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Evaluating resampled and fused Sentinel-2 data and machine-learning algorithms for mangrove mapping in the northern coast of Qeshm island, Iran

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NATURE CONSERVATION-BULGARIA 2023年第52期52卷 1-22页

作者： Soffianian, Ali Reza Toosi, Neda Bihamta Asgarian, Ali Regnauld, Herve Fakheran, Sima Waser, Lars T. Isfahan Univ Technol Dept Nat Resources Esfahan *** Iran LETG Littoral Environm Teledetect Geomat UMR 6554 Rennes France Swiss Fed Inst Forest Snow & Landscape Res WSL CH-8903 Birmensdorf Switzerland

Mangrove forests, as an essential component of the coastal zones in tropical and subtropical areas, provide a wide range of goods and ecosystem services that play a vital role in ecology. Mangroves are globally threatened, disappearing, and degraded. Consequently, knowledge on mangroves distribution and change is important for effective conservation and making protection policies. Developing remote sensing data and classification methods have proven to be suitable tools for mapping mangrove forests over a regional scale. Here, we scrutinized and compared the performance of pixel-based and object-based methods under Support Vector Machine (SVM) and Random Forest (RF) algorithms in mapping a mangrove ecosystem into four main classes (Mangrove tree, mudflat, water, and sand spit) using resampled and fused Senti-nel-2 images. Additionally, landscape metrics were used to identify the differences between spatial patterns obtained from different classification methods. Results showed that pixel-based classifications were influ-enced heavily by the effect of salt and pepper noise, whereas in object-based classifications, boundaries of land use land cover (LULC) polygons were smoother and visually more appealing. object-based clas-sifications, with an excellent level of kappa, distinguished mudflat and sand spit from each other and from mangrove better than the pixel-based classifications which obtained a fair-to-good level of kappa. RF and SVM performed differently under comparable circumstances. The results of landscape metrics comparison presented that the classification methods can be affected on quantifying area and size metrics. Although the results supported the idea that fused Sentinel images may provide better results in mangrove LULC classification, further research needs to develop and evaluate various image fusion approaches to make use of all Sentinel's fine resolution images. Our results on the mapping of mangrove ecosystems can contribute to the improvement of

关键词： image fusion landscape metric mangroves object-based classification Sentinel-2

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Satellite remote sensing and GIS-based multi-criteria analysis for flood hazard mapping

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NATURAL HAZARDS 2016年第1-Sup期83卷 S31-S51页

作者： Franci, Francesca Bitelli, Gabriele Mandanici, Emanuele Hadjimitsis, Diofantos Agapiou, Athos Univ Bologna Dept Civil Chem Environm & Mat Engn DICAM Vle Risorgimento 2 I-40136 Bologna Italy Cyprus Univ Technol Dept Civil Engn & Geomat Lab Remote Sensing & Geoinformat Saripolou 2-8 CY-3603 Limassol Cyprus

This work focuses on the exploitation of very high-resolution (VHR) satellite imagery coupled with multi-criteria analysis (MCA) to produce flood hazard maps. The methodology was tested over a portion of the Yialias river watershed basin (Nicosia, Cyprus). The MCA methodology was performed selecting five flood-conditioning factors: slope, distance to channels, drainage texture, geology and land cover. Among MCA methods, the analytic hierarchy process technique was chosen to derive the weight of each criterion in the computation of the flood hazard index (FHI). The required information layers were obtained by processing a VHR GeoEye-1 image and a digital elevation model. The satellite image was classified using an object-based technique to extract land use/cover data, while GIS geoprocessing of the DEM provided slope, stream network and drainage texture data. Using the FHI, the study area was finally classified into seven hazard categories ranging from very low to very high in order to generate an easily readable map. The hazard seems to be severe, in particular, in some urban areas, where extensive anthropogenic interventions can be observed. This work confirms the benefits of using remote sensing data coupled with MCA approach to provide fast and cost-effective information concerning the hazard assessment, especially when reliable data are not available.

