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Mapping Two Decades of New York State Forest Aboveground Biomass Change Using Remote Sensing

REMOTE SENSING 2022年第16期14卷 4097-4097页

作者： Tamiminia, Haifa Salehi, Bahram Mahdianpari, Masoud Beier, Colin M. Johnson, Lucas SUNY Coll Environm Sci & Forestry ESF Dept Environm Resources Engn Syracuse NY 13210 USA Mem Univ Newfoundland CORE St John NL A1B 3X5 Canada Mem Univ Newfoundland Dept Elect & Comp Engn St John NL A1B 3X5 Canada SUNY Coll Environm Sci & Forestry SUNY ESF Sustainable Resources Management Syracuse NY 13210 USA SUNY Coll Environm Sci & Forestry SUNY ESF Grad Program Environm Sci Syracuse NY 13210 USA

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, which is a valuable source for historical mapping over large areas. As such, this paper proposed a machine learning-based workflow for historical AGB estimation and its change analysis from 2001 to 2019 for the New York State's forests using Landsat historical imagery, airborne LiDAR, and forest plot data. As the object-based image analysis (OBIA) is able to incorporate spectral, contextual, and textural features into the regression model, the proposed method utilizes an OBIA approach and a random forest (RF) regression model implemented on the Google Earth Engine (GEE) cloud computing platform. Results demonstrated that there is a considerable decrease of 983.79 x 10(6) Mg/ha in the AGB of deciduous forests from 2001 to 2006, followed by an increase of 618.28 x 10(6) Mg/ha from 2006 to 2011, continued with an increase of 229.12 x 10(6) Mg/ha of deciduous forests from 2011-2016. Finally, the results demonstrated a slight change in AGB from 2016 to 2019. The transferability of the proposed framework provides a practical solution for monitoring forests in other states or even on a national scale.

关键词： object-based image analysis Landsat imagery airborne LiDAR change detection state-wide mapping

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AUTOMATED DETECTION OF MALE EIDERS OVER MULTISPECTRAL AERIAL PHOTOGRAPHS

AUTOMATED DETECTION OF MALE EIDERS OVER MULTISPECTRAL AERIAL...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Haddadi, A. Leblon, B. Gilliland, S. Mahoney, M. Douglas, A. Univ New Brunswick Fac Forestry & Environm Management Fredericton NB Canada Canadian Wildlife Serv Sackville NB Canada Southern Gulf St Lawrence Coalit Sustainabil Stratford PE Canada

ISBN: (纸本)9781665403696

An automated method has been developed to detect male eiders (COEI_M) on aerial multispectral images. Potential bird regions were determined after filtering the images with an average filter, masking out the ocean background and the land areas, and applying a watershed segmentation algorithm. Large and small objects were then removed. For each remaining object and its neighborhood, spectral and geometrical variables were computed. The variables that were not significantly different between the true and false COEI_M classes were removed based on a p-value of higher than 0.08 computed by an ANOVA. The objects were divided into training and validation datasets. A support vector machine using a radial basis function was used to classify the objects in the two classes. We achieved an overall accuracy of 98.6% with the validation data set. The user's accuracy and producer's accuracy for the true COEI_M class in the validation dataset were 86.4% and 92.7%, respectively.

关键词： bird detection object-based image analysis support vector machine American common eider Somateria mollisima dresseri

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Unsupervised mapping of a hybrid urban area in South Africa

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APPLIED GEOMATICS 2021年第4期13卷 619-643页

作者： Ikokou, Guy Blanchard Smit, Julian Tshwane Univ Technol Geomat Dept Pretoria South Africa Univ Cape Town Geomat Div Cape Town South Africa

