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Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2013年第8期185卷 6261-6275页

作者： Meneguzzo, Dacia M. Liknes, Greg C. Nelson, Mark D. US Forest Serv No Res Stn USDA St Paul MN 55108 USA

Discrete trees and small groups of trees in nonforest settings are considered an essential resource around the world and are collectively referred to as trees outside forests (ToF). ToF provide important functions across the landscape, such as protecting soil and water resources, providing wildlife habitat, and improving farmstead energy efficiency and aesthetics. Despite the significance of ToF, forest and other natural resource inventory programs and geospatial land cover datasets that are available at a national scale do not include comprehensive information regarding ToF in the United States. Additional ground-based data collection and acquisition of specialized imagery to inventory these resources are expensive alternatives. As a potential solution, we identified two remote sensing-based approaches that use free high-resolution aerial imagery from the National Agriculture imagery Program (NAIP) to map all tree cover in an agriculturally dominant landscape. We compared the results obtained using an unsupervised per-pixel classifier (independent component analysis-[ICA]) and an object-based image analysis (OBIA) procedure in Steele County, Minnesota, USA. Three types of accuracy assessments were used to evaluate how each method performed in terms of: (1) producing a county-level estimate of total tree-covered area, (2) correctly locating tree cover on the ground, and (3) how tree cover patch metrics computed from the classified outputs compared to those delineated by a human photo interpreter. Both approaches were found to be viable for mapping tree cover over a broad spatial extent and could serve to supplement ground-based inventory data. The ICA approach produced an estimate of total tree cover more similar to the photo-interpreted result, but the output from the OBIA method was more realistic in terms of describing the actual observed spatial pattern of tree cover.

关键词： Trees outside forests Forest inventory Tree cover Aerial photography object-based image analysis Independent component analysis

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High-latitude wetland mapping using multidate and multisensor Earth observation data: a case study in the Northwest Territories

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JOURNAL OF APPLIED REMOTE SENSING 2020年第3期14卷

作者： Merchant, Michael Haas, Claudia Schroder, Julien Warren, Rebecca Edwards, Rebecca Ducks Unlimited Canada Natl Boreal Program Edmonton AB Canada Govt Northwest Terr Yellowknife NT Canada

The main objective in our study was to derive an accurate wetland inventory of the Dinaga Wek'ehodi region, Northwest Territories, while also enhancing our previously established wetland mapping workflow. Our methods used multidate optical and radar satellite imagery and fused these data with ArcticDEM topographic variables. Additionally, few studies to date have assessed the ArcticDEM for wetland mapping;our research helps fill this critical gap in the literature. A machine-learning, object-based approach was employed to classify the fused data stacks and included both mean and standard deviation image-object feature extractions. In this study, 18 random forest models were tested, each including various sensor inputs and feature extractions. The highest accuracy was achieved using a fusion of optical, radar, and ArcticDEM data and included both the mean and standard deviation of image objects (88.17% overall accuracy and kappa 0.858). Vegetated wetlands had producer accuracies ranging from 74% to 86%, whereas open water was 92%. Feature importance rankings indicated that 16 of the top 20 variables were derived from optical data, three from radar, and one from the ArcticDEM. The results of our study will be used to assist governments and other interested parties in advancing conservation initiatives for this significant high-latitude region. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： fusion Arctic digital elevation model object-based image analysis wetlands

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A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species

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REMOTE SENSING OF ENVIRONMENT 2012年 124卷 516-533页

作者： Pu, Ruiliang Landry, Shawn Univ S Florida Dept Geog Environm & Planning Tampa FL 33620 USA Univ S Florida Florida Ctr Community Design & Res Tampa FL 33620 USA

