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The emission of dust particles, mainly from arid and semi-arid lands, as a result of climate change and human activities, is known to be a global issue. Identifying dust emission sources is the first key step in dealing with the hazardous consequences of this rising phenomenon. This study is an attempt to address one of the major challenges in mapping dust emission sources. Accordingly, an innovative approach based on visual interpretation of multi-temporal MODIS-Terra/Aqua imagery and object-oriented image segmentation techniques has been developed and implemented in the study areas of the Tigris and Euphrates basin and eastern Iran. This approach takes advantage of land surface characteristics (i.e., dust drivers), including geomorphology, soil, land use/cover, and land surface radiation, to attribute dust emission hotspots to their corresponding areas using multi-source remote sensing data. The results show that the multi-resolution segmentation algorithm with optimized parameters can identify homogeneous segments corresponding to dust emission sources in the study areas with an average spatial agreement of ∼92% compared to the reference areas. Our findings emphasize the robustness and generalizability of the proposed approach, and its capabilities can be used in a complementary way with visual interpretation of satellite images to map dust sources with high spatial accuracy.

The Great Wall, as a globally important large-scale linear cultural heritage asset, is an example of the integration of architecture and landscape, demonstrating the interaction and feedback between heritage and the environment. In the context of advocating the holistic protection of cultural heritage and surroundings, this study utilizes landscape character assessment (LCA) to identify the landscape character of the Great Wall heritage area. Taking the heritage area of the Great Wall in Beijing, China, as an example, principal component analysis (PCA), two-step clustering, and the eCognition software were used to identify and describe the landscape character types, and the interaction mechanism between heritage and the environment was further explored through the reclassification process. A total of 20 landscape character types and 201 landscape character areas were identified in the study area, and a deep coupling relationship between heritage and the environment and cultural landscape spatial patterns were found in the core heritage area. The heritage and environmental character of linear heritage areas should be integrated so as to protect, manage, and plan cultural heritage areas at the landscape level. This study identifies and describes the character of the coupling of heritage and the environment in the Great Wall area for the first time, expands the types and methods of landscape character assessment, and carries out the exploration to combine natural and cultural elements of large-scale linear cultural heritage areas. [ABSTRACT FROM AUTHOR]

Rice is one of the most important food staples in many countries, particularly Iran. Because irrigated rice production differs from other agricultural fields, this study developed a paddy field mapping model based on phenological aspects, various satellite sensor data, and an object-based approach. This study uses the phonological features of rice plants as well as annual data on land surface temperature (LST) to produce the paddy map. The core remote sensing data consists of the yearly LST from MODIS and multi-temporal Landsat-8 satellite imagery. The detection of phenological characteristics and the selection of relevant Landsat images were made possible by analyzing the LST time series with Google Earth Engine. After that, object-based image classification and fuzzy functions are used to create flexible and comprehensible rules for discovering paddy fields in Rasht, Iran. Data such as the digital elevation model (DEM) and spectral indices including NDVI, EVI, and LSWI were employed to improve the object-based classification. Due to the unique properties of paddy lands, a DEM of 12.5 m obtained from the ALOS PALSAR sensor could help distinguish paddy lands from other vegetation. A comparison finally made between the object-based and pixel-based classification methods showed that the former is more accurate. Overall accuracy and kappa coefficient for the pixel-based classification approach were 92% and 0.89, respectively, whereas overall accuracy and kappa coefficient for the object-based classification method were 94% and 0.92, respectively. Eventually, the producer's accuracy of the paddy class has increased from 88% to 94%.

