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Monitoring and Projecting Land Use/Land Cover Changes of Eleven Large Deltaic Areas in Greece from 1945 Onwards

REMOTE SENSING 2020年第8期12卷 1241-1241页

作者： Krina, Anastasia Xystrakis, Fotios Karantininis, Kostas Koutsias, Nikos Univ Patras Dept Environm Engn G Seferi 2 GR-30100 Agrinion Greece Swedish Univ Agr Sci SLU Dept Work Sci Business Econ & Environm Psychol AE POB 88 SE-23053 Alnarp Sweden

Wetlands are areas of high biodiversity and provide many ecosystem services of high value. However, they are under constant threat from intense anthropogenic pressures, mainly agriculture intensification, urbanization, pollution, and climate change. The temporal and spatial patterns of land use/land cover (LULC) changes within eleven large wetlands in Greece were analyzed based on thematic maps generated from aerial orthophotos taken in 1945, 1975, and 2007. Socio-economic developments and the consequent need for more arable land and utilization of water resources are among the factors that mainly determine their evolution. In 2007, LULC classes related to wetland vegetation were reduced to one third as compared to 1945 and they were mainly replaced with croplands and urban infrastructures. Each of the different sub-periods that was considered (1945-1975 and 1975-2007) was distinguished by characteristic patterns of change. Agricultural land increased up to 42% from 1945 to 1975 and became the dominant LULC class in all deltaic areas but Evros. A considerable stability was observed for the period 1975-2007 for all LULC classed but it is remarkable the extent of urban areas that doubled. There is a tendency of landscape simplification and homogenization among the deltaic areas and the output of Markov chain analysis indicates that future composition of deltaic landscapes will be similar to the current one if the main driving forces remain constant. Changes in LULC composition and structure are also combined with coastal erosion in all deltaic areas. This is attributed to the modification of sedimentary deposits due to dam construction. The results summarize the change trajectories of the major deltaic areas in Greece from 1945 to 2007 thus offering a great outlook of changes that allows managers to understand how policies and socio-economic requirements affect the deltaic ecosystems and what decisions should be made to protect and enhance them.

关键词： wetlands landscape metrics object-based classification aerial photos interpretation Mediterranean Markov chain Monte Carlo randomization

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Multi-Scale Mapping and Analysis of Broadleaf Species Distribution Using Remotely Piloted Aircraft and Satellite Imagery

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REMOTE SENSING 2024年第24期16卷 4809-4809页

作者： Chandrasekaran, Aishwarya Hupy, Joseph P. Shao, Guofan Utah State Univ Dept Environm & Soc Logan UT 84322 USA Purdue Univ Sch Aviat Technol & Transportat W Lafayette IN 47907 USA Purdue Univ Dept Forestry & Nat Resources W Lafayette IN 47907 USA

Tree species mapping from the individual crown to landscape scales provides crucial information on the diversity and richness of forest ecosystems, supporting major conservation decisions under ongoing climate change. With the emergence of Remote Piloted Aircraft (RPA), high spatial resolution datasets can be obtained and analyzed to inherently improve the current understanding of broadleaf tree species distribution. The utility of RPA for mapping broadleaf species at broader scales using satellite data needs to be explored. This study investigates the use of RPA RGB imagery captured during peak fall foliage to leverage coloration commonly exhibited by different broadleaf tree species during phenology transition to delineate individual tree crowns and map species distribution. Initially, a two-step hybrid segmentation procedure was designed to delineate tree crowns for two broadleaf forests using RPA imagery collected during the fall season. With the tree crowns, a subsequent object-based Random Forest (ORF) model was tested for classifying common and economically important broadleaf tree species groups. The classified map was further utilized to improve ground reference data for mapping species distribution at the stand and landscape scales using multispectral satellite imagery (1.4 m to 10 m). The results indicated an improvement in the overall accuracy of 0.13 (from 0.68 to 0.81) and a MICE metric of 0.14 (from 0.61 to 0.75) using reference samples derived from RPA data. The results of this preliminary study are promising in utilizing RPA for multi-scale mapping of broadleaf tree species effectively.

