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Assessment of immediate and five-year earthquake impacts on river systems in sabah, Malaysia using multi-temporal satellite imageries

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GEOENVIRONMENTAL DISASTERS 2024年第1期11卷 16页

作者： Chai, Lee Ting Nainar, Anand Roslee, Rodeano Wong, Wilson Vun Chiong Phua, Mui-How Univ Malaysia Sabah Fac Trop Forestry Jalan UMS Kota Kinabalu 88400 Sabah Malaysia Univ Malaysia Sabah Fac Sci & Nat Resources Jalan UMS Kota Kinabalu 88400 Sabah Malaysia Univ Malaysia Sabah Nat Disaster Res Ctr NDRC Jalan UMS Kota Kinabalu 88400 Sabah Malaysia

Background Earthquake is one of the most destructive natural disasters, which cause immediate and long-term changes to the river systems. This research aimed to examine the immediate and five-year impacts of the 2015 Ranau Earthquake (6.0 Mw) on river systems in Malaysian state of Sabah, a region of low earthquake *** We used object-based classification on Landsat 8 (2014 and 2015) and Sentinel-2A (2020) satellite imageries to derive land cover time series for investigating the impacts on the riparian *** The earthquake removed vegetation in the riparian zones of four rivers, the highest being the Penataran River (69.21 ha). During the immediate impact period (2014-2015), river bar formation occurred in all rivers, with the largest increase occurring in the Kadamaian River (56.97 ha), followed by the Panataran River (54.36 ha), which had no river bar before the earthquake. The river bar of the Kadamaian River continued to increase, whereas the river bar of the Panataran River decreased five years after the earthquake. Land cover transition analysis revealed that 78.39 ha of vegetation, barren land, and river water areas changed to river bars in the Kadamaian riparian area during the immediate impact period. Except for 26.87% of river bars in the Kadamaian riparian area in 2015, most river bars transitioned to other land cover types five years later. During the period of immediate impact, 22.05 ha of vegetation and 10.71 ha of river water were transformed into river bars along the Penataran River. Five years later, except for 16.2 ha, all river bar areas had transitioned to other cover types. Additionally, 17.7 ha of new river bars were formed. This study provides crucial data on post-earthquake land cover changes, particularly river bar formation and changes, for assessing the earthquake impacts on the river systems and supporting impact mitigation.

关键词： Earthquake Post-earthquake impacts Land cover transition object-based classification

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Tree species classification based on WorldView-2 imagery in complex urban environment 3

Tree species classification based on WorldView-2 imagery in ...

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3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA)

作者： Li, Dan Ke, Yinghai Gong, Huili Chen, Beibei Zhu, Lin Capital Normal Univ Base State Key Lab Urban Environm Proc & Digital Beijing Peoples R China

ISBN: (纸本)9781479941841

Urban vegetation, particularly trees, plays important roles in the urban ecosystems. In this study, we examined the potential of World View-2 imagery (acquired on September 14, 2012) for urban tree species classification in the capital city of Beijing, China. Four tree species including Chinese white poplar(Populus tomentosa Carriere),Chineses scholartree(Sophora Japonica), Gingko(Ginkgo biloba L.)and Paulownia(Paulownia Sieb.) were identified. To evaluate the impact of complex urban environment on urban tree species classification, we compared classification accuracies on shadow-removed image and shadow-recovered image. object-based hierarchical approach was used to detect shadow/non-shadow area and vegetated/non-vegetated area. Support-Vector-Machine method was used to classify tree species in vegetated area using object-based approach. We used Linear Correlation Correction(LCC) method to restore spectral information under shadowed area. The results show that tree species classification for shadow-removed imagery obtained overall accuracy of 80.97% and kappa value of 0.7286, while the accuracy and kappa value decreased to 76.88% and 0.6693when shadow-recovered image was used. In future research, we will explore the method to improve the accuracy of classification for shadow-recovered image.

关键词： urban tree species WorldView-2 shadow recovery object-based classification

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Building roof extraction as data for suitability analysis

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APPLIED GEOMATICS 2020年第4期12卷 455-466页

作者： Adeleke, A. K. Smit, J. L. Univ Cape Town Sch Architecture Planning & Geomat Geomat Div Private Bag X3 ZA-7700 Rondebosch South Africa

The geographic potentials of building roofs in urban areas are often under-utilized or sometimes neglected entirely. This is due to the lack of large-scale building roof inventory in the municipality planning offices in African cities. Building rooftops offer great potentials in addressing key urbanization problems such as suitability analysis, disaster management, and others. This paper seeks to demonstrate how remote sensing techniques can be applied to detect and extract building roofs for various suitability analyses (e.g., solar energy and rainwater harvesting) by developing a unique two-in-one technique, which detects and extracts building roof planes and building roof outlines. Using the City of Cape Town, South Africa, as a case study, building rooftops were extracted using the integration of light detection and ranging (LiDAR) data and aerial imagery in an object-based rule-set classification process. This resulted in a building roof inventory, which has crucial attributes of evaluating a building roof for various suitability analyses. The technique developed is well suited for residential and industrial areas where buildings are easily separated from each other. The method and the extracted results were accessed for accuracy and were found to compare well with similar methods already benchmarked.

