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

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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A supervised approach for simultaneous segmentation and classification of remote sensing images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 142卷 162-173页

作者： Zanotta, Daniel Capella Zortea, Maciel Ferreira, Matheus Pinheiro Natl Inst Sci Educ & Technol R Engn Alfredo Huch 475 BR-96201460 Rio Grande RS Brazil IBM Res Ave Pasteur 138-146 BR-22290240 Rio De Janeiro RJ Brazil Natl Inst Space Res INPE Remote Sensing Div BR-12227010 Sao Jose Dos Campos SP Brazil

object-based image classification is recognized as one of the best strategies to analyze high spatial resolution remote sensing images. This process includes defining scale parameters to form regions sharing similar characteristics such as color, texture, or shape. Traditionally, in an object-based supervised classification setting the image is classified only after the segmentation process is completed. However, when the imaged objects on the ground are heterogeneous and of different sizes, some resulting segments can be appropriate for classification while others are over or under-segmented. This may cause partial failure of the subsequent classification. In this paper, we introduce a simultaneous approach based on the interception of the segmentation stage by provisional classification of under-growing segments. Our proposal is to optimize the classification process by iteratively updating the labels of previously generated regions only if the estimated posterior probabilities of the winning classes in the new segments increase. Experiments with three multispectral datasets acquired by Landsat-5 TM, QuickBird-II, and WorldView-3 in rural and urban areas compare traditional object-based approach based on region growing with the proposed method using well-established classifiers. Our results show that the proposed method becomes much less sensitive to the choice of segmentation parameters and reaches similar, or even better, classification accuracies.

关键词： object-based image analysis Segmentation Supervised classification Multispectral imaging

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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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Modelling the Spectral Uncertainty of Geographic Features in High-Resolution Remote Sensing images: Semi-Supervising and Weighted Interval Type-2 Fuzzy C-Means Clustering

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REMOTE SENSING 2019年第15期11卷 1750-1750页

作者： Guo, Jifa Du, Shihong Huo, Hongyuan Du, Shouji Zhang, Xiuyuan Tianjin Normal Univ Tianjin Key Lab ofWater Resources & Environm Sch Geog & Environm Sci Tianjin 300387 Peoples R China Peking Univ Inst Remote Sensing & GIS Beijing 100871 Peoples R China Beijing Univ Technol Coll Architecture & Civil Engn Beijing 100124 Peoples R China

The spectral uncertainty refers to the diversity and variations of spectral characteristics within a single geographic object or across different objects of the same class. Usually, existing methods represent the spectral characteristics as precise single-valued curves. Thus, the spectral variations cannot be modeled, which further restricts the analysis and classification performance of remote sensing images. On the other hand, unsupervised methods have poor performance in classification and modeling uncertainty, while supervised methods need a large number of samples with high quality. Fuzzy semi-supervised clustering (FSSC) methods achieve a high accuracy with limited labelled samples. Thus, currently, FSSC methods attract more and more attention. This paper proposes a novel method to model the spectral uncertainty for very-high-resolution (VHR) images based on interval type-2 fuzzy sets (IT2 FSs), namely the hierarchical semi-supervising and weighted interval type-2 fuzzy c-means for objects (hierarchical SSW-IT2FCM-O) clustering method. In this method, the VHR image is segmented into image objects to reduce spectral uncertainty within objects. Spectral values, spectral indices and textures were weighted for object-based image classification. To further reduce spectral uncertainty across different objects of the same class, the spectral characteristics of land cover types were represented as banded curves with certain widths instead of precise single-valued spectral curves. The experimental results show that the banded spectral curves produced by the hierarchical SSW-IT2FCM-O can effectively model the spectral uncertainty of geographic objects. From the perspective of classification, four typical validity indices along with the confusion matrix and kappa coefficient were used to test the effectiveness of the hierarchical SSW-IT2FCM-O method, and these indices show that the presented method SSW-IT2FCM-O has greater classification accuracy than the existing FSSC m

关键词： Fuzzy semi-supervised clustering fuzzy classification high-resolution remote sensing image object-based image analysis spectral uncertainty

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A spatiotemporal cube model for analyzing satellite image time series: Application to land-cover mapping and change detection

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REMOTE SENSING OF ENVIRONMENT 2019年 231卷 111212-000页

作者： Xi, Wenqiang Du, Shihong Wang, Yi-Chen Zhang, Xiuyuan Peking Univ Inst Remote Sensing & GIS Beijing 100871 Peoples R China Natl Univ Singapore Dept Geog Singapore 117570 Singapore