关键词： Flood hazard GeoEye-1 object-based classification Analytic hierarchy process Yialias river

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Assessing land use-land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm

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GEOCARTO INTERNATIONAL 2021年第4期36卷 361-375页

作者： Paul, Siddhartho Shekhar Li, Jianbing Li, Yubao Shen, Lei Univ British Columbia Fac Land & Food Syst Vancouver BC Canada UNBC Environm Engn Program Prince George BC Canada Wenzhou Univ WZU WZU UNBC Joint Res Inst Ecol & Environm Wenzhou Zhejiang Peoples R China

Land use-land cover (LULC) change and the associated risk of soil erosion have become a global environmental concern. We herein presented a geospatial analysis to detect LULC changes (1984-2010) in a Canadian watershed by using object-based classification of Landsat satellite images. We found that the watershed experienced a substantial increase in forest clear cutting and built-up areas. The detected LULC changes were implemented into the Revised Universal Soil Loss Equation (RUSLE) to examine the soil erosion potential. We divided the soil erosion risk into five classes ranging from very low (<6 ton ha(-1) year(-1)) to severe (33 ton ha(-1) year(-1)) levels. The random forest algorithm was then implemented and detected that the topography and LULC conditions of 1999 and 2010 had the most influence on the erosion in 2010. The findings of this study will support efficient LULC management to reduce soil erosion and the consequent degradation of water quality.

关键词： Industrial expansion Landsat object-based classification RUSLEFAC watershed

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Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2013年第1期20卷 60-66页

作者： Mitri, George H. Gitas, Ioannis Z. Univ Balamand Inst Environm Biodivers Program Tripoli North Lebanon Lebanon Univ Balamand Fac Sci Tripoli North Lebanon Libya Aristotle Univ Thessaloniki Sch Forestry & Nat Environm Lab Forest Management & Remote Sensing GR-54006 Thessaloniki Greece

Careful evaluation of forest regeneration and vegetation recovery after a fire event provides vital information useful in land management. The use of remotely sensed data is considered to be especially suitable for monitoring ecosystem dynamics after fire. The aim of this work was to map post-fire forest regeneration and vegetation recovery on the Mediterranean island of Thasos by using a combination of very high spatial (VHS) resolution (QuickBird) and hyperspectral (EO-1 Hyperion) imagery and by employing object-based image analysis. More specifically, the work focused on (1) the separation and mapping of three major post-fire classes (forest regeneration, other vegetation recovery, unburned vegetation) existing within the fire perimeter, and (2) the differentiation and mapping of the two main forest regeneration classes, namely, Pinus brutia regeneration, and Pin us nigra regeneration. The data used in this study consisted of satellite images and field observations of homogeneous regenerated and revegetated areas. The methodology followed two main steps: a three-level image segmentation, and, a classification of the segmented images. The process resulted in the separation of classes related to the aforementioned objectives. The overall accuracy assessment revealed very promising results (approximately 83.7% overall accuracy, with a Kappa Index of Agreement of 0.79). The achieved accuracy was 8% higher when compared to the results reported in a previous work in which only the EO-1 Hyperion image was employed in order to map the same classes. Some classification confusions involving the classes of P. brutia regeneration and P. nigra regeneration were observed. This could be attributed to the absence of large and dense homogeneous areas of regenerated pine trees in the study area. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Forest regeneration Vegetation recovery Very high spatial resolution imagery Hyperspectral imagery object-based classification

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Extraction and geospatial analysis of the Hersek Lagoon shoreline with Sentinel-2 satellite data

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SURVEY REVIEW 2024年第397期56卷 367-382页

作者： Senol, Halil Ibrahim Kaya, Yunus Yigit, Abdurahman Yasin Yakar, Murat Harran Univ Geomat Engn Dept Sanliurfa Turkiye Mersin Univ Geomat Engn Dept Mersin Turkiye

In this study, the coastal changes in Hersek Lagoon, have occurred in the 5 years (2015 to 2020) were examined. With the Modified Normalised Difference Water Index (MNDWI), the water areas in the lagoon were determined by object-based classification. When the changes are examined on a regional basis, it is seen that there are erosion and accretion areas were observed. Pearson's r value was examined between End Point Rate (EPR) and Linear Regression Rate (LRR) and a high correlation such as r = 0.921 was obtained. The maximum coastal change was observed as 18.74 m/year with EPR and 18.62 m/year with LRR.

关键词： Shoreline change Digital shoreline analysis system (DSAS) object-based classification Extraction Hersek lagoon Geospatial analysis

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Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery

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GEOCARTO INTERNATIONAL 2021年第13期36卷 1429-1442页

作者： Vigneshwaran, S. Kumar, S. Vasantha VIT Sch Civil Engn Vellore Tamil Nadu India