关键词： Urban extraction Suitability analysis object-based classification Data fusion

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ESTIMATING CROP COVER FRACTION FROM DIGITAL COLOR IMAGES 4

ESTIMATING CROP COVER FRACTION FROM DIGITAL COLOR IMAGES

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4th International Workshop on Geoinformation Science / 4th ISPRS International Workshop on Multi-Dimensional and Multi-Scale Spatial Data Modeling (GeoAdvances)

作者： Karakus, P. Karabork, H. OKU Engn Fac Dept Geomat Engn TR-80000 Osmaniye Turkey SU Dept Geomat Engn Engn Fac TR-42075 Konya Turkey

The use of automated methods to estimate crop cover fraction from digital color images has increased in recent years. Crop cover fraction can determine accurate, fast and inexpensive with this methods. A digital color images was acquired over each of the 30 sample fields in 2014 year at 2-3 week intervals. Study area has 15 sunflower fields and 15 corn fields. Digital color images were collected during 4 months, namely over the course of the growing season from sowing until harvesting to determine crop cover fraction. We used two approach to estimate crop cover fraction. In first method, the images were transformed from the RGB (red, green, blue) color space to the HSI (hue, intensity, saturation) color space. We used an object-based image analysis approach to classify the images into green vegetation and the other materials. In the second method, The Green Crop Tracker is less labor and time intensive than the object-based classification approach, is a viable alternative to ground-based methods. By comparing object-based classification method and Green Crop Tracker software 2014 growing season, results were obtained: There were high correlations between the estimations obtained by object-based classification method and Green Crop Tracker software (for 2014 R-2=0.89). The relationship between two methods for 2014-23 sunflower field was calculated R-2=0.97.

关键词： Crop Cover Fraction The Green Crop Tracker RGB to HSI Digital Camera object-based classification Corn Sunflower

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Detection and Mapping of Phragmites australis using High Resolution Multispectral and Hyperspectral Satellite Imagery

Detection and Mapping of Phragmites australis using High Res...

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作者： Nicholas J. Lantz University of Western Ontario

学位级别：硕士

Mapping invasive plant species is important to establish an invasion baseline, monitor plant propagation, and to implement an effective plan to deal with the invasion. In this thesis, methods are proposed to map invasive Phragmites australis in a Great Lakes coastal wetland. Chapter 2 presents an object-based Phragmites extraction method using Worldview-2 high-spatial-resolution satellite imagery. For the 4024 ha study area at Walpole Island, Ontario, 94% overall accuracy was achieved. Chapter 3 uses CHRIS PROBA hyperspectral satellite imagery for mapping the pixel abundance of Phragmites using a spectral mixture analysis method. An evaluation method was developed to assess the accuracy of the spectral mixture analysis fractions using the classification from Chapter 2. The overall accuracy for a Phragmites, native vegetation and water classification based on the dominant fraction in each pixel was 82.8%. A Phragmites invasion classification identifying pixels where Phragmites was non-dominant, potentially dominant, and dominant was 85.2% accurate.

关键词： Phragmites australis Invasive Species Non-Native Species Great Lakes Coastal Wetland Worldview-2 CHRIS PROBA object-based classification Spectral Mixture Analysis Walpole Island First Nation

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Understanding Community and Ecophysiology of Plant Species on the Colorado Plateau

Understanding Community and Ecophysiology of Plant Species o...

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作者： Yokum, Hannah Elizabeth Brigham Young University

学位级别：M.Sc., Master of Science

The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pi

关键词： Cclimate change Plant sensitivity object-based classification Pixel-based classification Ecophysiology

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Identification of Agricultural Land Use in California Through Remote Sensing

Identification of Agricultural Land Use in California Throug...