Urban forest tree species mapping has benefitted from advances in remote sensing techniques. In this study, we explored the potential of the newly developed high resolution satellite sensor, WorldView-2 (WV2) imagery for identifying and mapping urban tree species/groups in the City of Tampa, FL, USA by comparing capabilities between high resolution IKONOS (IKO, acquired on April 6, 2006) and WV2 (acquired on May 1, 2011) imagery for identifying the urban tree species. Seven urban tree species/groups were mapped, including: sand live oak (Quercus geminata), laurel oak (Q. laurifolia), live oak (Q. virginiana), pine (species group), palm (species group), camphor (Cinnamomum camphora), and magnolia (Magnolia grandiflora). image-objects (IOs) were used as the tree species mapping unit. A stepwise masking protocol was developed to separate sunlit and shadow/shaded tree canopy IOs from the study area prior to tree species mapping. Comparative analyses examined average accuracies of tree species mapping results between four-band IKO imagery and three different band combinations of WV2 imagery: four "traditional" bands, four additional bands, and all eight bands. Linear Discriminant analysis and Classification and Regression Trees were used to classify IOs using selected 10 features derived from IKO and band combinations of WV2 imagery. With the exception of sand live oak mapping result (due to phenological difference due to image collection dates), validation results indicate significantly improved mapping accuracies using all combinations of WV2 imagery (p<0.01). Results using independent validation samples demonstrated that average accuracy was increased by 16-18% using WV2 imagery compared to that using IKO imagery when considering mapping 6-species/group. Improved results with the WV2 sensor could be attributed to improved spatial resolution (4 m to 2 m) and additional bands (coastal, yellow, red-edge and NIR2). (c) 2012 Elsevier Inc. All rights reserved.

关键词： Urban trees WorldView-2 IKONOS object-based image analysis Stepwise masking system Linear discriminant analysis Classification and regression trees

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Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2015年 104卷 213-223页

作者： Zhang, Caiyun Florida Atlantic Univ Dept Geosci Boca Raton FL 33431 USA

Accurate mapping and effective monitoring of benthic habitat in the Florida Keys are critical in developing management strategies for this valuable coral reef ecosystem. For this study, a framework was designed for automated benthic habitat mapping by combining multiple data sources (hyperspectral, aerial photography, and bathymetry data) and four contemporary imagery processing techniques (data fusion, object-based image analysis (OBIA), machine learning, and ensemble analysis). In the framework, 1-m digital aerial photograph was first merged with 17-m hyperspectral imagery and 10-m bathymetiy data using a pixel/feature-level fusion strategy. The fused dataset was then preclassified by three machine learning algorithms (Random Forest, Support Vector Machines, and k-Nearest Neighbor). Final object-based habitat maps were produced through ensemble analysis of outcomes from three classifiers. The framework was tested for classifying a group-level (3-class) and code-level (9-class) habitats in a portion of the Florida Keys. Informative and accurate habitat maps were achieved with an overall accuracy of 88.5% and 83.5% for the group-level and code-level classifications, respectively. (c) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Data fusion Machine learning Benthic habitat mapping Hyperspectral imagery object-based image analysis Coral reef ecosystem

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Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery

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COMPUTERS & GEOSCIENCES 2017年 103卷 1-11页

作者： Canovas-Garcia, Fulgencio Alonso-Sarria, Francisco Gomariz-Castillo, Francisco Onate-Valdivieso, Fernando Univ Tecn Particular Loja Dept Geol & Minas & Ingn Civil San Cayetano Alto S-N Loja Ecuador Univ Murcia Inst Univ Agua & Medio Ambiente Edificio DCampus Espinardo S-N E-30100 Murcia Spain Univ Murcia Inst Euromediterrdneo Agua Edificio D S-N E-30100 Murcia Spain Univ Cuenca Dept Ingn Civil Av 12 Abril & Av Loja S-N Cuenca Ecuador