Remote sensing has long been used to study wildlife; however, manual methods of detecting wildlife in aerial imagery are often time-consuming and prone to human error, and newer computer vision techniques have not yet been extensively applied to wildlife surveys. We used the object-based image analysis (OBIA) software eCognition to detect laughing gull (Leucophaeus atricilla) nests in Jamaica Bay as part of an ongoing monitoring effort at the John F. Kennedy International Airport. Our technique uses a combination of high resolution 4-band aerial imagery captured via manned aircraft with a multispectral UltraCam Falcon M2 camera, LiDAR point cloud data, and land cover data derived from a bathymetric LiDAR point cloud to classify and extract laughing gull nests. Our ruleset uses the site (topographic position of nest objects), tone (spectral characteristic of nest objects), shape, size, and association (nearby objects commonly found with the objects of interest that help identify them) elements of image interpretation, as well as NDVI and a sublevel object examination to classify and extract nests. The ruleset achieves a producer’s accuracy of 98% as well as a user’s accuracy of 65% and a kappa of 0.696, indicating that it extracts a majority of the nests in the imagery while reducing errors of commission to only 35% of the final results. The remaining errors of commission are difficult for the software to differentiate without also impacting the number of nests successfully extracted and are best addressed by a manual verification of output results as part of a semi-automated workflow in which the OBIA is used to complete the initial search of the imagery and the results are then systematically verified by the user to remove errors. This eliminates the need to manually search entire sets of imagery for nests, resulting in a much more efficient and less error prone methodology than previous unassisted image interpretation techniques. Because of the extensibility of OBIA software and the increasing availability of imagery due to small unmanned aircraft systems (sUAS), our methodology and its benefits have great potential for adaptation to other species surveyed using aerial imagery to enhance wildlife population monitoring.

This study aimed to compare and assess different Geographic Object-Based Image Analysis (GEOBIA) and machine learning algorithms using unmanned aerial vehicles (UAVs) multispectral imagery. Two study sites were provided, a bergamot and an onion crop located in Calabria (Italy). The Large-Scale Mean-Shift (LSMS), integrated into the Orfeo ToolBox (OTB) suite, the Shepherd algorithm implemented in the Python Remote Sensing and Geographical Information Systems software Library (RSGISLib), and the Multi-Resolution Segmentation (MRS) algorithm implemented in eCognition, were tested. Four classification algorithms were assessed: K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Random Forests (RF), and Normal Bayes (NB). The obtained segmentations were compared using geometric and non-geometric indices, while the classification results were compared in terms of overall, user’s and producer’s accuracy, and multi-class F-scoreM. The statistical significance of the classification accuracy outputs was assessed using McNemar’s test. The SVM and RF resulted as the most stable classifiers and less influenced by the software used and the scene’s characteristics, with OA values never lower than 81.0% and 91.20%. The NB algorithm obtained the highest OA in the Orchard-study site, using OTB and eCognition. NB performed in Scikit-learn results in the lower (73.80%). RF and SVM obtained an OA>90% in the Crop-study site.

Cadastral map environment is directed, more than ever before, towards Artificial Intelligence use to produce fast and accurate maps and keep up with the huge population growth. The traditional approach currently in production of maps is expensive and effort-intensive in addition to be considered as highly timeconsuming process. UAV-based cadastral mapping imagery that use automatic techniques are newly being exploited to accelerate the process of production or updating. The state-of-the-art intelligent algorithms are capable to extract land boundaries from images better than conventional techniques. This paper presents an automatic workflow of cadastral map production based on land boundary and automatic feature extraction from UAV-based imageries. The developed workflow involved four steps: (i) Flight planning and Ground Control Points (GCPs) observation, (ii) image pre-processing, (iii) image segmentation (iv) boundary detection and extraction (classification), (v) postprocessing, and (vi) accuracy assessment. The study area includes geometrical and affect spectral reflectivity (slums) covered by 206 images those captured using a quad-rotary UAV (DJI Phantom 4 pro). The mosaic image of the Area of Interest (AOI) was produced by following Structure from Motion /Multi-view Stereo (SfM-MVS) photogrammetry which covers 0.592 km2 ground area using pix4DMapper software. The MultiResolution Segmentation (MRS) algorithm was applied for object generation and later both spectral and geometrical information (area, brightness, border and the normalized digital surface model NDSM)) utilised to extract the boundaries by Object Based Image Analysis (OBIA) rule-based expert systems in eCognition software. The most exciting challenge in this AOI was buildings separation depending on number of buildings using the QGIS software. The accuracy assessment of the obtained results showed that 88% of the extracted boundaries were correct following automatic extraction routine when compared to manual digitizing. This approach can highly compensate time, efforts, and low accuracy outcomes from traditional surveying and manual registration approaches applied in Iraqi institutes nowadays. The presented approach can definitely help to speed up map production phase and keep pace up with population expansion and modern technologies. [ABSTRACT FROM AUTHOR]

