spatial resolution. Results show that object-based classification produces maps with continuous and homogeneous lava surfaces, in agreement with the physical characteristics of lava flows, while lava flows mapped through the pixel-based classification are heterogeneous and fragmented including much "salt and pepper noise". In terms of accuracy, both pixel-based and object-based classification performs well but the former results in higher accuracies than the latter except for mapping lava flow age groups without using topographic features. It is concluded that despite spectral similarity, lava flows of contrasting age can be well discriminated and mapped by means of image classification. The classification approach demonstrated in this study only requires easily accessible image data and can be applied to other volcanoes as well if there is sufficient information to calibrate the mapping. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Lava flows Nyamuragira Pixel-based classification object-based classification Random forest

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Accurate determination of surface reference data in digital photographs in ice-free surfaces of Maritime Antarctica

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SCIENCE OF THE TOTAL ENVIRONMENT 2016年 573卷 290-302页

作者： Pina, Pedro Vieira, Goncalo Bandeira, Lourenco Mora, Carla Univ Lisbon Inst Super Tecn CERENA Ave Rovisco Pais P-1049001 Lisbon Portugal Univ Lisbon CEG Inst Geog & Ordenamento Terr Rua Branca Edmee Marques P-1600276 Lisbon Portugal

The ice-free areas of Maritime Antarctica show complex mosaics of surface covers, with wide patches of diverse bare soils and rock, together with various vegetation communities dominated by lichens and mosses. The micro scale variability is difficult to characterize and quantify, but is essential for ground-truthing and for defining classifiers for large areas using, for example high resolution satellite imagery, or even ultra-high resolution unmanned aerial vehicle (UAV) imagery. The main objective of this paper is to verify the ability and robustness of an automated approach to discriminate the variety of surface types in digital photographs acquired at ground level in ice-free regions of Maritime Antarctica. The proposed method is based on an object-based classification procedure built in two main steps: first, on the automated delineation of homogeneous regions (the objects) of the images through the watershed transform with adequate filtering to avoid an over-segmentation, and second, on labelling each identified object with a supervised decision classifier trained with samples of representative objects of ice-free surface types (bare rock, bare soil, moss and lichen formations). The method is evaluated with images acquired in summer campaigns in Fildes and Barton peninsulas (King George Island, South Shetlands). The best performances for the datasets of the two peninsulas are achieved with a SVM classifier with overall accuracies of about 92% and kappa values around 0.89. The excellent performances allow validating the adequacy of the approach for obtaining accurate surface reference data at the complete pixel scale (sub-metric) of current very high resolution (VHR) satellite images, instead of a common single point sampling. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Image analysis Segmentation object-based classification Ice-free surfaces Polar regions

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Mapping Postfire Vegetation Recovery Using EO-1 Hyperion Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2010年第3期48卷 1613-1618页

作者： Mitri, George H. Gitas, Ioannis Z. Univ Trieste Dept Biol I-34127 Trieste Italy Aristotle Univ Thessaloniki Sch Forestry & Nat Environm Lab Forest Management & Remote Sensing Thessaloniki 54124 Greece

The aim of this paper is to investigate whether it is possible to accurately map postfire vegetation recovery on the Mediterranean island of Thasos by employing Earth Observing- 1 (EO-1) Hyperion imagery and object-based classification. Specific objectives include the following: 1) locating and mapping areas of forest regeneration and other vegetation recovery and distinguishing among them;2) distinguishing between Pinus brutia regeneration and Pinus nigra regeneration within the area of forest regeneration;and 3) examining whether it is possible to distinguish between areas of forest regeneration (Pinus brutia, Pinus nigra) and mature forest. The data used in this study consist of satellite images, field-spectroradiometry measurements, and field observations of the homogenous revegetated areas. The methodology comprised four consecutive steps. The first step involved preprocessing of the Hyperion image and field data. Subsequently, an object-oriented model was developed, which involved three steps, namely, image segmentation, object training, and object classification. The process resulted in the separation of five classes ("brutia mature," "nigra mature," "brutia regeneration," "nigra regeneration," and "other vegetation"). The accuracy assessment revealed very promising results (approximately 75.81% overall accuracy, with a Kappa Index of Agreement of 0.689). Some classification confusion involving the classes of Pinus brutia regeneration and Pinus nigra regeneration was recorded. This could be attributed to the absence of large homogenous areas of regenerated pine trees. The main conclusion drawn in this paper was that object-based classification can be used to accurately map postfire vegetation recovery using EO-1 Hyperion imagery.

关键词： Hyperspectral remote sensing object-based classification vegetation recovery

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Use of textural measurements to map invasive wetland plants in the Hudson River National Estuarine Research Reserve with IKONOS satellite imagery

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REMOTE SENSING OF ENVIRONMENT 2010年第4期114卷 876-886页

作者： Laba, Magdeline Blair, Betsy Downs, Roger Monger, Bruce Philpot, William Smith, Stephen Sullivan, Patrick Baveye, Philippe C. Cornell Univ Dept Civil & Environm Engn Ithaca NY 14853 USA Bard Coll Field Stn Hudson River NERR NYSDEC Annandale On Hudson NY 12504 USA Cornell Univ Inst Resource Informat Sci Ithaca NY 14853 USA Cornell Univ Dept Earth & Atmospher Sci Ithaca NY 14853 USA Cornell Univ Dept Nat Resources Ithaca NY 14853 USA Abertay Univ SIMBIOS Ctr Dundee DD1 1HG Scotland