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作者： Robinson, Todd Allen California Polytechnic State University

学位级别：M.Sc., Master of Science

Ground truthing actual crop types in an area can be expensive and timeconsuming. The California Department of Water Resources attempts to ground truth land use in each county in California every five years. However, this is limited by budgetary constraints and often results in infrequent (more than every ten years) surveying of many counties. An accurate accounting of crops growing in a region is important for a variety of purposes including farm production estimates, groundwater and surface water modeling, evapotranspiration estimation, water planning, research applications, etc. Agricultural land use is continually changing due to development and environmental factors. Currently, USDA NASS provides georeferenced land use maps of regions throughout the U.S. While these are beneficial, the accuracy is not very high for California due to the wide variety of crops grown throughout the state. California has an increasingly complex agricultural system which includes multi-crops changing on an annual and even semiannual basis, long growing seasons, and complex and flexible irrigation schedules. Remotely sensed data from available satellites are used to more accurately classify crop types within the Madera and Merced Counties of California’s Central Valley. An initial classification approach utilizing a simplified decision tree for a data subset of the area considered is presented. In order to accommodate the larger dataset at hand, a computer based approach is applied using the Nearest Neighbor classification algorithm in the computer program eCognition. Iterative analyses were performed to consider a range of scenarios with varying spectral inputs. The results show the methods presented can be beneficial in discriminating 24 of the major crop types from multi-temporal spectral data.

关键词： remote sensing land classification decision tree eCognition Phenology object-based classification

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object-based Urban Building Footprint Extraction and 3D Building Reconstruction from Airborne LiDAR Data

Object-based Urban Building Footprint Extraction and 3D Buil...

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作者： Ting Zhao University of Western Ontario

学位级别：硕士

Buildings play an essential role in urban intra-construction, urban planning, climate studies and disaster management. The precise knowledge of buildings not only serves as a primary source for interpreting complex urban characteristics, but also provides decision makers with more realistic and multidimensional scenarios for urban management. In this thesis, the 2D extraction and 3D reconstruction methods are proposed to map and visualize urban buildings. Chapter 2 presents an object-based method for extraction of building footprints using LiDAR derived NDTI (Normalized Difference Tree Index) and intensity data. The overall accuracy of 94.0% and commission error of 6.3% in building extraction is achieved with the Kappa of 0.84. Chapter 3 presents a GIS-based 3D building reconstruction method. The results indicate that the method is effective for generating 3D building models. The 91.4% completeness of roof plane identification is achieved, and the overall accuracy of the flat and pitched roof plane classification is 88.81%, with the user's accuracy of the flat roof plane 97.75% and pitched roof plane 100%.

关键词： Building Footprints LiDAR multiple returns NDTI object-based classification GIS 3D Reconstruction

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Proposta de classificação e de pós-classificação baseada em objetos da cobertura e do uso da terra por meio de imagens obtidas por Veículo Aéreo Não Tripulado (VANT)

Proposta de classificação e de pós-classificação basead...

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作者： Ruiz, Luis Fernando Chimelo

One of the main applications of remote sensing images is both land cover and land use classifications. For more refined mapping, Unmanned Aerial Vehicle (UAV) aerial images are currently used. However, these images show both high intraclass and interclasses spectral variability, making it difficult to achieve land cover and land use classifications. based on these considerations, this thesis aims: both to develop and to evaluate a nonparametric pattern recognition method for object-based land cover and land use classification, which is called Iterative K - Nearest Neighbors Algorithm (IKNN); both to propose and to evaluate two postclassification methods that consider the objects’ context, Voting Neighbors objects (VNO) and Quantification of object Frontiers (QOF); and to develop an automated tool for objectbased classification that integrates recognition patterns and object-based Image Analysis, called GeoPatterns. To do so, an aerial orthoimage was obtaining by an Unmanned Aerial Vehicle (UAV) Echar 20B. This orthoimage was segmenting by using region growing method. Both proposed tools and methods were developing by using Python programming language and Scikit-Learn (data mining), Numpy (scientific computing) and PyQGIS (which integrate Python and QGIS) libraries. The IKNN method allowed the selection of the most relevant characteristics and the treatment of the overlapping of its values. To the 60% confidence threshold, IKNN method resulted in a Correct Proportion equal to 90.0%, which was superior to the Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) methods. The VNO object-based post-classification method increased the accuracy of the classification from 92.5% to 95.7% in the evaluation of 7,000 pixels or higher segmentation objects. The QOF post-classification method obtained higher results, reaching up to 97.0% for 9,400 pixels or higher objects classification. The GeoPatterns program enabled the integration of both nonparametric recognition patt

关键词： Sensoriamento remoto Data mining Veículo aéreo não tripulado object-based classification Uso da terra Machine learning Tese

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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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