Rice is one of the most important food staples in many countries, particularly Iran. Because irrigated rice production differs from other agricultural fields, this study developed a paddy field mapping model based on phenological aspects, various satellite sensor data, and an object-based approach. This study uses the phonological features of rice plants as well as annual data on land surface temperature (LST) to produce the paddy map. The core remote sensing data consists of the yearly LST from MODIS and multi-temporal Landsat-8 satellite imagery. The detection of phenological characteristics and the selection of relevant Landsat images were made possible by analyzing the LST time series with Google Earth Engine. After that, object-based image classification and fuzzy functions are used to create flexible and comprehensible rules for discovering paddy fields in Rasht, Iran. Data such as the digital elevation model (DEM) and spectral indices including NDVI, EVI, and LSWI were employed to improve the object-based classification. Due to the unique properties of paddy lands, a DEM of 12.5 m obtained from the ALOS PALSAR sensor could help distinguish paddy lands from other vegetation. A comparison finally made between the object-based and pixel-based classification methods showed that the former is more accurate. Overall accuracy and kappa coefficient for the pixel-based classification approach were 92% and 0.89, respectively, whereas overall accuracy and kappa coefficient for the object-based classification method were 94% and 0.92, respectively. Eventually, the producer's accuracy of the paddy class has increased from 88% to 94%. [ABSTRACT FROM AUTHOR]

This study aimed to compare and assess different Geographic Object-Based Image Analysis (GEOBIA) and machine learning algorithms using unmanned aerial vehicles (UAVs) multispectral imagery. Two study sites were provided, a bergamot and an onion crop located in Calabria (Italy). The Large-Scale Mean-Shift (LSMS), integrated into the Orfeo ToolBox (OTB) suite, the Shepherd algorithm implemented in the Python Remote Sensing and Geographical Information Systems software Library (RSGISLib), and the Multi-Resolution Segmentation (MRS) algorithm implemented in eCognition, were tested. Four classification algorithms were assessed: K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Random Forests (RF), and Normal Bayes (NB). The obtained segmentations were compared using geometric and non-geometric indices, while the classification results were compared in terms of overall, user's and producer's accuracy, and multi-class F-scoreM. The statistical significance of the classification accuracy outputs was assessed using McNemar's test. The SVM and RF resulted as the most stable classifiers and less influenced by the software used and the scene's characteristics, with OA values never lower than 81.0% and 91.20%. The NB algorithm obtained the highest OA in the Orchard-study site, using OTB and eCognition. NB performed in Scikit-learn results in the lower (73.80%). RF and SVM obtained an OA>90% in the Crop-study site. [ABSTRACT FROM AUTHOR]