Satellite image time series (SITS) data have been widely used in resource investigations and environmental monitoring. However, three characteristics of geographic objects (i.e., spatiotemporal heterogeneity, spatio-temporal correlation and scale characteristics) pose great challenges to SITS analysis;while, existing studies ignore these characteristics, leading to unsatisfactory SITS analysis results including land-cover classification and change detection. Aiming at integratively measuring and expressing these three characteristics, this study proposes a spatiotemporal cube model and a spatiotemporal multi-resolution segmentation (ST-MRS) method for analyzing SITS. First, the novel spatiotemporal model, ST-cube, is introduced to reduce the spectral heterogeneity in spatial and temporal domains. Both spatial and temporal heterogeneities are considered and defined while constructing the spatiotemporal segmentation model. Second, the ST-MRS method is proposed by extending the existing mono-temporal MRS method into spatiotemporal domain to segment SITS into relatively homogeneous ST-cubes. Third, a novel unsupervised method is developed to evaluate the segmentation results of the ST-MRS method. In experiments, we apply this method to Shunyi District in Beijing, China, generate ST-cubes at different spatiotemporal scales, and compare our method with existing ones for SITS classification. Furthermore, the intra-annual seasonal changes of land covers are detected based on ST-cubes classification results. The results indicate the effectiveness and superiority of the ST-cubes in representing geographic objects and analyzing spatiotemporal characteristics over existing pixel- and object-based methods, contributing to a wider range of applications and analysis of SITS.

关键词： Satellite image time series object-based image analysis Multiresolution segmentation Land-cover mapping Change detection

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Photovoltaic panel extraction from very high-resolution aerial imagery using region-line primitive association analysis and template matching

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 141卷 100-111页

作者： Wang, Min Cui, Qi Sun, Yujie Wang, Qiao Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China Satellite Environm Applicat Ctr Minist Environm Protect Beijing 100029 Peoples R China

In object-based image analysis (OBIA), object classification performance is jointly determined by image segmentation, sample or rule setting, and classifiers. Typically, as a crucial step to obtain object primitives, image segmentation quality significantly influences subsequent feature extraction and analyses. By contrast, template matching extracts specific objects from images and prevents shape defects caused by image segmentation. However, creating or editing templates is tedious and sometimes results in incomplete or inaccurate templates. In this study, we combine OBIA and template matching techniques to address these problems and aim for accurate photovoltaic panel (PVP) extraction from very high resolution (VHR) aerial imagery. The proposed method is based on the previously proposed region-line primitive association framework, in which complementary information between region (segment) and line (straight line) primitives is utilized to achieve a more powerful performance than routine OBIA. Several novel concepts, including the mutual fitting ratio and best-fitting template based on region-line primitive association analyses, are proposed. Automatic template generation and matching method for PVP extraction from VHR imagery are designed for concept and model validation. Results show that the proposed method can successfully extract PVPs without any user-specified matching template or training sample. High user independency and accuracy are the main characteristics of the proposed method in comparison with routine OBIA and template matching techniques. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Photovoltaic panel object-based image analysis Region-line primitive association framework Template matching Information extraction High-resolution imagery

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Calculation of the optimal segmentation scale in object-based multiresolution segmentation based on the scene complexity of high-resolution remote sensing images

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JOURNAL OF APPLIED REMOTE SENSING 2018年第2期12卷

作者： Feng, Tianjing Ma, Hairong Cheng, Xinwen Zhang, Hongping China Univ Geosci Fac Informat Engn Wuhan Hubei Peoples R China Hubei Acad Agr Sci Wuhan Hubei Peoples R China

The quality of multiresolution segmentation directly influences the accuracy of high-resolution remote sensing image classification using object-oriented analysis technology. However, a perfect segmentation scale optimization method has not yet been developed. Using the fact that the optimal segmentation scale of high-resolution remote sensing images is closely related to the complexity of the objects on the image, we propose an approach for calculating the optimal segmentation scale based on the scene complexity of an image. First, we calculate the scene complexity of high-resolution remote sensing images using Watson's vision model. Then, we analyze the relationship between the image scene complexity and the optimal segmentation scale based on the model calculation. Optimal segmentation scales are found to be related to the scene complexity of high-resolution remote sensing images by an exponential function, allowing direct calculation of the optimal segmentation scale based on the fitted formulas and the image scene complexity. Finally, we propose a multilevel segmentation strategy to increase the object targeting in the optimal segmentation scale. The optimal segmentation scale calculation method proposed here is simple to perform and has a broad range of potential applications. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： object-based image analysis high-resolution remote sensing images optimal segmentation scale scene complexity

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object-based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2

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REMOTE SENSING 2019年第10期11卷 1257-1257页

作者： Csillik, Ovidiu Belgiu, Mariana Asner, Gregory P. Kelly, Maggi Arizona State Univ Ctr Global Discovery & Conservat Sci Tempe AZ 85287 USA Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA Univ Calif Davis Div Agr & Nat Resources Davis CA 95616 USA

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.