Urban green space (UGS) plays a vital role in maintaining the ecological balance of a city and in ensuring healthy living of the city inhabitants. It is generally suggested that one-third of the city should be covered by green and to ensure this, the city administrators must have an accurate map of the existing UGS. Such a map would be useful to visualize the distribution of the existing green cover and also to find out the areas that can possibly be converted to UGS. Reported studies on UGS mapping have mostly used medium and high resolution images such as Landsat-TM, ETM+, Sentinel-2A, IKONOS, etc. However, studies on the use of very high resolution images for UGS extraction are very limited. The present study is a first attempt in utilizing the very high resolution Worldview-3 image for UGS extraction. Performance of different classification methods such as unsupervised, supervised, object based and normalized difference vegetation index (NDVI) were compared using the pan sharpened Worldview-3 image covering part of New Delhi in India. It was found that the unsupervised classification followed by manual recoding method showed superior performance with overall accuracy (OA) of 99% and kappa coefficient of 0.98. Also, the OA achieved in the present study is the highest when compared to other reported studies on UGS extraction. The map of UGS revealed that almost 40% of the study area is covered by green which is more than the recommended value of 33% (one-third). In order to check the universality of the unsupervised classification approach in extracting UGS, Worldview-3 image covering Rio in Brazil was tested. It was found that an OA of 98% and kappa coefficient of 0.95 were obtained which clearly indicate that the proposed approach would work very well in extracting UGS from any Worldview-3 imagery.

关键词： Urban green space mapping unsupervised classification supervised classification NDVI object-based classification

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Synergistic use of RADARSAT-2 Ultra Fine and Fine Quad-Pol data to map oilsands infrastructure land: object-based approach

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2015年 38卷 193-203页

作者： Jiao, Xianfeng Zhang, Ying Guindon, Bert Nat Resources Canada Canada Ctr Mapping & Earth Observat Ottawa ON K1A 0E4 Canada

The landscape of Alberta's oilsands regions is undergoing extensive change due to the creation of infrastructure associated with the exploration for and extraction of this resource. Since most oil sands mining activities take place in remote forests or wetlands, one of the challenges is to collect up-to date and reliable information about the current state of land. Compared to optical sensors, SAR sensors have the advantage of being able to routinely collect imagery for timely monitoring by regulatory agencies. This paper explores the capability of high resolution RADARSAT-2 Ultra Fine and Fine Quad-Pol imagery for mapping oilsands infrastructure land using an object-based classification approach. Texture measurements extracted from Ultra Fine data are used to support an Ultra Fine based classification. Moreover, a radar vegetation index (RVI) calculated from PolSAR data is introduced for improved classification performance. The RVI is helpful in reducing confusion between infrastructure land and low vegetation covered surfaces. When Ultra Fine and PoISAR data are used in combination, the kappa value of well pads and processing facilities detection reached 0.87. In this study, we also found that core hole sites can be identified from early spring Ultra Fine data. With single-date image, kappa value of core hole sites ranged from 0.61 to 0.69. Crown Copyright (C) 2015 Published by Elsevier B.V. All rights reserved.

关键词： Radarsat-2 PolSAR object-based classification Mapping Oilsands infrastructure land

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Regional hydromorphological characterization with continuous and automated remote sensing analysis based on VHR imagery and low-resolution LiDAR data

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EARTH SURFACE PROCESSES AND LANDFORMS 2017年第3期42卷 531-551页

作者： Demarchi, Luca Bizzi, Simone Piegay, Herve European Commiss Joint Res Ctr Inst Environm & Sustainabil Water Resources Unit Via E Fermi 2749 I-21027 Ispra Italy Warsaw Univ Life Sci Fac Civil & Environm Engn Dept Hydraul Engn Nowoursynowska 166 Warsaw Poland Politecn Milan Dept Elect Informat & Bioengn Piazza Leonardo da Vinci Milan Italy Univ Lyon UMR CNRS EVS 5600 ISIG Platform Site ENS Lyon15 Parvis Rene Descartes F-69362 Lyon France

Recent developments in remote sensing (RS) technologies lead the way in characterizing river morphology at regional scales and inferring potential channel responses to human pressures. In this paper, a unique regional database of continuous hydromorphological variables (HyMo DB) based on areal and topographic data has been generated from RS analysis. Key riverscape units with specific geomorphic meaning have been automatically mapped for 1700 km(2) of river floodplains from simultaneous very-high-resolution (VHR) near-infrared aerial imagery and low-resolution LiDAR-derived products. A multi-level, geographical object-based architecture (GEOBIA) was employed to integrate both spectral and topographic information and generate a regional classifier able to automatically map heterogeneous fluvial patterns in different geographical and topographical contexts of the Piedmont Region (Italy). This HyMo-generated DB offers a unique set of tools for hydromorphologists and can be exploited for different purposes. For the first time, topographic information can be exploited regionally per riverscape unit class, allowing for quantitative analysis of their regional spatial and statistical variability. In this manner, river types can be automatically characterized and classified using objective and repeatable hydromorphological variables. We discuss the potential of quantifying functional links between riverscape units and their driving processes, a valuable source of information to start assessing and highlighting the entity of potential channel adjustments at the regional scale to human pressures. The HyMo DB can also be integrated with historical, field-based information to better comprehend current fluvial changes at a local scale. In view of future RS acquisitions, the present approach will result in a suitable procedure for quantitative, objective and continuous monitoring of river evolutions over large scales. This type of hydromorphological characterization will allow reg