IntroductionCity is considered as a dynamic phenomenon that is transforming and changing over time. The set of human elements and artificial factors are effective in creating this dynamism and transformation (Roshani et al., 2016: 32). In fact, urban landscapes are a combination of natural and human components. The expansion of cities has occurred with unprecedented growth throughout the world in the last few decades. The main concern of this rapid development is its profound impacts on urban ecosystems and biodiversity due to fragmentation.This issue has increased interest among ecological researchers to examine the structure of urban landscapes and environmental outcomes and, consequently, the physical development of cities (Fan et al., 2017). Understanding the landscape pattern, changes and interactions between human activities and natural phenomena is essential for land management and decision- making (Kammer et al., 2015). It should be noted that traditional methods such as mapping to examine changes in land uses are time-consuming and costly and require special skills (Nazarnejad et al., 2018: 24). In this regard, the emergence of remote sensing techniques and GIS has enabled researchers, planners and city managers to have a comprehensive view of land and land use change over time at a lower cost (Aryana et al., 2010).Data and MethodNowadays, the concept of cities is unperceivable without considering growth in various forms. In fact, urban life and its physical and natural impacts are considered as one of the important topics in planning and macro management. In the present study, Landsat satellite images with a resolution of 30 meters, TM and OLI sensors were used for the years 1984, 2000, 2018 and 2019. After pre-processing and processing, the images were classified in eCognition software using the nearest neighbor algorithm. Object-oriented classification is the newest method of classification based on segmentation.The images were classified into 6 categories (construction, gardens and green space, water, vegetation, barren and rangeland) to extract the land cover map and detect the changes in the images resulting from classification, and the generated maps entered Fragstats software to measure land use metrics. This software obtains metrics at three levels: spot, land class, and landscape (Marx, 1995: 23). One of the advantages of assessment by using landscape metrics is to help gain knowledge on the level of development or fragmentation of the study area in the shortest time possible with digital data that is comparable to replicate the study in future. Given the aim of this study, a class of metrics was analyzed.The NP metric has been increasing for all three cities, reflecting the fact that land uses have fragmented and lost their integrity over time in recent decades due to the growth of urbanization and the increase in construction. The LPI metric for the construction land use of Tabriz city had the lowest value for 1984 and has had an increasing trend in later periods, and the cities of Urmia and Maragheh, in addition to the construction land use show an increase in barren land use. The ED metric calculates spot margin density and uses it to measure the degree of connection of land uses. Due to an increase in construction, land uses of the three cities have reduced and they have faced degradation of rangeland over a 35-year period.LSI metric: This indicator represents the standardized size of total length of edge or edge density. The land uses of construction, gardens and green space are showing increasing trend for Tabriz and Maragheh cities, and land uses of vegetation, gardens and green space and construction are showing an increasing trend for Urmia city. This disorder has been more severe between 2000 and 2019. It suggests that the shape of the landscape in cities has become more complex and geometrically irregular. Then, the patterns of urban development trends were analyzed using the Landscape Index. This index is defined according to buffer. In this index, if the newly grown spot belongs to the center and around it, it will be infilling type, if the development is from the margin of empty fields, it will be edge-expansion type, and if the expansion of the area is far from the main area and in empty parts, it will be outlying type.Results and DiscussionNowadays, changing lifestyles and meeting the vital needs of human beings in different regions requires basic information that satellite images make it possible for planners and managers by collecting information from land sources in a regular and systematic framework. Obtaining quantitative data and preparing maps that are a reflection of biological and human phenomena on the surface of the earth over time will solve one of the problems of urban human beings, namely environmental problems. Good urban management requires classification maps in addition to a proper understanding of the nature of components of the landscape. Gaining knowledge on the process of change in the city and directions of its development will play an effective role in managing the current situation.Results obtained for three periods suggest that urban construction had the most changes in the landscape of ​​Tabriz and Maragheh cities between 1984 and 2019, which has led to a reduction in rangeland. The metropolis of Urmia shows the change of agricultural lands and an increase in gardens, in addition to construction. Also, based on the landscape development index, it was found that Tabriz and Urmia develops mostly from suburbs and outskirts, indicating the construction of towns and marginalization development from the center to the outskirts. Expansion of commercial centers and reconstruction of old areas may be one of its reasons. In the Maragheh city, expansion is from the center, which includes the increase of the rural population or the integration of the villages and from distant area, which indicates development of gardens and other things. In general, several influential factors were considered in the present study. By obtaining quantitative data (according to a management principle, non-quantified data cannot be managed) and comparing them with classified maps using object-oriented classification technique, as one of the most accurate and up-to-date classifications, the ecology of cities was examined over a period of 35 years to provide basic information for planners and city managers for major cities that are increasingly growing.

Background Despite availability of valuable ecological data in published thematic maps, manual methods to transfer published maps to a more accessible digital format are time-intensive. Application of object-based image analysis makes digitization faster. Methods Using object-based image analysis followed by random forests classification, we rapidly digitized choropleth maps of white-tailed deer (Odocoileus virginianus) densities in the conterminous US during 1982 and 2001 to 2005 (hereafter, 2003), allowing access to deer density information stored in images. Results The digitization process took about one day each per deer density map, of which about two hours was computer processing time, which will differ due to factors such as resolution and number of objects. Deer were present in 4.75 million km2 (60% of the area) and 5.56 million km2 (70%) during 1982 and 2003, respectively. Population and density in areas with deer presence were 17.15 million and 3.6 deer/km2 during 1982 and 29.93 million and 5.4 deer/km2 during 2003. Greatest densities were 7.2 deer/km2 in Georgia during 1982 and 14.6 deer/km2 in Wisconsin during 2003. Six states had deer densities ≥9.8 deer/km2 during 2003. Colorado, Idaho, and Oregon had greatest increases in population and area of deer presence, and deer expansion is likely to continue into western states. Error in these estimates may be similar to error resulting from differential reporting by state agencies. Deer densities likely are within historical levels in most of the US. Discussion This method rapidly reclaimed informational value of deer density maps, enabling greater analysis, and similarly may be applied to digitize a variety of published maps to geographic information system layers, which permit greater analysis.

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.