关键词： multi-scale analysis remotely piloted aircraft broadleaf hybrid segmentation object-based classification stand and landscape scales

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Mapping standing dead trees in temperate montane forests using a pixel-and object-based image fusion method and stereo WorldView-3 imagery

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ECOLOGICAL INDICATORS 2021年 133卷 108438-108438页

作者： Liu, Xiang Frey, Julian Denter, Martin Zielewska-Buettner, Katarzyna Still, Nicole Koch, Barbara Univ Freiburg Chair Remote Sensing & Landscape Informat Syst D-79106 Freiburg Germany Univ Freiburg Chair Forest Growth & Dendroecol D-79106 Freiburg Germany Forest Res Inst Baden Wurttemberg FVA Dept Forest Nat Conservat Wonnhaldestr 4 D-79100 Freiburg Germany Univ Freiburg Chair Forestry Econ & Forest Planning D-79106 Freiburg Germany

Information about the distribution of standing dead trees (SDT) is essential for forest biodiversity estimation, forest disturbances monitoring, and forest management strategy planning. Although remote sensing techniques offer unique capabilities to map SDT over large areas, three major hurdles exist: (1) the sporadic distribution of SDT in the study area;(2) often poor spectral separability between SDT and bare ground in forests;(3) the prominent spectral variability within SDT due to variations in background effect and canopy illumination. To address these problems, we proposed a pixel- and object-based image fusion (POBIF) approach using very high-resolution (VHR) stereo WorldView-3 (WV-3) data. The stereo WV-3 derived spectral bands, canopy height model (CHM), vegetation indices (VIs), and texture features were used as inputs in six classification scenarios with different variable combinations. A deep learning algorithm, deep neural network (DNN), and two machine learning algorithms, support vector machine (SVM) and random forest (RF), were utilized to process the pixel-based (PB) and object-based (OB) information. All PB and OB classifiers were then combined using a stacked generalization strategy to develop the POBIF model. Comparing the six scenarios we assessed the importance of the CHM, VIs, and textures for accurate SDT mapping. As a result, we found (1) the POBIF outperformed both PB and OB methods for SDT mapping, generating notably higher F1-score (p < 0.05);(2) Adding the CHM reduced the commission and omission errors caused by bare ground and artificial surfaces, and the highest classification accuracy was achieved when combing all the WV-3 derived variables;(3) SDT covered 0.89% of the forest areas in the study area and was particularly distributed on higher and steeper north-facing (northeast and northwest) slopes. Large tracts of SDT were found in the strictly protected forests. The study highlights the potential of VHR stereo WV-3 imagery and the

关键词： Standing dead trees Stereo WorldView-3 object-based classification Machine learning Fusion method

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Accurate classification of land use and land cover using a boundary-specific two-level learning approach augmented with auxiliary features in Google Earth Engine

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2023年第11期195卷 1280-1280页

作者： Selvaraj, Rohini Amali, D. Geraldine Bessie Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 India

Land use land cover (LULC) classification using remote sensing images is a valuable resource in various fields such as climate change, urban development, and land degradation monitoring. The city of Madurai in India is known for its diverse geographical elements and rich heritage, which includes the cultural sport of "Jallikattu": whose main competitor, the zebusare deeply affected by the conversion of their waterbodies and pastures into concrete jungles. Hence, monitoring land degradation is vital in preserving the geography and cultural heritage of the study area, Madurai. The "Landsat 8 Operational Land Imager tier_2 collection_2 Level_2 Surface Reflectance" image was taken for this study. The LULC classification is performed based on the following classes: forest, agriculture, urban, water bodies, uncultivated land, and bare land. The objective of the study is to incorporate auxiliary features to spectral and textural features along with a simple non-iterative clustering (SNIC) segmentation algorithm and implement a boundary-specific two-level learning approach based on support vector machines (SVM) and k nearest neighbors (kNN) classification algorithms. The overall accuracy (OA) of 95.78% and 0 .94 Kappa score (K) were obtained using a boundary-specific two-level model augmented with auxiliary feature and SNIC algorithm in comparison to PB, OB, and OBS, which achieve OA (K) of 81% (0.76), 91% (0.89), and 94.42% (0.92), respectively. The results demonstrate a notable enhancement in overall classification accuracy when augmenting the features and refining classification decisions using a boundary-specific two-level learning approach.