Hybrid urban areas are dominated by important spectral mixtures from formal and informal housing units which make them difficult to map even for the most robust classifier. Proposals to introduce other descriptive features, such as size, shape, texture, and context into the classification process, come with another drawback which is how to ensure the selected feature thresholds are optimal. image segmentation which is the backbone of object-based analysis depends on a range of parameters including scale parameter, shape, smoothness, colour, and compactness weighting factors. Current techniques to select optimal segmentation scales only give the remote sensing analyst control over one parameter out of five (20%). This study proposes a classification strategy that gives the analyst control of 60% of the parameters to ensure an acceptable segmentation outcome. The study also proposes a feature selection approach that eliminates feature overlaps within the feature space which may not be observable within the original data. An automatic optimal parameter selection function is also proposed in this study. Tested on a SPOTS resolution merge image, the approach overpowered the accuracy metrics of (Kemper et al. in Int Arch Photogramm Remote Sens Spat Inform Sci 40(7): 1389, 2015) with overall, sensitivity, specificity, precision, true skill statistic accuracies of respectively 0.97, 0.96, 1, 0.942, 0.95 against 0.97, 0.804, 0.98, 0.477, and 0.781. Similar trends are observed with the smallest average error of omission for built-up and non-built structures at 0.042 and 0 against to 0.196 and 0.164. The errors of commission for built-up and non-built-up structures were 0.060 and 0.008 respectively compared to 0.523 and 0.585.

关键词： Urban mapping object-based image analysis Informal settlement image segmentation parameters Principal component Chebyshev's rule

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Extraction of Broad-Leaved Tree Crown based on UAV Visible images and OBIA-RF Model: A Case Study for Chinese Olive Trees

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REMOTE SENSING 2022年第10期14卷 2469页

作者： Yang, Kaile Zhang, Houxi Wang, Fan Lai, Riwen Fujian Agr & Forestry Univ Forestry Coll Fuzhou 350002 Peoples R China State Forestry & Grassland Adm Soil & Water Conse Key Lab Fuzhou 350002 Peoples R China Cross Strait Collaborat Innovat Ctr Soil & Water Fuzhou 350002 Peoples R China Univ Key Lab Geomat Technol & Optimized Resources Fuzhou 350002 Peoples R China

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images.

关键词： remote sensing unmanned aerial vehicle object-based image analysis random forest feature selection tree crown extraction

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A DETERMINATION OF THE EARTHQUAKE DISASTER AREA BY object-based analysis USING A SINGLE SATELLITE image 37

A DETERMINATION OF THE EARTHQUAKE DISASTER AREA BY OBJECT-BA...

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IEEE International Geoscience & Remote Sensing Symposium

作者： Park, Jonggeol Harada, Ippei Kwak, Youngjoo Tokyo Univ Informat Sci Wakaba Ku 4-1 Onaridai Chiba 2658501 Japan Int Ctr Water Hazard & Risk Management Tsukuba Ibaraki 3058516 Japan

ISBN: (纸本)9781509049516

The final goal of this study is to create data to support emergency efforts in a disaster affected area by locating damaged buildings shortly after the disaster. In this study, prioritizing the practicality of the method for emergency purposes, we designed a method only to use a single satellite image of an affected area, eliminating the use of complex algorithms and auxiliary data. The uniqueness of our method lies in the application of an object-based region segmentation to images and the use of features of objects obtained from texture, hierarchical and other information in order to extract damaged buildings. Out of 26 features resulting from the analysis of objects, we found one feature and three combinations of two different features that are effective in extracting damaged buildings, such as Rectangular fit, Homogeneity, Number of sub-objects/Area, and Length of longest of edge/Area.

关键词： Earthquake disaster area object-based image analysis GeoEye-1

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A novel per pixel and object-based ensemble approach for flood susceptibility mapping

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GEOMATICS NATURAL HAZARDS & RISK 2020年第1期11卷 2147-2175页

作者： Gudiyangada Nachappa, Thimmaiah Meena, Sansar Raj Univ Salzburg Dept Geoinformat Z GIS Salzburg Austria

Conducting flood susceptibility assessments is critical for the identification of flood hazard zones and the mitigation of the detrimental impacts of floods in the future through improved flood management measures. The significance of this study is that we create ensemble methods using the per-pixel approaches of frequency ratio (FR), analytical hierarchical process (AHP), and evidence belief function (EBF) used for weightings with the object-based 'geons' approach used for aggregation to create a flood susceptibility map for the East Rapti Basin in Nepal. We selected eight flood conditioning factors considered to be relevant in the study area. The flood inventory data for the East Rapti basin was derived from past flood inventory datasets held in the regional database system by the International Centre for Integrated Mountain Development (ICIMOD). The flood inventory was classified into training and validation datasets based on the widely used split ratio of 70/30. The Receiver Operating Characteristic (ROC) was used to determine the accuracy of the flood susceptibility maps. The AUC results indicated that the combined per-pixel and object-based geon approaches yielded better results than the per-pixel approaches alone. Our results showed that the object-based geon approach creates meaningful regional units that are beneficial for future planning.