Green infrastructure (GI) mapping and monitoring is crucial in urban areas, and remote sensing is widely used to accomplish the task. Improved moderate resolution Sentinel-2A (10 m) and LandSat-8 (15 m) images, in place of commercial satellite images, enable GI mapping with little to no cost. Considering so, the objective of this paper is to evaluate the potential of GI feature extraction of Sentinel-2A (S2) and LandSat-8 (L8) (freely available images) using the Object Based Image Analysis (OBIA) method. The advantage of using OBIA over pixel-based analysis has been investigated primarily with very high resolution images. Using OBIA, bottom up (i.e. Multiresolution) and top down (i.e. Spectral Difference) segmentation were implemented using eCognition to obtain image objects for both S2 and L8 images. Then, rule-based classification was performed to extract GI areas from the objects. NDVI, NDWI, NIR/R ratios were utilized in rule set development, after several trial and error process. Both S2 and L8 provided acceptable extraction of GI for urban areas. However, with an overall accuracy of 71.24%, S2 was more effective when extracting GI areas. Shadows along roads and high rise buildings caused some inaccuracy in classification.

In this project, which is aimed to determine the presence of olive trees, the template method which is applied manually (screen digitize) and OLICOUNT software which can make semiautomatic counting, eCognition software which is an object-based approach, and new M-OLICOUNT methods created by modifying the OLICOUNT were compared. In this study, 60 cm resolution images of Quickbird satellite including NIR wavelength energy were used. In order to prevent the decrease of accuracy by other trees mixing with the olive trees, the image of the winter months when the trees shed their leaves was used. As the study area, Karaot Village and its environs in the northeast of Torbali district of ízmir province, villages in the center and surrounding of Urla District and lands in Mordogan Town of Karaburun District where the olive cover is widespread were chosen. As a result, it was determined that the accuracy rate of manual counting method was higher in comparison with other methods in the areas where olive trees were found mixed with natural vegetation where the topography was sloped under the conditions of Aegean Region and therefore the shadow effect was high. The other three methods were found to provide better performance in terms of time, labor and cost in areas which are flat and with a slope that is close to being flat and where the olive tree cultivation is made as an orchard production. [ABSTRACT FROM AUTHOR]

Background. Despite availability of valuable ecological data in published thematic maps, manual methods to transfer published maps to a more accessible digital format are time-intensive. Application of object-based image analysis makes digitization faster. Methods. Using object-based image analysis followed by random forests classification, we rapidly digitized choropleth maps of white-tailed deer (Odocoileus virginianus) densities in the conterminous US during 1982 and 2001 to 2005 (hereafter, 2003), allowing access to deer density information stored in images. Results. The digitization process took about one day each per deer density map, of which about two hours was computer processing time, which will differ due to factors such as resolution and number of objects. Deer were present in 4.75 million km2 (60% of the area) and 5.56 million km2 (70%) during 1982 and 2003, respectively. Population and density in areas with deer presence were 17.15 million and 3.6 deer/km2 during 1982 and 29.93 million and 5.4 deer/km2 during 2003. Greatest densities were 7.2 deer/km2 in Georgia during 1982 and 14.6 deer/km2 in Wisconsin during 2003. Six states had deer densities ≥9.8 deer/km2 during 2003. Colorado, Idaho, and Oregon had greatest increases in population and area of deer presence, and deer expansion is likely to continue into western states. Error in these estimates may be similar to error resulting from differential reporting by state agencies. Deer densities likely are within historical levels in most of the US. Discussion. This method rapidly reclaimed informational value of deer density maps, enabling greater analysis, and similarly may be applied to digitize a variety of published maps to geographic information system layers, which permit greater analysis. [ABSTRACT FROM AUTHOR]

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The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.

Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush (Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature AnalystTM for ArcMap 10.1®, ENVI Feature Extraction®, and Trimble eCognition Developer 8.2®) to extract tree canopy cover using NAIP (National Agricultural Imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point intercept) on 309 plots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was >45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature AnalystTM, and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point intercept method (r = 0.85 for ENVI, 0.83 for Feature AnalystTM, and 0.83 for eCognition). All software packages accurately and inexpensively extracted P-J canopy cover from NAIP imagery when the imagery was not blurred, and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel’s oak), which had similar spectral values as P-J.