At this point, models, and accompanying field data, that could be used to predict the likely response of estuaries and tidal marshes to future environmental change are lacking. To improve this situation, monitoring efforts in these complex ecosystems need to be intensified, and new, efficient monitoring techniques should be developed. In this context, our research assessed the use of IKONOS satellite imagery to map plant communities at Tivoli Bays, in the Hudson River National Estuarine Research Reserve (HRNERR). Tivoli Bays, a freshwater tidal wetland, contains a unique assemblage of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). To study the effects of textural information on the accuracy of land cover maps produced for the HRNERR, seven different 11-class land cover maps were produced using a maximum-likelihood classification on seven combinations of spectral and textural data derived from an IKONOS image. Conventional contingency tables served as a basis for an accuracy assessment of these maps. The overall classification accuracies, as assessed by the contingency tables, ranged from 45% to 77.7%. The maximum-likelihood classification relying on four spectral and four 5-by-5 filter textural bands (created by superposing a textural filter separately on each band of the IKONOS image) had the lowest overall accuracy, whereas the one based on four spectral and four 3-by-3 filter textural bands associated with all segments, identified by an object-based classification of the IKONOS image, had the highest accuracy. Results suggest that a combination of per-pixel classification and incorporation of texture for segments generated through an object-based classification slightly increases classification accuracy from 76.2% for the maximum-likelihood classification of the four spectral bands of the IKONOS image to 77.7% for the combination of spectral and textural information produced for selected segments. F

关键词： NERR Ikonos object-based classification Texture Image segmentation Invasive Wetlands Estuarine National Estuarine Research Reserve

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Multi-Sensor Monitoring System for Forest Cover Change Assessment in Central Africa

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2013年第1期6卷 110-120页

作者： Desclee, Baudouin Simonetti, Dario Mayaux, Philippe Achard, Frederic Commiss European Communities Joint Res Ctr Inst Environm & Sustainabil I-21020 Ispra Italy Arcadia SIT I-21020 Ispra Italy

Forest monitoring from earth observation is crucial over tropical regions to assess forest extent and provide up-to-date estimates of deforestation rates. based on a systematic sample of 20x20 km size sites, a processing chain has been developed at the European Commission's Joint Research Centre (JRC) for producing deforestation estimates between years 1990, 2000 and 2005. Whereas this monitoring exercise was based on Landsat imagery, limitations in Landsat availability over Central Africa for year 2010 required alternative imagery such as the Disaster Monitoring Constellation (DMC). The classification module of the existing JRC processing chain is based on tasseled caps analysis (TCap-based). We adapted this module to DMC imagery by selecting the most suitable object-features through their assessments using a sub-sample of existing land-cover maps of years 1990 and 2000. The processing chain is adapted for the production of land-cover change maps between years 2000 and 2010. The accuracy of the land-cover maps produced for year 2010 with the two methods (original TCap-based and adapted Multi-Sensor) is assessed through a reference dataset. Overall accuracies are similar for both approaches (93% and 95% respectively), but the Multi-Sensor approach shows a significant improvement when considering only changed objects (83% overall accuracy versus 56% for TCap-based). Our results show that, even by using DMC imagery with lower radiometric quality (compared to Landsat) an automated classification can provide land-cover maps with similar accuracy thanks to an appropriate object-features selection. Similar adaptations need to be developed for other satellite imagery such as SPOT and RapidEye.

关键词： Change detection DMC deforestation forest mapping and monitoring Landsat TM object-based classification multi-temporal analysis remote sensing

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Monitoring recent changes of vegetation in Fildes Peninsula (King George Island, Antarctica) through satellite imagery guided by UAV surveys

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 704卷 135295-135295页

作者： Miranda, Vasco Pina, Pedro Heleno, Sandra Vieira, Goncalo Mora, Carla Schaefer, Carlos E. G. R. Univ Lisbon IST Ctr Recursos Nat & Ambiente CERENA P-1049001 Lisbon Portugal Univ Lisbon IGOT CEG P-1600276 Lisbon Portugal Univ Fed Vicosa Dept Solos BR-36571000 Vicosa MG Brazil

Mapping accurately vegetation surfaces in space and time in the ice-free areas of Antarctica can provide important information to quantitatively describe the evolution of their ecosystems. Spaceborne remote sensing is the adequate way to map and evaluate multitemporal changes on the Antarctic vegetation at large but its nature of occurrence, in relatively small and sparse patches, makes the identification very challenging. The inclusion of an intermediate scale of observation between ground and satellite scales, provided by Unmanned Aerial Vehicles (UAV) imagery, is of great help not only for their effective classification, but also for discriminating their main communities (lichens and mosses). Thus, this paper quantifies accurately recent changes of the vegetated areas in Fildes Peninsula (King George Island, Antarctica) through a novel methodology based on the integration of multiplatform data (satellite and UAV). It consists of multiscale imagery (spatial resolution of 2 m and 2 cm) from the same period to create a robust classifier that, after intensive calibration, is adequately used in other dates, where field reference data is scarce or not available at all. The methodology is developed and tested with UAV and satellite data from 2017 showing overall accuracies of 96% and kappa equal to 0.94 with a SVM classifier. These high performances allow the extrapolation to a pair of previous dates, 2006 and 2013, when atmospherically clear very high-resolution satellite imagery are available. The classification allows verifying a loss of the total area of vegetation of 4.5% during the 11-year time period under analysis, which corresponds to a 10.3% reduction for Usnea sp. and 9.8% for moss formations. Nevertheless, the breakdown analysis by time period shows a distinct behaviour for each vegetation type which are evaluated and discussed, namely for Usnea sp. whose decline is likely to be related to changing snow conditions. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Vegetation Mapping Change detection UAV Satellite object-based classification Permafrost Antarctica

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