关键词： Floods flood susceptibility object-based image analysis FR AHP EBF natural hazards

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Watching the Saltmarsh Grow: A High-Resolution Remote Sensing Approach to Quantify the Effects of Wetland Restoration

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REMOTE SENSING 2022年第18期14卷 4559-4559页

作者： Rummell, Ashley J. Leon, Javier X. Borland, Hayden P. Elliott, Brittany B. Gilby, Ben L. Henderson, Christopher J. Olds, Andrew D. Univ Sunshine Coast Sch Sci Technol & Engn Maroochydore Qld 4558 Australia

Coastal wetlands are restored to regenerate lost ecosystem services. Accurate and frequent representations of the distribution and area of coastal wetland communities are critical for evaluating restoration success. Typically, such data are acquired through laborious, intensive and expensive field surveys or traditional remote sensing methods that can be erroneous. Recent advances in remote sensing techniques such as high-resolution sensors (<2 m resolution), object-based image analysis and shallow learning classifiers provide promising alternatives but have rarely been applied in a restoration context. We measured the changes to wetland communities at a 200 ha restoring coastal wetland in eastern Australia, using remotely sensed Worldview-2 imagery, object-based image analysis and random forest classification. Our approach used structural rasters (digital elevation and canopy height models) and a multi-temporal technique to distinguish between spectrally similar land cover. The accuracy of our land cover maps was high, with overall accuracies ranging between 91 and 95%, and this supported early detection of increases in the area of key ecosystems, including mixed she-oak and paperbark (10 ha), mangroves (0.91 ha) and saltmarsh (4.31 ha), over a 5-year monitoring period. Our approach provides coastal managers with an accurate and frequent method for quantifying early responses of coastal wetlands to restoration, which is essential for informing adaptive management in the regeneration of ecosystem services.

关键词： coastal wetland restoration Worldview-2 object-based image analysis change detection multi-temporal

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Monitoring post-fire neighborhood competition effects on pine saplings under different environmental conditions by means of UAV multispectral data and structure-from-motion photogrammetry

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022年 305卷 114373-114373页

作者： Manuel Fernandez-Guisuraga, Jose Calvo, Leonor Suarez-Seoane, Susana Univ Leon Fac Biol & Environm Sci Area Ecol Leon 24071 Spain Univ Oviedo Dept Organisms & Syst Biol Ecol Unit Oviedo Mieres Spain Univ Oviedo Res Unit Biodivers UO CSIC PA Oviedo Mieres Spain

In burned landscapes, the recruitment success of the tree dominant species mainly depends on plant competition mechanisms operating at fine spatial scale, that may hinder resource availability during the former years after the disturbance. Data acquisition at very high spatial resolution from unmanned aerial vehicles (UAV) have promoted new opportunities for understanding context-dependent competition processes in post-fire environments. Here, we explored the potentiality of UAV-borne data for assessing inter-specific competition effects of understory woody vegetation on pine saplings, as well as intra-specific interactions of neighboring saplings, across three burned landscapes located along a climatic/productivity gradient in the Iberian Peninsula. Geographic object-based image analysis (GEOBIA), including multiresolution segmentation and support vector machine (SVM) classification, was used to map pine saplings and understory shrubs at species level. Input data were, on the one hand, multispectral (11.31 ***(-1)) and Structure-from-Motion (SfM) canopy height model (CHM) data fusion, hereafter MS-CHM, and, on the other, RGB (3.29 ***(-1)) and CHM data fusion, hereafter RGB-CHM. A Random Forest (RF) regression algorithm was used to evaluate the effects of neighborhood competition on the relative growth in height of 50 pine saplings randomly sampled across the MS-CHM classified map. Circular plots of 3 m radius were set from the centroid of each target pine sapling to measure percentage cover, mean height of all individuals in the plot and mean height of individuals contacting the target sapling. Competing shrub species were differentiated according to their fire-adaptive traits (i.e. seeders vs resprouters). object-based image classification applied on MS-CHM yielded higher overall accuracy for the three sites (83.67% +/- 3.06%) than RGB-CHM (74.33% +/- 3.21%). Intra-specific competitive effects were not detected, whereas increasing cover and height of sh