Intertidal habitats like oyster reefs and salt marshes provide vital ecosystem services including shoreline erosion control, habitat provision, and water filtration. However, these systems face significant global change as a result of a combination of anthropogenic stressors like coastal development and environmental stressors such as sea-level rise and disease. Traditional intertidal habitat monitoring techniques are cost and time-intensive, thus limiting how frequently resources are mapped in a way that is often insufficient to make informed management decisions. Unoccupied aircraft systems (UASs) have demonstrated the potential to mitigate these costs as they provide a platform to rapidly, safely, and inexpensively collect data in coastal areas. In this study, a UAS was used to survey intertidal habitats along the Gulf of Mexico coastline in Florida, USA. The structure from motion photogrammetry techniques were used to generate an orthomosaic and a digital surface model from the UAS imagery. These products were used in a geographic object-based image analysis (GEOBIA) workflow to classify mudflat, salt marsh, and oyster reef habitats. GEOBIA allows for a more informed classification than traditional techniques by providing textural and geometric context to habitat covers. We developed a ruleset to allow for a repeatable workflow, further decreasing the temporal cost of monitoring. The classification produced an overall accuracy of 79% in classifying habitats in a coastal environment with little spectral and textural separability, indicating that GEOBIA can differentiate intertidal habitats. This method allows for effective monitoring that can inform management and restoration efforts.

Nghiên cứu này lựa chọn các tham số phân đoạn phù hợp đối với ảnh vệ tinh đa phổ Landsat 8 để phân loại thảm phủ/sử dụng đất (LULC-Land Use Land Cover). Đầu tiên thuật toán phân đoạn đa phân giải (Multiresolution) được sử dụng để phân đoạn ảnh theo các mức độ khác nhau. Hai tham số được sử dụng để phân đoạn ảnh là tham số quy mô (Scale) và tham số hình dạng (Shape). Nghiên cứu đã thử nghiệm phân đoạn với các cấp độ khác nhau của hai tham số này. Với tham số Scale, có 3 cấp độ được thử nghiệm bao gồm: 20, 30, 40, trong khi tham số Shape các giá trị thay đổi bao gồm 0,1, 0,2, 0,3, 0,4. Sau đó sử dụng thuật toán Nearest Neighbor để phân loại ảnh thành 7 lớp thảm phủ/sử dụng đất dựa vào vùng mẫu đã được lựa chọn trên thực địa và dữ liệu có sẵn. Cuối cùng sử dụng bộ dữ liệu độc lập để đánh giá kết quả phân loại dựa vào độ chính xác toàn bộ (Overall Accuracy) và hệ số Kappa. Kết quả chỉ ra khi sử dụng ảnh vệ tinh Landsat 8 để phân thành 7 loại LULC bao gồm: Đất nông nghiệp, Cao su, Khu dân cư, Mặt nước, Rừng thường xanh, Rừng bán thường xanh và Rừng Khộp, các tham số phù hợp lần lượt là: Scale=40, Shape=0,3 cho độ chính xác cao nhất với độ chính xác toàn bộ là 81,9% và hệ số Kappa là 0,788.

The benefits of tree canopy in urban and suburban landscapes are increasingly well known: stormwater runoff control, air-pollution mitigation, temperature regulation, carbon storage, wildlife habitat, neighborhood cohesion, and other social indicators of quality of life. However, many urban areas lack high-resolution tree canopy maps that document baseline conditions or inform tree-planting programs, limiting effective study and management. This paper describes a GEOBIA approach to tree-canopy mapping that relies on existing public investments in LiDAR, multispectral imagery, and thematic GIS layers, thus eliminating or reducing data acquisition costs. This versatile approach accommodates datasets of varying content and quality, first using LiDAR derivatives to identify aboveground features and then a combination of LiDAR and imagery to differentiate trees from buildings and other anthropogenic structures. Initial tree canopy objects are then refined through contextual analysis, morphological smoothing, and small-gap filling. Case studies from locations in the United States and Canada show how a GEOBIA approach incorporating data fusion and enterprise processing can be used for producing high-accuracy, high-resolution maps for large geographic extents. These maps are designed specifically for practical application by planning and regulatory end users who expect not only high accuracy but also high realism and visual coherence.

