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Almeida, Luís, Rafael Almar, Erwin Bergsma, et al. "Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery." Remote Sensing 11, no. 5 (2019): 590. http://dx.doi.org/10.3390/rs11050590.
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High spatial resolution coastal Digital Elevation Models (DEMs) are crucial to assess coastal vulnerability and hazards such as beach erosion, sedimentation, or inundation due to storm surges and sea level rise. This paper explores the possibility to use high spatial-resolution Pleiades (pixel size = 0.7 m) stereoscopic satellite imagery to retrieve a DEM on sandy coastline. A 40-km coastal stretch in the Southwest of France was selected as a pilot-site to compare topographic measurements obtained from Pleiades satellite imagery, Real Time Kinematic GPS (RTK-GPS) and airborne Light Detection and Ranging System (LiDAR). The derived 2-m Pleiades DEM shows an overall good agreement with concurrent methods (RTK-GPS and LiDAR; correlation coefficient of 0.9), with a vertical Root Mean Squared Error (RMS error) that ranges from 0.35 to 0.48 m, after absolute coregistration to the LiDAR dataset. The largest errors (RMS error > 0.5 m) occurred in the steep dune faces, particularly at shadowed areas. This work shows that DEMs derived from sub-meter satellite imagery capture local morphological features (e.g., berm or dune shape) on a sandy beach, over a large spatial domain.
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Tian, J., X. Zhuo, X. Yuan, C. Henry, P. d’Angelo, and T. Krauss. "APPLICATION ORIENTED QUALITY EVALUATION OF GAOFEN-7 OPTICAL STEREO SATELLITE IMAGERY." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2022 (May 17, 2022): 145–52. http://dx.doi.org/10.5194/isprs-annals-v-1-2022-145-2022.
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Abstract. GaoFen-7 (GF-7) satellite mission is further expanding the very high resolution 3D mapping application. Carrying the first civilian Chinese sub-meter resolution stereo satellite sensors, GF-7 satellite was launched on November 7, 2019. With 0.65 meter resolution on backward view and 0.8 meter resolution forward view, GF-7 has been designed to meet the demand of natural resource monitoring, land surveying, and other mapping applications in China. The use of GF-7 for 3D city reconstruction is unfortunately restricted by the fixed large stereo view angle of forward and backward cameras with +26 and −5 degrees respectively which is not optimal for dense stereo matching in urban regions. In this paper we intensively evaluate the quality of the GF-7 datasets by performing a series of urban monitoring applications, including road detection, building extraction and 3D reconstruction. In addition, we propose a 3D reconstruction workflow which uses the land cover classification result to refine the stereo matching result. Six sub-urban regions are selected from the available datasets in the middle of Germany. The results show that basic elements in urban scenes like buildings and roads could be detected from GF-7 datasets with high accuracy. With the proposed workflow, a 3D city model with a visually observed good quality can be delivered.
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Manos, Elias, Chandi Witharana, Mahendra Rajitha Udawalpola, Amit Hasan, and Anna K. Liljedahl. "Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery." Remote Sensing 14, no. 11 (2022): 2719. http://dx.doi.org/10.3390/rs14112719.
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Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is currently at major risk of structural failure. Risk assessment frameworks that attempt to study this issue assume that precise information on the location and extent of infrastructure is known. However, complete, high-quality, uniform geospatial datasets of built infrastructure that are readily available for such scientific studies are lacking. While imagery-enabled mapping can fill this knowledge gap, the small size of individual structures and vast geographical extent of the Arctic necessitate large volumes of very high spatial resolution remote sensing imagery. Transforming this ‘big’ imagery data into ‘science-ready’ information demands highly automated image analysis pipelines driven by advanced computer vision algorithms. Despite this, previous fine resolution studies have been limited to manual digitization of features on locally confined scales. Therefore, this exploratory study serves as the first investigation into fully automated analysis of sub-meter spatial resolution satellite imagery for automated detection of Arctic built infrastructure. We tasked the U-Net, a deep learning-based semantic segmentation model, with classifying different infrastructure types (residential, commercial, public, and industrial buildings, as well as roads) from commercial satellite imagery of Utqiagvik and Prudhoe Bay, Alaska. We also conducted a systematic experiment to understand how image augmentation can impact model performance when labeled training data is limited. When optimal augmentation methods were applied, the U-Net achieved an average F1 score of 0.83. Overall, our experimental findings show that the U-Net-based workflow is a promising method for automated Arctic built infrastructure detection that, combined with existing optimized workflows, such as MAPLE, could be expanded to map a multitude of infrastructure types spanning the pan-Arctic.
