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UAV-based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring

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DRONES 2023年第11期7卷

作者： Thomasberger, Aris Nielsen, Mette Moller Tech Univ Denmark Natl Inst Aquat Resources Sect Coastal Ecol DK-2800 Kgs. Lyngby Denmark

Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, as well as for the identification of ecologically relevant information such as the vegetation depth limit. To address these limitations, a payload system was developed to collect subsurface data in the form of videos and depth measurements. In a 7 ha large study area, 136 point observations were collected and subsequently used to (1) train and validate the object-based classification of aerial imagery, (2) create a class distribution map based on the interpolation of point observations, (3) identify additional ecological relevant information and (4) create a bathymetry map of the study area. The classification based on ground-truthing samples achieved an overall accuracy of 98% and agreed to 84% with the class distribution map based on point interpolation. Additional ecologically relevant information, such as the vegetation depth limit, was recorded, and a bathymetry map of the study site was created. The findings of this study show that UAV-based shallow-water monitoring can be improved by applying the proposed tool.

关键词： unoccupied aerial vehicles UAV drones ground-truthing object-based image analysis photogrammetry seagrasses coastal zone mapping shallow bathymetry benthic ecology Limfjorden

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Mapping Floods in Lowland Forest Using Sentinel-1 and Sentinel-2 Data and an object-based Approach

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FORESTS 2021年第5期12卷 553页

作者： Gasparovic, Mateo Klobucar, Damir Univ Zagreb Fac Geodesy Chair Photogrammetry & Remote Sensing Kaciceva 26 Zagreb 10000 Croatia Croatian Forests Ltd Prod & Dev Dept Ivana Mestrovica 28 Koprivnica 48000 Croatia

The impact of floods on forests is immediate, so it is necessary to quickly define the boundaries of flooded areas. Determining the extent of flooding in situ has shortcomings due to the possible limited spatial and temporal resolutions of data and the cost of data collection. Therefore, this research focused on flood mapping using geospatial data and remote sensing. The research area is located in the central part of the Republic of Croatia, an environmentally diverse area of lowland forests of the Sava River and its tributaries. Flood mapping was performed by merging Sentinel-1 (S1) and Sentinel-2 (S2) mission data and applying object-based image analysis (OBIA). For this purpose, synthetic aperture radar (SAR) data (GRD processing level) were primarily used during the flood period due to the possibility of all-day imaging in all weather conditions and flood detection under the density of canopy. The pre-flood S2 imagery, a summer acquisition, was used as a source of additional spectral data. Geographical information system (GIS) layers-a multisource forest inventory, habitat map, and flood hazard map-were used as additional sources of information in assessing the accuracy of and interpreting the obtained results. The spectral signature, geometric and textural features, and vegetation indices were applied in the OBIA process. The result of the work was a developed methodological framework with a high accuracy and speed of production. The overall accuracy of the classification is 94.94%. based on the conducted research, the usefulness of the C band of the S1 in flood mapping in lowland forests in the leaf-off season was determined. The paper presents previous research and describes the SAR parameters and characteristics of floodplain forest with a significant impact on the accuracy of classification.

关键词： rapid mapping floods forests machine learning Sentinel-1 Sentinel-2 remote sensing image classification object-based image analysis

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Quantifying the influence of individual trees on slope stability at landscape scale

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021年 286卷 112194-112194页

作者： Spiekermann, Raphael I. McColl, Sam Fuller, Ian Dymond, John Burkitt, Lucy Smith, Hugh G. Manaaki Whenua Landcare Res Palmerston North New Zealand Massey Univ Sch Agr & Environm Palmerston North New Zealand

Silvopastoralism in New Zealand's highly erodible hill country is an important form of erosion and sediment control. Yet, there has been little quantitative work to establish the effectiveness of space-planted trees in reducing shallow landslide erosion. We propose a method to provide high-resolution spatially explicit individual tree influence models at landscape scale for the dominant species in pastoral hill country. The combined hydrological and mechanical influence of trees on slopes is inferred through the spatial relationship between trees and landslide erosion. First, we delineate individual tree crowns and classify these into four dominant species classes found in New Zealand's pastoral hill country. This is the first species classification of individual trees at landscape scale in New Zealand using freely accessible data, achieving an overall accuracy of 92.6%. Second, we develop tree influence models for each species class by means of inductive inference. The inferred empirical tree influence models largely agree with the shape and distribution of existing physical root reinforcement models. Of exotic species that were planted for erosion and sediment control, poplars (Populus spp.) and willows (Salix spp.) make up 51% (109,000 trees) in pastoral hill country at a mean density of 3.2 trees/ha. In line with previous studies, poplars and willows have the greatest contribution to slope stability with an average maximum effective distance of 20 m. Yet, native kanuka (Kunzea spp.) is the most abundant woody vegetation species in pastoral hill country within the study area, with an average of 24.1 stems per ha (sph), providing an important soil conservation function. A large proportion (56% or 212.5 km(2)) of pastoral hill-country in the study area remains untreated. The tree influence models presented in this study can be integrated into landslide susceptibility modelling in silvopastoral landscapes to both quantify the reduction in landslide susceptibility ac