Green infrastructure (GI) mapping and monitoring is crucial in urban areas, and remote sensing is widely used to accomplish the task. Improved moderate resolution Sentinel-2A (10 m) and LandSat-8 (15 m) images, in place of commercial satellite images, enable GI mapping with little to no cost. Considering so, the objective of this paper is to evaluate the potential of GI feature extraction of Sentinel-2A (S2) and LandSat-8 (L8) (freely available images) using the Object Based Image Analysis (OBIA) method. The advantage of using OBIA over pixel-based analysis has been investigated primarily with very high resolution images. Using OBIA, bottom up (i.e. Multiresolution) and top down (i.e. Spectral Difference) segmentation were implemented using eCognition to obtain image objects for both S2 and L8 images. Then, rule-based classification was performed to extract GI areas from the objects. NDVI, NDWI, NIR/R ratios were utilized in rule set development, after several trial and error process. Both S2 and L8 provided acceptable extraction of GI for urban areas. However, with an overall accuracy of 71.24%, S2 was more effective when extracting GI areas. Shadows along roads and high rise buildings caused some inaccuracy in classification. [ABSTRACT FROM AUTHOR]

This article is about choosing an optimal method of water extraction in arid areas. Water is a very important asset in arid areas. In order to control water allocation, spetialists need fast and accurate information about water objects and irrigation sets. Remote sensing data are a useful tool for this. Many researchers have used these data with satisfying results. With remote sensing data a lot of methods and ways to extract water objects have been created. Choosing a more effective method has been an important goal for researches. In our paper a lot of water extraction methods are compared in the Beshbulak and the Yangiobod region. According to the results, the most accurate method of water extraction for arid zones is chosen. This method is water objects extracting with the NIR2 (near infrared) band of VHR (very high resolution) data. [ABSTRACT FROM AUTHOR]

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The Banana Bunchy Top Virus (Genus: Babuvirus) reduces plant growth and prevents banana production. Because of the very large number of properties with banana plants in South East Queensland, Australia, a mapping approach was developed to delineate individual and clusters of banana plants to help plant identification and enable prioritization of plant inspections for Banana Bunchy Top Virus. Due to current outbreaks in South East Queensland, there are concerns that the virus may spread to the major banana growing districts further north. The mapping approach developed was based on very high spatial resolution airborne orthophotos. Object-based image analysis was used to: (1) detect banana plants using edge and line detection approaches; (2) produce accurate and realistic outlines around classified banana plants; and (3) evaluate the mapping results. The mapping approach was developed based on 10 image tiles of 1 km × 1 km and was applied to orthophotos (3600 image tiles) from September 2011 covering the entire Sunshine Coast Region in South East Queensland. Based on field inspections of the classified maps, a user’s mapping accuracy of 88% (n = 146) was achieved. The results will facilitate the detection of banana plants and increase the inspection rate of Banana Bunchy Top Virus in the future.

The continuous expansion of built-up areas in the urban environment at the expense of green spaces brings up numerous environmental problems, for which accurate and efficient solutions should be found. The assessment of ecosystem services developed within the field of landscape ecology is playing an ever more important role in environmental sciences and thus may offer suitable answers. Such assessments can be carried out by developing indicators. Accordingly, in the case of urban trees, an accurate quantitative characterization of their services (such as e.g. carbon sequestration, pollutant removal and microclimate regulation) is also needed. The aim of this study is to establish a generally applicable method based on indicator development, using widely available data. In the case of urban green spaces there are several services for which the development of proper indicators and evaluation methods requires a delineation of tree crowns, or at least the crown projection area. Accordingly, in our work, we map the crown projection area of a large and popular urban park of Szeged, Széchenyi square, using object-based image analysis on UltraCamD digital orthophotos. Following a multiresolution segmentation the classification of the resulting objects was carried out, using the eCognition image analysis software. Besides fulfilling the policy objectives related to the evaluation of urban ecosystem services, the produced crown base can also be used in several other types of urban ecological and urban climatological studies (e.g. urban climate modelling, human-comfort assessment). In this paper the first results are presented.

The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 m2 were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500−1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