关键词： Land use and land cover Google Earth Engine Boundary-specific two-level classification Auxiliary features object-based classification Pixel-based classification

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Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

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REMOTE SENSING 2020年第6期12卷 909-909页

作者： Pereira-Pires, Joao E. Aubard, Valentine Ribeiro, Rita A. Fonseca, Jose M. Silva, Joao M. N. Mora, Andre NOVA Univ Lisbon Ctr Technol & Syst UNINOVA Sch Sci & Technol P-2829516 Caparica Portugal Univ Lisbon Forest Res Ctr Sch Agr P-1349017 Lisbon Portugal

The difficult job of fighting fires and the nearly impossible task to stop a wildfire without great casualties requires an imperative implementation of proactive strategies. These strategies must decrease the number of fires, the burnt area and create better conditions for the firefighting. In this line of action, the Portuguese Institute of Nature and Forest Conservation defined a fire break network (FBN), which helps controlling wildfires. However, these fire breaks are efficient only if they are correctly maintained, which should be ensured by the local authorities and requires verification from the national authorities. This is a fastidious task since they have a large network of thousands of hectares to monitor over a full year. With the increasing quality and frequency of the Earth Observation Satellite imagery with Sentinel-2 and the definition of the FBN, a semi-automatic remote sensing methodology is proposed in this article for the detection of maintenance operations in a fire break. The proposed methodology is based on a time-series analysis, an object-based classification and a change detection process. The change detection is ensured by an artificial neural network, with reflectance bands and spectral indices as features. Additionally, an analysis of several bands and spectral indices is presented to show the behaviour of the data during a full year and in the presence of a maintenance operation. The proposed methodology achieved a relative error lower than 4% and a recall higher than 75% on the detection of maintenance operations.

关键词： remote sensing fire break object-based classification change detection wildfires artificial neural networks sentinel-2

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In-Season Crop Mapping with GF-1/WFV Data by Combining object-based Image Analysis and Random Forest

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REMOTE SENSING 2017年第11期9卷 1184-1184页

作者： Song, Qian Hu, Qiong Zhou, Qingbo Hovis, Ciara Xiang, Mingtao Tang, Huajun Wu, Wenbin Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Minist Agr Key Lab Agr Remote Sensing AGRIRS Beijing 100081 Peoples R China Michigan State Univ Ctr Syst Integrat & Sustainabil E Lansing MI 48824 USA

Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

关键词： crop mapping in-season object-based classification Random Forest

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classification of Tropical Forest Tree Species Using Meter-Scale Image Data

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REMOTE SENSING 2019年第12期11卷 1411-1411页

作者： Cross, Matthew Scambos, Ted Pacifici, Fabio Vargas-Ramirez, Orlando Moreno-Sanchez, Rafael Marshall, Wesley Univ Colorado GES Denver CO 80204 USA Univ Colorado CIRES Boulder CO 80303 USA DigitalGlobe Denver CO 80234 USA La Selva Biol Stn Puerto Viejo Sarapiqui 41001 Croatia Univ Colorado Dept Civil Engn Denver CO 80217 USA

Accurate classification of tropical tree species is critical for understanding forest habitat, biodiversity, forest composition, biomass, and the role of trees in climate variability through carbon uptake. The aim of this study is to establish an accurate classification procedure for tropical tree species, specifically testing the feasibility of WorldView-3 (WV-3) multispectral imagery for this task. The specific study site is a defined arboretum within a well-known tropical forest research location in Costa Rica (La Selva Biological Station). An object-based classification is the basis for the analysis to classify six selected tree species. A combination of pre-processed WV-3 bands were inputs to the classification, and an edge segmentation process defined multi-pixel-scale tree canopies. WorldView-3 bands in the Green, Red, Red Edge, and Near-Infrared 2, particularly when incorporated in two specialized vegetation indices, provide high discrimination among the selected species. classification results yield an accuracy of 85.37%, with minimal errors of commission (7.89%) and omission (14.63%). Shadowing in the satellite imagery had a significant effect on segmentation accuracy (identifying single-species canopy tops) and on classification. The methodology presented provides a path to better characterization of tropical forest species distribution and overall composition for improving biomass studies in a tropical environment.

关键词： tropical forest tree species identification object-based classification image segmentation WorldView-3

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WATER 2018年第5期10卷 555页

作者： Wang, Xianwei Chen, Huijiao Chen, Yaning Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Guangdong Key Lab Urbanizat & Geosimulat Guangzhou 510275 Guangdong Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol Urumqi 830011 Peoples R China