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Xu, Huiyao, Jia Song, and Yunqiang Zhu. "Evaluation and Comparison of Semantic Segmentation Networks for Rice Identification Based on Sentinel-2 Imagery." Remote Sensing 15, no. 6 (2023): 1499. http://dx.doi.org/10.3390/rs15061499.
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Efficient and accurate rice identification based on high spatial and temporal resolution remote sensing imagery is essential for achieving precision agriculture and ensuring food security. Semantic segmentation networks in deep learning are an effective solution for crop identification, and they are mainly based on two architectures: the commonly used convolutional neural network (CNN) architecture and the novel Vision Transformer architecture. Research on crop identification from remote sensing imagery using Vision Transformer has only emerged in recent times, mostly in sub-meter resolution or even higher resolution imagery. Sub-meter resolution images are not suitable for large scale crop identification as they are difficult to obtain. Therefore, studying and analyzing the differences between Vision Transformer and CNN in crop identification in the meter resolution images can validate the generalizability of Vision Transformer and provide new ideas for model selection in crop identification research at large scale. This paper compares the performance of two representative CNN networks (U-Net and DeepLab v3) and a novel Vision Transformer network (Swin Transformer) on rice identification in Sentinel-2 of 10 m resolution. The results show that the three networks have different characteristics: (1) Swin Transformer has the highest rice identification accuracy and good farmland boundary segmentation ability. Although Swin Transformer has the largest number of model parameters, the training time is shorter than DeepLab v3, indicating that Swin Transformer has good computational efficiency. (2) DeepLab v3 also has good accuracy in rice identification. However, the boundaries of the rice fields identified by DeepLab v3 tend to shift towards the upper left corner. (3) U-Net takes the shortest time for both training and prediction and is able to segment the farmland boundaries accurately for correctly identified rice fields. However, U-Net’s accuracy of rice identification is lowest, and rice is easily confused with soybean, corn, sweet potato and cotton in the prediction. The results reveal that the Vision Transformer network has great potential for identifying crops at the country or even global scale.
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Yalcin, I., S. Kocaman, S. Saunier, and C. Albinet. "RADIOMETRIC QUALITY ASSESSMENT FOR MAXAR HD IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 797–804. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-797-2021.
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Abstract. The requirement for very high-resolution satellite imagery by different applications has been increasing continuously. Several commercial and government-supported missions provide sub-meter spatial resolutions from optical sensors aboard Earth Observation (EO) satellites. The MAXAR satellite constellation acquires images with up to 30 cm Ground Sampling Distances (GSDs); and the High-Definition (HD) image production technology developed by MAXAR doubles the resolution by using artificial intelligence methods. Although the spatial resolution is one of the most important image quality metrics, several other factors indicated by diverse radiometric and geometric characteristics may circumscribe the usability of data in different projects. As part of mandatory activities of European Space Agency (ESA), Earthnet Programme provides a framework for integrating Third-Party Missions into the overall EO strategy and promotes the international use of the data. The Earthnet Data Assessment Pilot (EDAP) project aims at assessing the quality and the suitability of TPMs, and provides a communication platform between mission providers to ensure the coherence of the systems. In this study, the radiometric quality of the MAXAR HD products was evaluated within the EDAP project framework by using several General Image-Quality Equation (GIQE) metrics, visual inspections, and comparative assessments with orthophotos obtained from an Unmanned Aerial Vehicle (UAV) platform and with the original (non-HD) orthophotos with 30 cm resolutions. The results show that the spatial resolution improvements are observable in urban areas, where sharp edges are present. However, blurring and color noise patterns also occured in the HD images.
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Farella, E. M., F. Remondino, C. Cahalane, et al. "GEOMETRIC PROCESSING OF VERY HIGH-RESOLUTION SATELLITE IMAGERY: QUALITY ASSESSMENT FOR 3D MAPPING NEEDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W3-2023 (October 19, 2023): 47–54. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-47-2023.