Random forest is a classification technique widely used in remote sensing. One of its advantages is that it produces an estimation of classification accuracy based on the so called out-of-bag cross-validation method. It is usually assumed that such estimation is not biased and may be used instead of validation based on an external data-set or a cross-validation external to the algorithm. In this paper we show that this is not necessarily the case when classifying remote sensing imagery using training areas with several pixels or objects. According to our results, out-of-bag cross-validation clearly overestimates accuracy, both overall and per class. The reason is that, in a training patch, pixels or objects are not independent (from a statistical point of view) of each other;however, they are split by bootstrapping into in bag and out-of-bag as if they were really independent. We believe that putting whole patch, rather than pixels/objects, in one or the other set would produce a less biased out-of-bag cross-validation. To deal with the problem, we propose a modification of the random forest algorithm to split training patches instead of the pixels (or objects) that compose them. This modified algorithm does not overestimate accuracy and has no lower predictive capability than the original. When its results are validated with an external data-set, the accuracy is not different from that obtained with the original algorithm. We analysed three remote sensing images with different classification approaches (pixel and object based);in the three cases reported, the modification we propose produces a less biased accuracy estimation.

关键词： Classification Random forest object-based image analysis Bagging Statistical independence

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Applying object-based segmentation in the temporal domain to characterise snow seasonality

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2014年第Nov.期97卷 98-110页

作者： Thompson, Jeffery A. Lees, Brian G. Univ New S Wales Sch Phys Environm & Math Sci Canberra ACT 2600 Australia

In the context of a changing climate it is important to be able to monitor and map descriptors of snow seasonality. Because of its relatively low elevation range, Australia's alpine bioregion is a marginal area for seasonal snow-cover with high inter-annual variability. It has been predicted that snow-cover will become increasingly ephemeral within the alpine bioregion as warming continues. To assist the monitoring of snow seasonality and ephemeral snow-cover, a remote sensing method is proposed. The method adapted principles of object-based image analysis that have traditionally be used in the spatial domain and applied them in the temporal domain. The method allows for a more comprehensive characterisation of snow seasonality relative to other methods. Using high-temporal resolution (daily) MODIS image time-series, remotely sensed descriptors were derived and validated using in situ observations. Overall, moderate to strong relationships were observed between the remotely sensed descriptors of the persistent snow-covered period (start r = 0.70, p < 0.001;end r = 0.88, p < 0.001 and duration r = 0.88, p < 0.001) and their in situ counterparts. Although only weak correspondence (r = 0.39, p < 0.05) was observed for the number of ephemeral events detected using remote sensing, this was thought to be related to differences in the sampling frequency of the in situ observations relative to the remotely sense observations. For 2009, the mapped results for the number of snow-cover events suggested that snow-cover between 1400 and 1799 m was characterised by a high numbers of ephemeral events. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： MODIS object-based image analysis Time-series Alpine Australia Snow cover Seasonality

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Incorporating crop phenological trajectory and texture for paddy rice detection with time series MODIS, HJ-1A and ALOS PALSAR imagery

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EUROPEAN JOURNAL OF REMOTE SENSING 2019年第1期52卷 73-87页

作者： Singha, Mrinal Sarmah, Sangeeta Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100100 Peoples R China Univ Chinese Acad Sci Inst Atmospher Phys Beijing Peoples R China

Repeated monitoring of paddy rice is essential for government agencies and policy makers to maintain the balance of supply and demand for rice. Recent studies have mostly concentrated on the mapping of paddy rice with temporal satellite imagery during growing seasons. Given the phenological variation within paddy rice fields and spectral confusion between paddies and other vegetation classes, our ability to identify paddy rice fields with temporal imagery remains limited. The objective of this study is to develop new phenology and textural-based strategies to detect paddies with HJ-1A, MODIS and PALSAR FNF imagery. Two phenology-based strategies that track the seasonal trajectory of crops and one textural-based strategy that contains image surface characteristics are presented. With the proposed strategies, temporal, spectral and textural features were investigated for paddy rice detection. The results indicate that the phenology-based strategies could reveal the phenological variation within paddy rice and significantly improved the detection accuracy. Seasonal amplitude, grey level co-occurrence matrix entropy and spectral features of the heading stage were proven to be important in identifying paddy rice. It was concluded that the combination of HJ-1A, MODIS and PALSAR FNF imagery are promising in facilitating the rapid mapping of paddy rice at a regional scale.