Studies have investigated the glacier projected area (2D Area) on a horizontal plane, which is much smaller than the glacier topographic surface extent (3D Area) in steep terrains. This study maps the glacier outline in Central Tianshan using Landsat images from four periods, i.e., 1989, 2002, 2007 and 2015, by an object-based classification approach, and analyzes the glacier 2D and 3D area changes related to topographic factors based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM data. This approach shows an accuracy of 90.8% for clean ice mapping. The derived clean ice outlines are in good agreement with the 2nd Chinese Glacier Inventory (CGI2) and the Global Land Ice Measurements from Space (GLIMS). The fields with a northern aspect receive the least surface solar radiation, leading to dominant existing glaciers. Glaciers are near evenly distributed in slope zones of 0 degrees to 50 degrees and have a mean slope angle of 28.8 degrees, resulting in a 30.3% larger 3D area than the 2D area in 2015 in Central Tianshan. The glacier 2D area decreased by 404 km(2) (-8.1%) between 1989 and 2015, while the 3D area declined by 516 km(2) (-7.9%). The glacier 2D area showed a reduction of -1.8% between 1989 and 2002, -3.8% between 2002 and 2007, and -2.7% between 2007 and 2015, and these retreating rates closely responded to the variations of regional mean air temperature and precipitation. Topographically, most reductions occurred in elevation bands of 3000-4000 m and in slope zones of 10-20 degrees and 40-50 degrees, and in the eastern aspect fields. The northern Tekes River catchment had the largest shrinking rate of -17.0% (2D area), followed by the southern Karasu River (-14.2%) and Muzart River (-7.7%) catchments. In contrast, glaciers in the Kumerik/Aksu and Tailan River catchments in the Tuomuer region showed little change (-2%).

关键词： glacier area changes clean ice landsat images object-based classification Central Tianshan

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Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)

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REMOTE SENSING 2018年第5期10卷 778-778页

作者： Wang, Minye Fei, Xianyun Zhang, Yuanzhi Chen, Zhou Wang, Xiaoxue Tsou, Jin Yeu Liu, Dawei Lu, Xia HuaiHai Inst Technol Sch Geomat & Marine Informat Lianyungang 222002 Peoples R China Chinese Acad Sci Key Lab Lunar Sci & Deep Space Explorat Natl Astron Observ Beijing 100101 Peoples R China Chinese Univ Hong Kong Ctr Housing Innovat Shatin Hong Kong Peoples R China

Coastal wetland vegetation is a vital component that plays an important role in environmental protection and the maintenance of the ecological balance. As such, the efficient classification of coastal wetland vegetation types is key to the preservation of wetlands. based on its detailed spatial information, high spatial resolution imagery constitutes an important tool for extracting suitable texture features for improving the accuracy of classification. In this paper, a texture feature, Completed Local Binary Patterns (CLBP), which is highly suitable for face recognition, is presented and applied to vegetation classification using high spatial resolution Pleiades satellite imagery in the central zone of Yancheng National Natural Reservation (YNNR) in Jiangsu, China. To demonstrate the potential of CLBP texture features, Grey Level Co-occurrence Matrix (GLCM) texture features were used to compare the classification. Using spectral data alone and spectral data combined with texture features, the image was classified using a Support Vector Machine (SVM) based on vegetation types. The results show that CLBP and GLCM texture features yielded an accuracy 6.50% higher than that gained when using only spectral information for vegetation classification. However, CLBP showed greater improvement in terms of classification accuracy than GLCM for Spartina alterniflora. Furthermore, for the CLBP features, CLBP_magnitude (CLBP_m) was more effective than CLBP_sign (CLBP_s), CLBP_center (CLBP_c), and CLBP_s/m or CLBP_s/m/c. These findings suggest that the CLBP approach offers potential for vegetation classification in high spatial resolution images.

关键词： coastal wetland vegetation feature extraction completed local binary patterns (CLBP) object-based classification

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Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-2

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REMOTE SENSING 2022年第13期14卷 3227页

作者： Aliabad, Fahime Arabi Malamiri, Hamid Reza Ghafarian Shojaei, Saeed Sarsangi, Alireza Ferreira, Carla Sofia Santos Kalantari, Zahra Yazd Univ Fac Nat Resources & Desert Studies Dept Arid Land Management Yazd *** Iran Yazd Univ Dept Geog Yazd *** Iran Delft Univ Technol Dept Geosci & Engn NL-2628 CD Delft Netherlands Univ Tehran Fac Nat Resources Dept Arid & Mt Reg Reclamat Tehran *** Iran Univ Tehran Fac Geog Dept Remote Sensing & GIS Tehran *** Iran Stockholm Univ Bolin Ctr Climate Res Dept Phys Geog S-10691 Stockholm Sweden Polytech Inst Coimbra Agr Sch Coimbra Res Ctr Nat Resources Environm & Soc CERNAS P-3045601 Coimbra Portugal KTH Royal Inst Technol Dept Sustainable Dev Environm Sci & Engn SEED S-11428 Stockholm Sweden

One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method.

关键词： remote sensing satellite images UAV land cover change object-based classification

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