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Abstract. In recent decades, the geospatial domain has benefitted from technological advances in sensors, methodologies, and processing tools to expand capabilities in mapping applications. Airborne techniques (LiDAR and aerial photogrammetry) generally provide most of the data used for this purpose. However, despite the relevant accuracy of these technologies and the high spatial resolution of airborne data, updates are not sufficiently regular due to significant flight costs and logistics. New possibilities to fill this information gap have emerged with the advent of Very High Resolution (VHR) optical satellite images in the early 2000s. In addition to the high temporal resolution of the cost-effective datasets and their sub-meter geometric resolutions, the synoptic coverage is an unprecedented opportunity for mapping remote areas, multi-temporal analyses, updating datasets and disaster management. For all these reasons, VHR satellite imagery is clearly a relevant study for National Mapping and Cadastral Agencies (NMCAs). This work, supported by EuroSDR, summarises a series of experimental analyses carried out over diverse landscapes to explore the potential of VHR imagery for large-scale mapping.
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Udawalpola, M., A. Hasan, A. K. Liljedahl, A. Soliman, and C. Witharana. "OPERATIONAL-SCALE GEOAI FOR PAN-ARCTIC PERMAFROST FEATURE DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 175–80. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-175-2021.
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Abstract. Regional extent and spatiotemporal dynamics of Arctic permafrost disturbances remain poorly quantified. High spatial resolution commercial satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. The imagery is still largely underutilized, and value-added Arctic science products are rare. Knowledge discovery through artificial intelligence (AI), big imagery, high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of petabyte-scale imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. In addition to semantic complexities, multitude factors that are inherent to sub-meter resolution satellite imagery, such as file size, dimensions, spectral channels, overlaps, spatial references, and imaging conditions challenge the direct translation of AI-based approaches from computer vision applications. Memory limitations of Graphical Processing Units necessitates the partitioning of an input satellite imagery into manageable sub-arrays, followed by parallel predictions and post-processing to reconstruct the results corresponding to input image dimensions and spatial reference. We have developed a novel high performance image analysis framework –Mapping application for Arctic Permafrost Land Environment (MAPLE) that enables the integration of operational-scale GeoAI capabilities into Arctic science applications. We have designed the MAPLE workflow to become interoperable across HPC architectures while utilizing the optimal use of computing resources.
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Höschle, Caroline, Hannah C. Cubaynes, Penny J. Clarke, Grant Humphries, and Alex Borowicz. "The Potential of Satellite Imagery for Surveying Whales." Sensors 21, no. 3 (2021): 963. http://dx.doi.org/10.3390/s21030963.
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The emergence of very high-resolution (VHR) satellite imagery (less than 1 m spatial resolution) is creating new opportunities within the fields of ecology and conservation biology. The advancement of sub-meter resolution imagery has provided greater confidence in the detection and identification of features on the ground, broadening the realm of possible research questions. To date, VHR imagery studies have largely focused on terrestrial environments; however, there has been incremental progress in the last two decades for using this technology to detect cetaceans. With advances in computational power and sensor resolution, the feasibility of broad-scale VHR ocean surveys using VHR satellite imagery with automated detection and classification processes has increased. Initial attempts at automated surveys are showing promising results, but further development is necessary to ensure reliability. Here we discuss the future directions in which VHR satellite imagery might be used to address urgent questions in whale conservation. We highlight the current challenges to automated detection and to extending the use of this technology to all oceans and various whale species. To achieve basin-scale marine surveys, currently not feasible with any traditional surveying methods (including boat-based and aerial surveys), future research requires a collaborative effort between biology, computation science, and engineering to overcome the present challenges to this platform’s use.
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Wohlfeil, J., H. Hirschmüller, B. Piltz, A. Börner, and M. Suppa. "FULLY AUTOMATED GENERATION OF ACCURATE DIGITAL SURFACE MODELS WITH SUB-METER RESOLUTION FROM SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B3 (July 23, 2012): 75–80. http://dx.doi.org/10.5194/isprsarchives-xxxix-b3-75-2012.
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Belart, Joaquín M. C., Etienne Berthier, Eyjólfur Magnússon, et al. "Winter mass balance of Drangajökull ice cap (NW Iceland) derived from satellite sub-meter stereo images." Cryosphere 11, no. 3 (2017): 1501–17. http://dx.doi.org/10.5194/tc-11-1501-2017.