关键词： Individual tree crowns Tree species classification Slope stability Landslide susceptibility object-based image analysis Silvopastoralism

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object-based image Procedures for Assessing the Solar Energy Photovoltaic Potential of Heterogeneous Rooftops Using Airborne LiDAR and Orthophoto

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REMOTE SENSING 2020年第2期12卷 223-223页

作者： Tiwari, Arti Meir, Isaac A. Karnieli, Arnon Ben Gurion Univ Negev Jacob Blaustein Inst Desert Res Remote Sensing Lab IL-85105 Beer Sheva Israel Ben Gurion Univ Negev Fac Engn Sci Struct Engn Dept IL-85105 Beer Sheva Israel

Available renewable energy resources play a vital role in fulfilling the energy demands of the increasing global population. To create a sustainable urban environment with the use of renewable energy in human habitats, a precise estimation of solar energy on building roofs is essential. The primary goal of this paper is to develop a procedure for measuring the rooftop solar energy photovoltaic potential over a heterogeneous urban environment that allows the estimation of solar energy yields on flat and pitched roof surfaces at different slopes and in different directions, along with multi-segment roofs on a single building. Because of the complex geometry of roofs, very high-resolution data, such as ortho-rectified aerial photography (orthophotos), and LiDAR data have been used to generate a new object-based algorithm to classify buildings. An overall accuracy index and a Kappa index of agreement (KIA) of 97.39% and 0.95, respectively, were achieved. The paper also develops a new model to create an aspect-slope map, which combines slope orientation with the gradient of the slope and uses it to demonstrate the collective results. This study allows the assessment of solar energy yields through defining solar irradiances in units of pixels over a specific time period. It might be beneficial in terms of more efficient measurements for solar panel installations and more accurate calculations of solar radiation for residents and commercial energy investors.

关键词： object-based image analysis segmentation LiDAR orthophoto aspect-slope map solar energy photovoltaic potential

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Explorative Study on Mapping Surface Facies of Selected Glaciers from Chandra Basin, Himalaya Using WorldView-2 Data

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REMOTE SENSING 2019年第10期11卷 1207-1207页

作者： Jawak, Shridhar D. Wankhede, Sagar F. Luis, Alvarinho J. Govt India Minist Earth Sci ESSO NCPOR Vasco Da Gama 403804 Goa India Svalbard Sci Ctr SIOS Knowledge Ctr Svalbard Integrated Arctic Earth Observing Syst S POB 156 N-9171 Longyearbyen Svalbard Norway Mangalore Univ Dept Marine Geol Mangalagangothri 574199 Karnataka India

Mapping of surface glacier facies has been a part of several glaciological applications. The study of glacier facies in the Himalayas has gained momentum in the last decade owing to the implications imposed by these facies on the melt characteristics of the glaciers. Some of the most commonly reported surface facies in the Himalayas are snow, ice, ice mixed debris, and debris. The precision of the techniques used to extract glacier facies is of high importance, as the result of many cryospheric studies and economic reforms rely on it. An assessment of a customized semi-automated protocol against conventional and advanced mapping algorithms for mapping glacier surface facies is presented in this study. Customized spectral index ratios (SIRs) are developed for effective extraction of surface facies using thresholding in an object-based environment. This method was then tested on conventional and advanced classification algorithms for an evaluation of the mapping accuracy for five glaciers located in the Himalayas, using very high-resolution WorldView-2 imagery. The results indicate that the object-based image analysis (OBIA) based semi-automated SIR approach achieved a higher average overall accuracy of 87.33% (kappa = 0.85) than the pixel-based image analysis (PBIA) approach. Among the conventional methods, the Maximum Likelihood performed the best, with an overall accuracy of 78.71% (kappa = 0.75). The Constrained Energy Minimization, with an overall accuracy of 68.76% (kappa = 0.63), was the best performer of the advanced algorithms. The advanced methods greatly underperformed in this study. The proposed SIRs show a promise in the mapping of minor features such as crevasses and in the discrimination between ice-mixed debris and debris. We have efficiently mapped surface glacier facies independently of short-wave infrared bands (SWIR). There is a scope for the transferability of the proposed SIRs and their performance in varying scenarios.

