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Assessment of Spatial Patterns of Backyard Shacks Using Landscape Metrics

DRONES 2023年第9期7卷

作者： Mudau, Naledzani Mhangara, Paidamwoyo Univ Witwatersrand Fac Sci Sch Geog Archaeol & Environm Studies Johannesburg 2000 South Africa South African Natl Space Agcy Mark Shuttleworth StInnovat Hub Enterprise Bldg ZA-0184 Pretoria South Africa

Urban informality in developing economies like South Africa takes two forms: freestanding shacks are built in informal settlements, and backyard shacks are built in the yard of a formal house. The latter is evident in established townships around South African cities. In contrast to freestanding shacks, the number of backyard shacks has increased significantly in recent years. The study assessed the spatial patterns of backyard shacks in a formal settlement containing low-cost government houses (LCHs) using Unmanned Aerial Vehicle (UAV) products and landscape metrics. The backyard shacks were mapped using object-based image analysis (OBIA), which uses height information, vegetation index, and radiometric values. We assessed the effectiveness of rule-based and Random Forest (RF) OBIA techniques in detecting formal and informal structures. Informal structures were further classified as backyard shacks using spatial analysis. The spatial patterns of backyard shacks were assessed using eight shapes, aggregation, and landscape metrics. The analysis of the shape metrics shows that the backyard shacks are primarily square, as confirmed by a higher shape index value and a lower fractional dimension index value. The contiguity index of backyard shack patches is 0.6. The values of the shape metrics of backyard shacks were almost the same as those of formal and informal dwelling structures. The values of the assessed aggregation metrics of backyard shacks were more distinct from formal and informal structures compared with the shape metrics. The aggregation metrics show that the backyard shacks are less connected, less dense, and more isolated from each other compared with formal and freestanding shacks. The Shannon's Diversity Index and Simpson's Evenness Index values of informal settlements and formal areas with backyard shacks are almost the same. The results achieved in this study can be used to understand and manage informality in formal settlements.

关键词： backyard shacks informal settlements unmanned aerial vehicles object-based image analysis

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Inventory of Glacial Lake in the Southeastern Qinghai-Tibet Plateau Derived from Sentinel-1 SAR image and Sentinel-2 MSI image

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REMOTE SENSING 2023年第21期15卷 5142-5142页

作者： Zhang, Yuan Zhao, Jun Yao, Xiaojun Duan, Hongyu Yang, Jianxia Pang, Wenlong Northwest Normal Univ Coll Geog & Environm Sci Lanzhou 730070 Gansu Peoples R China China Geol Survey Xining Ctr Nat Resources Comprehens Survey Xining 810000 Peoples R China

The glacial lakes in the Southeastern Qinghai-Tibet Plateau (SEQTP) have undergone dramatic expansion in the context of global warming, leading to several glacial lake outburst floods (GLOFs) disasters. However, there is a gap and incompleteness in glacial lake inventories across this area due to the heavy cloud cover. In this study, an updated and comprehensive glacial lake inventory was produced by object-based image analysis (OBIA) and manual vectorization based on the Sentinel-1 SAR and Sentinel-2 MSI images acquired in 2022. Detailed steps regarding the OBIA were provided, and the feature set of Sentinel-1 SAR images suitable for extracting glacial lakes was also determined in this paper. We found that the mean combination of ascending-orbit and descending-orbit images is appropriate for mapping glacial lakes. VV-polarized backscattering coefficients from ascending-orbit achieved a better performance for delineating glacial lakes within the study area. Moreover, the distribution of glacial lakes was characterized in terms of four aspects: size, type, elevation, and space. There were 3731 glacial lakes with a total area of 1664.22 +/- 0.06 km2 in the study area;most of them were less than 0.07 km2. Ice-contacted lakes were primarily located in the Palongzangbo basin (13.24 +/- 0.08 km2). Nyang Qu basin had the most abundant glacial lake resources (2456 and 93.32 +/- 0.18 km2). A comparison with previously published glacial lake datasets demonstrated that our dataset is more complete. This inventory is useful for evaluating water resources, studying glacier-glacial lake interactions, and assessing GLOFs' susceptibility in the SEQTP.