关键词： VHR and LiDAR data object-based classification regional scale hydromorphological characterization

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Environmental Approach in Modelling of Urban Growth: Tehran City, Iran

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ASIAN JOURNAL OF WATER ENVIRONMENT AND POLLUTION 2018年第2期15卷 47-56页

作者： Aqbelaghi, Aref Shahi Ghorbani, Mehdi Farhadi, Ebrahim Shafiee, Hadi Univ Tehran Dept Geog Tehran Iran

The rapid growth of urbanization has put heavy pressure on the land and its surrounding resources, reduction of vegetation cover, open spaces and serious social and environmental problems. Therefore, a basic step for managing and planning urban growth, as well as evaluating its cumulative effects, is to study and simulate the physical growth of the city. The purpose of this study is to understand the factors that influence the physical growth in Tehran on the basis of sustainable urban development in terms of environmental dimension and the preservation of environmental conditions in the next two decades. For this, using Landsat multi-temporal satellite imagery and object-based classification, land-use was assessed during the period from 1990 to 2015. In the next step, using the multi-criteria analysis model and the environment-based growth model, the impact of independent variables on urban growth, including 18 variables, was calculated from 1990 to 2015 and the map of urbanization potential was produced. Then the area of change for the expected year was predicted quantitatively using the Markov chain analysis. Finally, using Cellular Automata model, urban growth simulation for 2015 was performed with relative accuracy of 0.91 and Kappa coefficient of 0.87, and this model was used to estimate urban growth in 2025. The results show that urban growth will accelerate in 2025, as in the period 2003-2015, and often in the western and northeastern parts of the city. if the nature and extent of the impact of factors affecting urban growth will remain constant.

关键词： Urban growth modelling cellular automata object-based classification Tehran

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MAPPING LAND COVER IN THE TAITA HILLS, SE KENYA, USING AIRBORNE LASER SCANNING AND IMAGING SPECTROSCOPY DATA FUSION 36

MAPPING LAND COVER IN THE TAITA HILLS, SE KENYA, USING AIRBO...

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36th International Symposium on Remote Sensing of Environment

作者： Piiroinen, R. Heiskanen, J. Maeda, E. Hurskainen, P. Hietanen, J. Pellikka, P. Univ Helsinki Dept Geosci & Geog POB 64 FI-00014 Helsinki Finland

ISBN: (纸本)9781629935201

The Taita Hills, located in south-eastern Kenya, is one of the world's biodiversity hotspots. Despite the recognized ecological importance of this region, the landscape has been heavily fragmented due to hundreds of years of human activity. Most of the natural vegetation has been converted for agroforestry, croplands and exotic forest plantations, resulting in a very heterogeneous landscape. Given this complex agro-ecological context, characterizing land cover using traditional remote sensing methods is extremely challenging. The objective of this study was to map land cover in a selected area of the Taita Hills using data fusion of airborne laser scanning (ALS) and imaging spectroscopy (IS) data. Land Cover classification System (LCCS) was used to derive land cover nomenclature, while the height and percentage cover classifiers were used to create objective definitions for the classes. Simultaneous ALS and IS data were acquired over a 10 km x 10 km area in February 2013 of which 1 km x 8 km test site was selected. The ALS data had mean pulse density of 9.6 pulses/m(2), while the IS data had spatial resolution of 1 m and spectral resolution of 4.5-5 nm in the 400-1000 nm spectral range. Both IS and ALS data were geometrically co-registered and IS data processed to at-surface reflectance. While IS data is suitable for determining land cover types based on their spectral properties, the advantage of ALS data is the derivation of vegetation structural parameters, such as tree height and crown cover, which are crucial in the LCCS nomenclature. Geographic object-based image analysis (GEOBIA) was used for segmentation and classification at two scales. The benefits of GEOBIA and ALS/IS data fusion for characterizing heterogeneous landscape were assessed, and ALS and IS data were considered complementary. GEOBIA was found useful in implementing the LCCS based classification, which would be difficult to map using pixel-based methods.

关键词： GEOBIA LCCS Hyperspectral data LiDAR Data fusion object-based classification

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