关键词： glacier facies object-based image analysis pixel-based image analysis WorldView-2 Himalaya

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A Novel object-based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks

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REMOTE SENSING 2019年第6期11卷 684-684页

作者： Papadomanolaki, Maria Vakalopoulou, Maria Karantzalos, Konstantinos Natl Tech Univ Athens Remote Sensing Lab Zografos 15780 Greece Univ Paris Saclay INRIA Cent Supelec Ctr Vis Numer F-91190 Gif Sur Yvette France

Deep learning architectures have received much attention in recent years demonstrating state-of-the-art performance in several segmentation, classification and other computer vision tasks. Most of these deep networks are based on either convolutional or fully convolutional architectures. In this paper, we propose a novel object-based deep-learning framework for semantic segmentation in very high-resolution satellite data. In particular, we exploit object-based priors integrated into a fully convolutional neural network by incorporating an anisotropic diffusion data preprocessing step and an additional loss term during the training process. Under this constrained framework, the goal is to enforce pixels that belong to the same object to be classified at the same semantic category. We compared thoroughly the novel object-based framework with the currently dominating convolutional and fully convolutional deep networks. In particular, numerous experiments were conducted on the publicly available ISPRS WGII/4 benchmark datasets, namely Vaihingen and Potsdam, for validation and inter-comparison based on a variety of metrics. Quantitatively, experimental results indicate that, overall, the proposed object-based framework slightly outperformed the current state-of-the-art fully convolutional networks by more than 1% in terms of overall accuracy, while intersection over union results are improved for all semantic categories. Qualitatively, man-made classes with more strict geometry such as buildings were the ones that benefit most from our method, especially along object boundaries, highlighting the great potential of the developed approach.

关键词： earth observation satellite data machine learning convolutional neural networks GEOBIA object-based image analysis superpixels anisotropic diffusion

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Spatio-temporal pattern of soil degradation in a Swiss Alpine grassland catchment

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REMOTE SENSING OF ENVIRONMENT 2019年 235卷 111441-000页

作者： Zweifel, Lauren Meusburger, Katrin Alewella, Christine Univ Basel Dept Environm Sci Bernoullistr 30 CH-4056 Basel Switzerland Swiss Fed Res Inst WSL Zurcherstr 111 CH-8903 Birmensdorf Switzerland

Soil degradation on Alpine grasslands is triggered mainly by extreme topography, prevailing climate conditions and land use practices. Suitable monitoring tools are required to assess soil erosion with high temporal and spatial resolution. In this study, we present an unprecedented and comprehensive approach based on objectbased image analysis (OBIA) to map and assess all occurring erosion processes within a catchment (Urseren Valley, Switzerland). Five high-resolution (0.25-0.5 m) orthophotos with RGB spectral information (Swissimage) produced during a 16-yr period were analyzed. Soil erosion sites are classified according to their type (shallow landslide or sites with reduced vegetation cover affected by sheet erosion) or the triggering land use management impacts (haying, trampling) with the Overall Accuracy ranging between 78 and 88% (Kappa 0.65-0.81) for the different years. The area affected by soil erosion increases for all classes during the study period (2000-2016) by a total of 156 +/- 18% (increase consisting of 3% shallow landslides, 5% livestock trails, 46% sheet erosion and 46% management effects). Slopes at lower elevations ( < 1800 m asl) are increasingly affected by livestock trails and sheet erosion caused by trampling and grazing as well as other management practices. For areas located above the agricultural land use, an increase in shallow landslides, as well as sheet erosion, can be observed. This points to climate change as a triggering factor of soil degradation, which has not been identified so far as a factor for soil erosion in the Urseren Valley. While OBIA yields conservative estimations mainly due to limitations of spatial resolutions, the method facilitates a comprehensive overview of the ongoing temporal and spatial development regarding soil degradation within the Urseren Valley.

关键词： Shallow landslides Sheet erosion Orthophotography Time series object-based image analysis Mapping Alps

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Estimating Pinyon and Juniper Cover Across Utah Using NAIP imagery

Estimating Pinyon and Juniper Cover Across Utah Using NAIP I...

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作者： Roundy, Darrell B. Brigham Young University

学位级别：M.Sc.

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 Analyst: TM 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) on 309 subplots 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 method (r = 0.85 for ENVI, 0.83 for Feature Analyst, and 0.83 for eCognition). Results from this study suggest that OBIA techniques may be used to extract P-J tree canopy cover accurately and inexpensively. All software packages accurately evaluated accurately extracted P-J canopy cover from NAIP imagery when imagery was not blurred and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel's oak), which are shrubs with similar spectral values as P-J.