关键词： Paddy rice phenology texture MODIS NDVI HJ-1A PALSAR object-based image analysis

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The potential of Gokturk 2 satellite images for mapping burnt forest areas

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TURKISH JOURNAL OF AGRICULTURE AND FORESTRY 2021年第1期45卷 91-101页

作者： Kuruca, Murat Matci, Dilek Kocuk Avdan, Ugur Eskisehir Tech Univ Res Inst Earth & Space Sci Eskisehir Turkey

Using remotely sensed data to identify burnt forest areas produces fast, economical, and highly accurate results. Accordingly, in this study we investigate the capabilities of Gokturk-2, Turkey's national satellite, for mapping burnt forest areas. We compare our results with those obtained from Landsat-8 and Worldview-2 satellite images, which are frequently used for mapping burnt areas. The capabilities of the satellites are compared, in terms of detecting burnt forest areas, using support vector machine (SVM) and rotation forest (RF) classification, which are advanced methods.-According to the results of the accuracy analysis, SVM classification gives similar kappa statistics and overall accuracy for Gokturk-2 and Landsat-8 images, while the performance of Worldview-2 shows greater general accuracy. Although there is no significant difference between the two classification methods forLandsat-8 images, SVM gives better results than RF for both Gokturk-2 and Worldview-2. The results of our study show that Gokturk-2 images are an effective source for mapping burnt forest areas.

关键词： Gokturk-2 mapping burnt forest area object-based image analysis rotation forest support vector machine

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Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2015年第2期53卷 952-964页

作者： Michel, Julien Youssefi, David Grizonnet, Manuel French Space Agcy Ctr Natl Etud Spatiales F-31400 Toulouse France

Segmentation of real-world remote sensing images is challenging because of the large size of those data, particularly for very high resolution imagery. However, a lot of high-level remote sensing methods rely on segmentation at some point and are therefore difficult to assess at full image scale, for real remote sensing applications. In this paper, we define a new property called stability of segmentation algorithms and demonstrate that piece- or tile-wise computation of a stable segmentation algorithm can be achieved with identical results with respect to processing the whole image at once. We also derive a technique to empirically estimate the stability of a given segmentation algorithm and apply it to four different algorithms. Among those algorithms, the mean-shift algorithm is found to be quite unstable. We propose a modified version of this algorithm enforcing its stability and thus allowing for tile-wise computation with identical results. Finally, we present results of this method and discuss the various trends and applications.

关键词： image processing image segmentation mean-shift object-based image analysis remote sensing stability of segmentation

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Forest cover changes in Gorce NP (Poland) using photointerpretation of analogue photographs and GEOBIA of orthophotos and nDSM based on image-matching based approach

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EUROPEAN JOURNAL OF REMOTE SENSING 2018年第1期51卷 501-510页

作者： Wezyk, Piotr Hawrylo, Pawel Janus, Bartlomiej Weidenbach, Markus Szostak, Marta Agr Univ Krakow Fac Forestry Dept Forest Management Geomat & Forest Econ Krakow Poland LandConsult De Buhl Germany

Forest cover change can be detected with high precision using 3D geospatial data and semi-automatic analyses of Remote Sensing data. The aim of our study, performed in Gorce National Park in Poland, was to generate a land use land cover (LULC) map and use it to analyse forest cover change. The study area is a subalpine forest region that has been affected by bark beetle and wind disturbances. The Geographic object-based image analysis approach was used for classification, with Colour Infrared orthophotos and normalized Digital Surface Models generated using image-matching approach. Gathered results showed that dominating LULC class is coniferous forests (3380 ha;47% of study area), when second largest class is deciduous forests (2204 ha;30%). The dead Norway spruce stands (465.5 ha;6.5%) showed significant increase comparing to 114.1 ha mapped in 1997.

关键词： Stereomatching object-based image analysis forest change

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