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Abstract. Sub-meter resolution, stereoscopic satellite images allow for the generation of accurate and high-resolution digital elevation models (DEMs) over glaciers and ice caps. Here, repeated stereo images of Drangajökull ice cap (NW Iceland) from Pléiades and WorldView2 (WV2) are combined with in situ estimates of snow density and densification of firn and fresh snow to provide the first estimates of the glacier-wide geodetic winter mass balance obtained from satellite imagery. Statistics in snow- and ice-free areas reveal similar vertical relative accuracy (< 0.5 m) with and without ground control points (GCPs), demonstrating the capability for measuring seasonal snow accumulation. The calculated winter (14 October 2014 to 22 May 2015) mass balance of Drangajökull was 3.33 ± 0.23 m w.e. (meter water equivalent), with ∼ 60 % of the accumulation occurring by February, which is in good agreement with nearby ground observations. On average, the repeated DEMs yield 22 % less elevation change than the length of eight winter snow cores due to (1) the time difference between in situ and satellite observations, (2) firn densification and (3) elevation changes due to ice dynamics. The contributions of these three factors were of similar magnitude. This study demonstrates that seasonal geodetic mass balance can, in many areas, be estimated from sub-meter resolution satellite stereo images.
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Gong, K., and D. Fritsch. "A DETAILED STUDY ABOUT DIGITAL SURFACE MODEL GENERATION USING HIGH RESOLUTION SATELLITE STEREO IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 1, 2016): 69–76. http://dx.doi.org/10.5194/isprsannals-iii-1-69-2016.
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Photogrammetry is currently in a process of renaissance, caused by the development of dense stereo matching algorithms to provide very dense Digital Surface Models (DSMs). Moreover, satellite sensors have improved to provide sub-meter or even better Ground Sampling Distances (GSD) in recent years. Therefore, the generation of DSM from spaceborne stereo imagery becomes a vivid research area. This paper presents a comprehensive study about the DSM generation of high resolution satellite data and proposes several methods to implement the approach. The bias-compensated Rational Polynomial Coefficients (RPCs) Bundle Block Adjustment is applied to image orientation and the rectification of stereo scenes is realized based on the Project-Trajectory-Based Epipolarity (PTE) Model. Very dense DSMs are generated from WorldView-2 satellite stereo imagery using the dense image matching module of the C/C++ library LibTsgm. We carry out various tests to evaluate the quality of generated DSMs regarding robustness and precision. The results have verified that the presented pipeline of DSM generation from high resolution satellite imagery is applicable, reliable and very promising.
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Gong, K., and D. Fritsch. "A DETAILED STUDY ABOUT DIGITAL SURFACE MODEL GENERATION USING HIGH RESOLUTION SATELLITE STEREO IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 1, 2016): 69–76. http://dx.doi.org/10.5194/isprs-annals-iii-1-69-2016.
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Photogrammetry is currently in a process of renaissance, caused by the development of dense stereo matching algorithms to provide very dense Digital Surface Models (DSMs). Moreover, satellite sensors have improved to provide sub-meter or even better Ground Sampling Distances (GSD) in recent years. Therefore, the generation of DSM from spaceborne stereo imagery becomes a vivid research area. This paper presents a comprehensive study about the DSM generation of high resolution satellite data and proposes several methods to implement the approach. The bias-compensated Rational Polynomial Coefficients (RPCs) Bundle Block Adjustment is applied to image orientation and the rectification of stereo scenes is realized based on the Project-Trajectory-Based Epipolarity (PTE) Model. Very dense DSMs are generated from WorldView-2 satellite stereo imagery using the dense image matching module of the C/C++ library LibTsgm. We carry out various tests to evaluate the quality of generated DSMs regarding robustness and precision. The results have verified that the presented pipeline of DSM generation from high resolution satellite imagery is applicable, reliable and very promising.
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Martinuzzi, Sebastián, Olga M. Ramos-González, Tischa A. Muñoz-Erickson, Dexter H. Locke, Ariel E. Lugo, and Volker C. Radeloff. "Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery." Ecological Applications 28, no. 3 (2018): 681–93. http://dx.doi.org/10.1002/eap.1673.
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Soroush, Mehrnoush, Alireza Mehrtash, Emad Khazraee, and Jason A. Ur. "Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq." Remote Sensing 12, no. 3 (2020): 500. http://dx.doi.org/10.3390/rs12030500.