关键词： object-based image analysis canopy cover National Agriculture imagery Program eCognition Feature Analyst ENVI Feature Extraction

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Nearshore Benthic Habitat Mapping based on Multi-Frequency, Multibeam Echosounder Data Using a Combined object-based Approach: A Case Study from the Rowy Site in the Southern Baltic Sea

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REMOTE SENSING 2018年第12期10卷 1983-1983页

作者： Janowski, Lukasz Trzcinska, Karolina Tegowski, Jaroslaw Kruss, Aleksandra Rucinska-Zjadacz, Maria Pocwiardowski, Pawel Univ Gdansk Inst Oceanog Al Marszalka Pilsudskiego 46 PL-81378 Gdynia Poland NORBIT Poland Sp Zoo Al Niepodleglosci 813-815-24 PL-81810 Sopot Poland

Recently, the rapid development of the seabed mapping industry has allowed researchers to collect hydroacoustic data in shallow, nearshore environments. Progress in marine habitat mapping has also helped to distinguish the seafloor areas of varied acoustic properties. As a result of these new developments, we have collected a multi-frequency, multibeam echosounder dataset from the valuable nearshore environment of the southern Baltic Sea using two frequencies: 150 kHz and 400 kHz. Despite its small size, the Rowy area is characterized by diverse habitat conditions and the presence of red algae, unique on the Polish coast of the Baltic Sea. This study focused on the utilization of multibeam bathymetry and multi-frequency backscatter data to create reliable maps of the seafloor. Our approach consisted of the extraction of 70 secondary features of bathymetric and backscatter data, including statistic and textural attributes of different scales. based on ground-truth samples, we have identified six habitat classes and selected the most relevant features of the bathymetric and backscatter data. Additionally, five types of image processing pixel-based and object-based classifiers were tested. We also evaluated the performance of algorithms using an accuracy assessment based on the validation subset of the ground-truth samples. Our best results reached 93% overall accuracy and a kappa coefficient of 0.90, confirming that nearshore seabed habitats can be accurately distinguished based on multi-frequency, multibeam echosounder measurements. Our predictive habitat mapping of shallow euphotic zones creates a new scientific perspective and provides relevant data for the management of natural resources. object-based approaches previously used in various environments and areas suggest that methodology presented in this study may be scalable.

关键词： habitat mapping multibeam echosounder multi-frequency image processing feature selection object-based image analysis

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Integrating multi-sensor remote sensing and species distribution modeling to map the spread of emerging forest disease and tree mortality

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REMOTE SENSING OF ENVIRONMENT 2019年 231卷 111238-000页

作者： He, Yinan Chen, Gang Potter, Christopher Meentemeyer, Ross K. Univ N Carolina Dept Geog & Earth Sci LRSEC 9201 Univ City Blvd Charlotte NC 28223 USA NASA Ames Res Ctr Moffett Field CA 94035 USA North Carolina State Univ Ctr Geospatial Analyt 3120 Jordan Hall Raleigh NC 27695 USA North Carolina State Univ Dept Forestry & Environm Resources 3120 Jordan Hall Raleigh NC 27695 USA

Forest ecosystems have been increasingly affected by a variety of disturbances, including emerging infectious diseases (ElDs), causing extensive tree mortality in the Western United States. Especially over the past decade, EID outbreaks occurred more frequently and severely in forest landscapes, which have killed large numbers of trees. While tree mortality is observable from remote sensing, its symptom may be associated with both disease and non-disease disturbances (e.g., wildfire and drought). Species distribution modeling is widely used to understand species spatial preferences for certain habitat conditions, which may constrain uncertain remote sensing approaches due to limited spatial and spectral resolution. In this study, we integrated multi-sensor remote sensing and species distribution modeling to map disease-caused tree mortality in a forested area of 80,000 ha from 2005 to 2016. We selected sudden oak death (caused by pathogen P. ramorum) as a case study of a rapidly spreading emerging infectious disease, which has killed millions of oak (Quercus spp.) and tanoak (Lithocarpus densifiorus) in California over the past decades. To balance the needs for fine-scale monitoring of disease distribution patterns and satisfactory coverage at broad scales, our method applied spectral unmixing to extract sub-pixel disease presence using yearly Landsat time series. The results were improved by employing the probability of disease infection generated from a species distribution model. We calibrated and validated the method with image samples from high-spatial resolution NAIP (National Agriculture imagery Program), and hyperspectral AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensors, Google Earth (R) imagery, and field observations. The findings reveal an annual sudden oak death infection rate of 7% from 2005 to 2016, with overall mapping accuracies ranging from 76% to 83%. The integration of multi-sensor remote sensing and species distribution modeling co

关键词： Multi-sensor remote sensing Species distribution model Emerging infectious disease (EID) Spectral unmixing Landscape epidemiology object-based image analysis Sudden oak death

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