关键词： glacial lake inventory Sentinel-1 SAR image Sentinel-2 MSI image object-based image analysis manual vectorization Southeastern Qinghai-Tibet Plateau

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On-farm evaluation of UAV-based aerial imagery for season-long weed monitoring under contrasting management and pedoclimatic conditions in wheat

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年 204卷

作者： Anderegg, Jonas Tschurr, Flavian Kirchgessner, Norbert Treier, Simon Schmucki, Manuel Streit, Bernhard Walter, Achim Swiss Fed Inst Technol Inst Integrat Biol Plant Pathol Grp Zurich Switzerland Swiss Fed Inst Technol Inst Agr Sci Crop Sci Grp Zurich Switzerland Agroscope Varieties & Prod Tech Plants & Plant Prod Nyon Switzerland Bern Univ Appl Sci Sch Agr Forest & Food Sci HAFL Zollikofen Switzerland Univ Str 2 CH-8092 Zurich Switzerland

Timely availability of weed infestation maps is a key prerequisite for the implementation of site-specific weed management practices. Low-altitude aerial imagery obtained from unmanned aerial vehicles (UAVs) has shown significant potential for weed detection in crops. However, most studies focused on wide-spaced row crops such as maize and sunflower and evaluated proposed methods on single or few well-characterized experimental sites at specific points in time representing a limited range of application scenarios. This study evaluated the feasibility of weed detection in on-farm wheat fields characterized by a narrow row spacing, throughout the early and late developmental stages using UAV imagery and ground-based high-resolution imagery. image data was obtained for nine sites, representing a wide range of management and pedoclimatic conditions. These sites can be seen as a representative sample of scenarios that would be encountered in practice. A high within-and across-site as well as temporal variation was observed for weed infestation levels and weed population species composition, highlighting the need for spatially and temporally resolved weed mapping. image-based classification of vegetation objects as crop or weed plants was achieved with an accuracy of 0.88 and 0.72 in ground-based high resolution images and UAV-based aerial images captured from an altitude of 10 m, respectively. The accuracy of pixel-wise, vegetation-index-based weed infestation estimation during the late vegetative stages varied strongly across sites. Our results highlight the critical importance of a high ground resolution for weed detection using object-based image analysis during the critical growth stages of wheat and of robust methods that are applicable across a range of scenarios. This suggested that future research aiming at a rapid implementation of site-specific weed management in wheat should focus on the development of ground-based systems. Yet, aerial monitoring of wheat stan

关键词： Precision agriculture object-based image analysis OBIA Vegetation index Site-specific management Remote sensing

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Identification of Abandoned Logging Roads in Point Reyes National Seashore

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REMOTE SENSING 2023年第13期15卷 3369页

作者： Wiskes, William Blesius, Leonhard Hines, Ellen San Francisco State Univ Dept Geog & Environm 1600 Holloway Ave San Francisco CA 94132 USA San Francisco State Univ Estuary & Ocean Sci Ctr Tiburon CA 94920 USA

Temporary roads are often placed in mountainous regions for logging purposes but then never decommissioned and removed. These abandoned forest roads often have unwanted environmental consequences. They can lead to altered hydrological regimes, excess erosion, and mass wasting events. These events can affect sediment budgets in streams, with negative consequences for anadromous fish populations. Maps of these roads are frequently non-existent;therefore, methods need to be created to identify and locate these roads for decommissioning. Abandoned logging roads in the Point Reyes National Seashore in California, an area partially under heavy forest canopy, were mapped using object-based image processing in concert with machine learning. High-resolution Q1 LiDAR point clouds from 2019 were used to create a bare earth model of the region, from which a slope model was derived. This slope model was then subjected to segmentation algorithms to identify and isolate regions of differing slopes. Regions of differing slopes were then used in a convolutional neural network (CNN), and a maximum likelihood classifier was used to delineate the historic road network. The accuracy assessment was conducted using historic aerial photos of the state of the region post-logging, along with ground surveys to verify the presence of logging roads in areas of question. This method was successfully able to identify road networks with a precision of 0.991 and an accuracy of 0.992. It was also found that the CNN was able to identify areas of highest disturbance to the slope gradient. This methodology is a valuable tool for decision makers who need to identify areas of high disturbance in order to mitigate adverse effects.

关键词： LiDAR object-based image analysis image segmentation CNN machine learning maximum likelihood classifier feature extraction aerial photos forest roads logging roads

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High throughput object-based image analysis of β-amyloid plaques in human and transgenic mouse brain

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JOURNAL OF NEUROSCIENCE METHODS 2012年第1期204卷 179-188页

作者： Samaroo, Harry D. Opsahl, Alan C. Schreiber, Jan O'Neill, Sharon M. Marconi, Michael Qian, Jessie Carvajal-Gonzalez, Santos Tate, Barbara Milici, Anthony J. Bales, Kelly R. Stephenson, Diane T. Pfizer Global Res Dev Neurosci Biol Groton CT 06340 USA Definiens AG D-80339 Munich Germany