关键词： Competition Fire object-based image analysis Pine Shrub Unmanned aerial vehicle

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Integrating Semi-Supervised Learning with an Expert System for Vegetation Cover Classification Using Sentinel-2 and RapidEye Data

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REMOTE SENSING 2022年第15期14卷 3605页

作者： Layegh, Nasir Farsad Darvishzadeh, Roshanak Skidmore, Andrew K. Persello, Claudio Krueger, Nina Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands MOSS Comp Graf Syst GmbH Hohenbrunner Weg 13 D-82024 Munich Germany

In complex classification tasks, such as the classification of heterogeneous vegetation covers, the high similarity between classes can confuse the classification algorithm when assigning the correct class labels to unlabelled samples. To overcome this problem, this study aimed to develop a classification method by integrating graph-based semi-supervised learning (SSL) and an expert system (ES). The proposed method was applied to vegetation cover classification in a wetland in the Netherlands using Sentinel-2 and RapidEye imagery. Our method consisted of three main steps: object-based image analysis (OBIA), integration of SSL and an ES (SSLES), and finally, random forest classification. The generated image objects and the related features were used to construct the graph in SSL. Then, an independently developed and trained ES was used in the labelling stage of SSL to reduce the uncertainty of the process, before the final classification. Different spectral band combinations of Sentinel-2 were then considered to improve the vegetation classification. Our results show that integrating SSL and an ES can result in significantly higher classification accuracy (83.6%) compared to a supervised classifier (64.9%), SSL alone (71.8%), and ES alone (69.5%). Moreover, utilisation of all Sentinel-2 red-edge spectral band combinations yielded the highest classification accuracy (overall accuracy of 83.6% with SSLES) compared to the inclusion of other band combinations. The results of this study indicate that the utilisation of an ES in the labelling process of SSL improves the reliability of the process and provides robust performance for the classification of vegetation cover.

关键词： semi-supervised learning expert system object-based image analysis Sentinel-2 vegetation cover image classification

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An object based framework for building change analysis using 2D and 3D information of high resolution satellite images

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ADVANCES IN SPACE RESEARCH 2020年第6期66卷 1386-1404页

作者： Mohammadi, Hamid Samadzadegan, Farhad Univ Tehran Coll Engn Sch Surveying & Geospatial Engn North Kargar Ave Tehran *** Iran

With the rapid change of urban areas in developing countries, construction areas are constantly appearing in different parts of cities. Those changed areas require timely monitoring to provide up-to-date information for of urban information systems. As a result, it is a challenge to develop an effective change analysis of different objects, especially buildings in cities. This paper presents an object-based framework for analyzing building changes from high-resolution satellite stereo images (HRSSI). The disparity information extracted from stereo images and spectral information including visible vegetation index (VVI) help extract the buildings in a hierarchical approach. Evaluations show the accuracy of higher than 98% and F1-Score of higher than 87% for the building extraction step. Then, each building object is classified into three main categories including "Remained Building", "Removed Building" or "Added Building". Also, the "Remained Building" objects are categorized into four change states including "Only 2D Change", "Only 3D Change", "2D with 3D Change" and "No Change". This is done by utilizing the object-based similarity analysis of the spectral information as well as the similarity analysis of the disparity information using CNN and their integration. Evaluations demonstrate the accuracy of higher than 97% and F1-Score of higher than 90% for this step. (C) 2020 Published by Elsevier Ltd on behalf of COSPAR.

关键词： Building extraction Building change analysis High resolution satellite stereo images 2D and 3D information object-based image analysis Convolutional neural network

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