关键词： agricultural mapping DTW eCognition land use land cover mapping object-based image analysis satellite image time series

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Estimation of crop plant density at early mixed growth stages using UAV imagery

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PLANT METHODS 2019年第1 p64-64期15卷 64-64页

作者： Koh, Joshua C. O. Hayden, Matthew Daetwyler, Hans Kant, Surya Agr Victoria Grains Innovat Pk110 Natimuk Rd Horsham Vic 3400 Australia Agr Victoria AgriBio Ctr AgriBiosci 5 Ring Rd Bundoora Vic 3083 Australia La Trobe Univ Sch Appl Syst Biol Bundoora Vic 3086 Australia

BackgroundUnmanned aerial vehicles (UAVs) equipped with lightweight sensors are making a significant impact in field-based crop phenotyping. UAV platforms have been successfully deployed to acquire phenotypic data in a precise and efficient manner that would otherwise be time-consuming and costly to acquire when undertaken through manual assessment. One example is the estimation of plant density (or counts) in field experiments. Challenges posed to digital plant counting models are heterogenous germination and mixed growth stages that are present in field experiments with diverse genotypes. Here we describe, using safflower as an example, a method based on template matching for seedling count estimation at early mixed growth stages using UAV *** object-based image analysis algorithm based on template matching was developed for safflower seedling detection at early mixed growth stages in field experiments conducted in 2017 and 2018. Seedling detection was successful when tested using a grouped template type with 10 subgroups representing safflower at 2-4 leaves growth stage in 100 selected plots from the 2017 field experiment. The algorithm was validated for 300 plots each from the 2017 and 2018 field experiments, where estimated seedling counts correlated closely with manual counting;R-2=0.87, MAE=8.18, RSME=9.38 for 2017 field experiment and R-2=0.86, MAE=9.16, RSME=10.51 for *** method for safflower seedling count at early mixed growth stages using UAV imagery was developed and validated. The model performed well across heterogenous growth stages and has the potential to be used for plant density estimation across various crop species.

关键词： object-based image analysis Plant phenotyping Safflower Seedling count Unmanned aerial vehicle

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Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes

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REMOTE SENSING OF ENVIRONMENT 2019年 230卷 111207-000页

作者： Rasanen, Aleksi Virtanen, Tarmo Univ Helsinki Helsinki Inst Sustainabil Sci HELSUS Fac Biol & Environm Sci Ecosyst & Environm Res Programme POB 65 FI-00014 Helsinki Finland Norwegian Univ Sci & Technol Dept Geog NO-7491 Trondheim Norway

It has been argued that even centimeter-level resolution is needed for mapping vegetation patterns in spatially heterogeneous landscapes such as northern peatlands. However, there are few systematic tests for determining what kind of spatial resolution and data combinations are needed and what the differences in mapping accuracy are when different datasets are omitted or included. We conducted 78 different object-based supervised random forest classifications on a patterned fen and its surroundings in Kaamanen, northern Finland, using remotely sensed optical imagery, topography, and vegetation height datasets from different platforms (unmanned aerial vehicle (UAV), aerial, satellite) with spatial resolution ranging from 5 cm to 3m. We compared differences in classification performance when we altered (1) classification and segmentation input data and features calculated from the data, or (2) the segmentation scale. We constructed training data with the help of transect-based field sampling and UAV imagery and tested classification accuracy using 412 field-surveyed vegetation plots. The most accurate classifications (75.7% overall accuracy) were obtained when we segmented a 5 cm resolution UAV image with a small segmentation scale and calculated features from all datasets. Classification accuracy was 2.2 percentage points (pp) lower with the most accurate aerial image (50 cm resolution) based classification, and 7.6 pp and 11.9pp lower with the most accurate WorldView-2 (2 m resolution) and PlanetScope (3 m resolution) satellite image based classifications respectively. Classification accuracies were low (46.7-56.0%) when we used only spectral data from one dataset. The inclusion of gray-level co-occurrence matrix textural features increased classification accuracy by 0.4-12.1pp and inclusion of multiple datasets by 8.2-25.0pp. Segmentation scale had a minor effect on classification accuracy (2.5-7.3pp difference between the finest and coarsest segmentation scale);ho

关键词： Arctic Data fusion Drone Land cover classification Lidar Northern boreal object-based image analysis Peatland UAS Ultra-high spatial resolution Very-high spatial resolution

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