Advances in imaging technology have enabled automated approaches for quantitative image analysis. In this study, a high content object based image analysis method was developed for quantification of beta-amyloid (A beta) plaques in postmortem brains of Alzheimer's disease (AD) subjects and in transgenic mice over overexpressing A beta. Digital images acquired from immunohistochemically stained sections of the superior frontal gyrus were analyzed for A beta plaque burden using a Definiens object-based segmentation approach. Blinded evaluation of A beta stained sections from AD and aged matched human subjects accurately identified AD cases with one exception. Brains from transgenic mice overexpressing A beta (PS1APP mice) were also evaluated by our Definiens object based image analysis approach. We observed an age-dependent increase in the amount of A beta plaque load that we quantified in both the hippocampus and cortex. From the contralateral hemisphere, we measured the amount of A beta in brain homogenates biochemically and observed a significant correlation between our biochemical measurements and those that we measured by our object based Definiens system in the hippocampus. Assessment of A beta plaque load in PS1APP mice using a manual segmentation technique (image-Pro Plus) confirmed the results of our object-based image analysis approach. image acquisition and analysis of 32 stained human slides and 100 mouse slides were executed in 8 h and 22 h, respectively supporting the relatively high throughput features of the Definiens platform. The data show that digital imaging combined with object based image analysis is a reliable and efficient approach to quantifying A beta plaques in human and mouse brain. (C) 2011 Elsevier B.V. All rights reserved.

关键词： High-throughput PS1APP object-based image analysis Alzheimer's disease

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Smallholder oil palm plantation sustainability assessment using multi-criteria analysis and unmanned aerial vehicles

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2023年第5期195卷 577-577页

作者： Wong, Yong Bin Gibbins, Chris Azhar, Badrul Phan, Su Shen Scholefield, Paul Azmi, Reza Lechner, Alex M. Univ Nottingham Malaysia Sch Environm & Geog Sci Semenyih 43500 Selangor Malaysia Univ Putra Malaysia Fac Forestry & Environm Serdang 43400 Selangor Malaysia Wild Asia 2 Jalan Raja Abdullah Kuala Lumpur 56000 Malaysia Lancaster Environm Ctr Ctr Ecol & Hydrol Bailrigg England Monash Univ Indonesia S Tangerang 15345 Indonesia

Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (similar to 5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).

关键词： UAV Oil palm smallholder Sustainability assessment Land cover and land use object-based image analysis Multi-criteria analysis

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A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California's Coastal Shrubs

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DRONES 2023年第7期7卷

作者： Detka, Jon Coyle, Hayley Gomez, Marcella Gilbert, Gregory S. Univ Calif Santa Cruz Dept Environm Studies 1156 High St Santa Cruz CA 95064 USA

Wildland conservation efforts require accurate maps of plant species distribution across large spatial scales. High-resolution species mapping is difficult in diverse, dense plant communities, where extensive ground-based surveys are labor-intensive and risk damaging sensitive flora. High-resolution satellite imagery is available at scales needed for plant community conservation across large areas, but can be cost prohibitive and lack resolution to identify species. Deep learning analysis of drone-based imagery can aid in accurate classification of plant species in these communities across large regions. This study assessed whether drone-based imagery and deep learning modeling approaches could be used to map species in complex chaparral, coastal sage scrub, and oak woodland communities. We tested the effectiveness of random forest, support vector machine, and convolutional neural network (CNN) coupled with object-based image analysis (OBIA) for mapping in diverse shrublands. Our CNN + OBIA approach outperformed random forest and support vector machine methods to accurately identify tree and shrub species, vegetation gaps, and communities, even distinguishing two congeneric shrub species with similar morphological characteristics. Similar accuracies were attained when applied to neighboring sites. This work is key to the accurate species identification and large scale mapping needed for conservation research and monitoring in chaparral and other wildland plant communities. Uncertainty in model application is associated with less common species and intermixed canopies.

关键词： chaparral deep learning drone machine learning neural network object-based image analysis photogrammetry remote sensing shrubland uncrewed aerial vehicle (UAV)

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Automated detection of migrating Gray whales and measurement of their bearing in satellite imagery

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MARINE MAMMAL SCIENCE 2024年第0期

作者： Rodofili, Esteban Nicolas Houegnigan, Ludwig Lecours, Vincent Klarenberg, Geraldine Cuesta, Eduardo Univ Florida Sch Nat Resources & Environm Gainesville FL USA Univ Florida Sch Forest Fisheries & Geomat Sci Gainesville FL USA Univ Politecn Cataluna Signal Theory & Commun Dept Barcelona Spain Univ Politecn Cataluna Intelligent Data Sci & Artificial Intelligence Res Barcelona Spain Univ Quebec Chicoutimi Lab Expertise & Rech Geog Appl Chicoutimi PQ Canada