Effective management of wildlife populations requires identification of the factors limiting their growth. The Piping Plover (Charadrius melodus) is an imperiled, disturbance-dependent, shorebird species that nests on broad, sparsely vegetated beaches, sandbars, and lakeshores. In areas minimally affected by human use, plover habitat loss occurs through vegetation encroachment and erosion. Alternatively, habitat availability may be increased by sand deposition caused by storm- or flood-induced sediment transport or scouring that removes vegetation, or by receding lake levels. To test the hypothesis that plover populations are limited by available breeding habitat, we estimated the amount of habitat available before and after four significant storm and flooding events (i.e., disturbance) by classifying pre- and postdisturbance aerial imagery. We then evaluated the population changes that occurred after disturbance-related habitat alterations. Additionally, we report on population changes from four population increases that occurred after habitat creation events for which we did not have imagery suitable for classification. The storm and flood effects considered were those from hurricanes and nor'easters on barrier islands of Virginia, North Carolina, New York, and Maryland, USA, and those from floods and high water output from the Gavins Point Dam on the Missouri River between South Dakota and Nebraska, USA. The amount of nesting habitat increased 27-950% at these sites, and plover populations increased overall 72-622% after these events (increase of 8-217 pairs in 3 to 8 years after the disturbance, average 12-116% increase annually). The demographic changes were driven by productivity in some cases and probably by increases in immigration in others, and occurred simultaneously with regional increases. Our results support our hypothesis that the focal plover populations were at or near carrying capacity and are habitat limited. Currently, human interventions such as beach stabilization, the construction of artificial dunes, and dams reduce natural disturbance, and therefore, the carrying capacity, in many plover breeding areas. If these interventions were reduced or modified in such a way as to create and improve habitat, plover populations would likely reach higher average numbers and the potential for achieving recovery goals would be increased.

The increasing volume of remote sensing data with improved spatial and temporal resolutions generates unique opportunities for monitoring and mapping of crops. We compared multiple single-band and multi-band object-based time-constrained Dynamic Time Warping (DTW) classifications for crop mapping based on Sentinel-2 time series of vegetation indices. We tested it on two complex and intensively managed agricultural areas in California and Texas. DTW is a time-flexible method for comparing two temporal patterns by considering their temporal distortions in their alignment. For crop mapping, using time constraints in computing DTW is recommended in order to consider the seasonality of crops. We tested different time constraints in DTW (15, 30, 45, and 60 days) and compared the results with those obtained by using Euclidean distance or a DTW without time constraint. Best classification results were for time delays of both 30 and 45 days in California: 79.5% for single-band DTWs and 85.6% for multi-band DTWs. In Texas, 45 days was best for single-band DTW (89.1%), while 30 days yielded best results for multi-band DTW (87.6%). Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1%. We discuss the implications of DTW dissimilarity values in understanding the classification errors. Considering the possible sources of errors and their propagation throughout our analysis, we had combined errors of 22.2% and 16.8% for California and 24.6% and 25.4% for Texas study areas. The proposed workflow is the first implementation of DTW in an object-based image analysis (OBIA) environment and represents a promising step towards generating fast, accurate, and ready-to-use agricultural data products.

Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush (Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature AnalystTM for ArcMap 10.1®, ENVI Feature Extraction®, and Trimble eCognition Developer 8.2®) to extract tree canopy cover using NAIP (National Agricultural Imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point intercept) on 309 plots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was >45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature Analyst™, and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point intercept method (r = 0.85 for ENVI, 0.83 for Feature Analyst™, and 0.83 for eCognition). All software packages accurately and inexpensively extracted P-J canopy cover from NAIP imagery when the imagery was not blurred, and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel's oak), which had similar spectral values as P-J. [ABSTRACT FROM AUTHOR]

This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyperplanes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas. [ABSTRACT FROM AUTHOR]

This paper describes the automated classification of tree species composition from Ikonos 4-meter imagery using an object-oriented approach. The image was acquired over a man-planted forest area with the proportion of various forest types (conifers, broadleaved, mixed) in the Krušné hory Mts., Czech Republic. In order to enlarge the class signature space, additional channels were calculated by low-pass filtering, IHS transformation and Haralick texture measures. Employing these layers, image segmentation and classification were conducted on several levels to create a hierarchical image object network. The higher level separated the image into smaller parts regarding the stand maturity and structure, the lower (detailed) level assigned individual tree clusters into classes for the main forest species. The classification accuracy was assessed by comparing the automated technique with the field inventory using Kappa coefficient. The study aimed to create a rule-base transferable to other datasets. Moreover, the appropriate scale of common image data and utilisation in forestry management are evaluated.

Information about the types of land cover and its use is obtained by the visual interpretation of the color composite of satellite images or by the use of automatic classification algorithms. For obvious reasons, the automatic classification methods make it possible to obtain information quicker and much faster than the traditional interpretation method.

Here we present results from ongoing work where we apply an object oriented mapping algorithm developed in eCognition in order to automatically identify and digitally map avalanche deposits. The algorithm performance is compared with respect to a selected number of manually digitized avalanche outlines mapped by avalanche experts. [ABSTRACT FROM AUTHOR]