Satellite images could aid in studying baleen whale migration routes given their extensive spatial coverage. We developed two object-based image analysis (OBIA) workflows to automatically detect migrating whales and their orientation. We used three sections of a Worldview-3 satellite image off California with migrating gray whales (Eschrichtius robustus), alternating training and testing under a three-fold approach in eCognition, with three classes: whale, water, and confounding objects (e.g., sea foam). After a workflow with 202 classification features, we applied feature selection through a minimum redundancy maximum relevance algorithm and retained 15 features. This selection was used in a second workflow which yielded fewer false negatives (FNs) and especially, fewer false positives (FPs) (22.6% vs. 11.1% FNs rate, 7599.6% vs. 1352.6% FP over detectable rate). While FPs were still considerable, image grid cells to review were reduced to less than 3% of cells for full manual analysis. Using Moore's test for paired circular data, we failed to find significant differences between manual and both automated or semiautomated orientation measurements (corrected for whales measured from head to tail). Most whales were oriented in the southeastern quadrant. The results are promising for satellite image studies of migration routes.

关键词： baleen whales migration routes object-based image analysis remote-sensing

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A Multi-Satellite Mapping Framework for Floating Kelp Forests

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REMOTE SENSING 2023年第5期15卷 1276-1276页

作者： Gendall, Lianna Schroeder, Sarah B. Wills, Peter Hessing-Lewis, Margot Costa, Maycira Univ Victoria SPECTRAL Remote Sensing Lab 3800 Finnerty Rd Victoria BC V8P 5C2 Canada Canadian Hydrog Serv Inst Ocean Sci 9860 West Saanich Rd Sidney BC V8L 4B2 Canada Hakai Inst Campbell River BC V0P 1H0 Canada

Kelp forests provide key habitat on the Pacific Coast of Canada;however, the long-term changes in their distribution and abundance remain poorly understood. With advances in satellite technology, floating kelp forests can now be monitored across large-scale areas. We present a methodological framework using an object-based image analysis approach that enables the combination of imagery from multiple satellites at different spatial resolutions and temporal coverage, to map kelp forests with floating canopy through time. The framework comprises four steps: (1) compilation and quality assessment;(2) preprocessing;(3) an object-oriented classification;and (4) an accuracy assessment. Additionally, the impact of spatial resolution on the detectability of floating kelp forests is described. Overall, this workflow was successful in producing accurate maps of floating kelp forests, with global accuracy scores of between 88% and 94%. When comparing the impact of resolution on detectability, lower resolutions were less reliable at detecting small kelp forests in high slope areas. based on the analysis, we suggest removing high slope areas (11.4%) from time series analyses using high- to medium-resolution satellite imagery and that error, in this case up to 7%, be considered when comparing imagery at different resolutions in low-mid slope areas through time.

关键词： kelp forests multispectral satellite time series spatial resolution object-based image analysis remote sensing

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Rapid Large-Scale Wetland Inventory Update Using Multi-Source Remote Sensing

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REMOTE SENSING 2023年第20期15卷 4960-4960页

作者： Igwe, Victor Salehi, Bahram Mahdianpari, Masoud SUNY Coll Environm Sci & Forestry Grad Program Environm Sci Syracuse NY 13210 USA SUNY Coll Environm Sci & Forestry Dept Environm Resources Engn Syracuse NY 13210 USA Mem Univ Newfoundland C CORE St John NF A1B 3X5 Canada Mem Univ Newfoundland Dept Elect & Comp Engn St John NF A1B 3X5 Canada

Rapid impacts from both natural and anthropogenic sources on wetland ecosystems underscore the need for updating wetland inventories. Extensive up-to-date field samples are required for calibrating methods (e.g., machine learning) and validating results (e.g., maps). The purpose of this study is to design a dataset generation approach that extracts training data from already existing wetland maps in an unsupervised manner. The proposed method utilizes the LandTrendr algorithm to identify areas least likely to have changed over a seven-year period from 2016 to 2022 in Minnesota, USA. Sentinel-2 and Sentinel-1 data were used through Google Earth Engine (GEE), and sub-pixel water fraction (SWF) and normalized difference vegetation index (NDVI) were considered as wetland indicators. A simple thresholding approach was applied to the magnitude of change maps to identify pixels with the most negligible change. These samples were then employed to train a random forest (RF) classifier in an object-based image analysis framework. The proposed method achieved an overall accuracy of 89% with F1 scores of 91%, 81%, 88%, and 72% for water, emergent, forested, and scrub-shrub wetland classes, respectively. The proposed method offers an accurate and cost-efficient method for updating wetland inventories as well as studying areas impacted by floods on state or even national scales. This will assist practitioners and stakeholders in maintaining an updated wetland map with fewer requirements for extensive field campaigns.

关键词： automated wetland mapping landcover Google Earth Engine object-based image analysis LandTrendr

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