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In this paper, we report the results of our work on automated detection of qanat shafts on the Cold War-era CORONA Satellite Imagery. The increasing quantity of air and space-borne imagery available to archaeologists and the advances in computational science have created an emerging interest in automated archaeological detection. Traditional pattern recognition methods proved to have limited applicability for archaeological prospection, for a variety of reasons, including a high rate of false positives. Since 2012, however, a breakthrough has been made in the field of image recognition through deep learning. We have tested the application of deep convolutional neural networks (CNNs) for automated remote sensing detection of archaeological features. Our case study is the qanat systems of the Erbil Plain in the Kurdistan Region of Iraq. The signature of the underground qanat systems on the remote sensing data are the semi-circular openings of their vertical shafts. We choose to focus on qanat shafts because they are promising targets for pattern recognition and because the richness and the extent of the qanat landscapes cannot be properly captured across vast territories without automated techniques. Our project is the first effort to use automated techniques on historic satellite imagery that takes advantage of neither the spectral imagery resolution nor very high (sub-meter) spatial resolution.
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Li, S., and H. Tang. "BUILDING DAMAGE EXTRACTION TRIGGERED BY EARTHQUAKE USING THE UAV IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 929–36. http://dx.doi.org/10.5194/isprs-archives-xlii-3-929-2018.
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When extracting building damage information, we can only determine whether the building is collapsed using the post-earthquake satellite images. Even the satellite images have the sub-meter resolution, the identification of slightly damaged buildings is still a challenge. As the complementary data to satellite images, the UAV images have unique advantages, such as stronger flexibility and higher resolution. In this paper, according to the spectral feature of UAV images and the morphological feature of the reconstructed point clouds, the building damage was classified into four levels: basically intact buildings, slightly damaged buildings, partially collapsed buildings and totally collapsed buildings, and give the rules of damage grades. In particular, the slightly damaged buildings are determined using the detected roof-holes. In order to verify the approach, we conduct experimental simulations in the cases of Wenchuan and Ya’an earthquakes. By analyzing the post-earthquake UAV images of the two earthquakes, the building damage was classified into four levels, and the quantitative statistics of the damaged buildings is given in the experiments.
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d'Angelo, P., G. Kuschk, and P. Reinartz. "Evaluation of Skybox Video and Still Image products." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 95–99. http://dx.doi.org/10.5194/isprsarchives-xl-1-95-2014.
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The SkySat-1 satellite lauched by Skybox Imaging on November 21 in 2013 opens a new chapter in civilian earth observation as it is the first civilian satellite to image a target in high definition panchromatic video for up to 90 seconds. The small satellite with a mass of 100 kg carries a telescope with 3 frame sensors. Two products are available: Panchromatic video with a resolution of around 1 meter and a frame size of 2560 &times; 1080 pixels at 30 frames per second. Additionally, the satellite can collect still imagery with a swath of 8 km in the panchromatic band, and multispectral images with 4 bands. Using super-resolution techniques, sub-meter accuracy is reached for the still imagery. The paper provides an overview of the satellite design and imaging products. The still imagery product consists of 3 stripes of frame images with a footprint of approximately 2.6 &times; 1.1 km. Using bundle block adjustment, the frames are registered, and their accuracy is evaluated. Image quality of the panchromatic, multispectral and pansharpened products are evaluated. The video product used in this evaluation consists of a 60 second gazing acquisition of Las Vegas. A DSM is generated by dense stereo matching. Multiple techniques such as pairwise matching or multi image matching are used and compared. As no ground truth height reference model is availble to the authors, comparisons on flat surface and compare differently matched DSMs are performed. Additionally, visual inspection of DSM and DSM profiles show a detailed reconstruction of small features and large skyscrapers.
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Tang, Hongzhao, Junfeng Xie, Xinming Tang, Wei Chen, and Qi Li. "On-Orbit Radiometric Performance of GF-7 Satellite Multispectral Imagery." Remote Sensing 14, no. 4 (2022): 886. http://dx.doi.org/10.3390/rs14040886.
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China’s first civilian, sub-meter, high-resolution stereo mapping satellite, GF-7, launched on 3 November 2019. Radiometric characterization of GF-7 multispectral imagery has been performed in this study. A relative radiometric accuracy evaluation of the GF-7 multispectral imagery was performed using several large uniform scenes, and the results showed that the accuracy is better than 2%. The absolute radiometric evaluation of the GF-7 satellite sensor was conducted at the Baotou and Dunhuang calibration sites, using the reflectance-based vicarious approach. The synchronous measurements of surface reflectance and atmospheric parameters were collected as the input for the radiative transfer model. The official radiometrically calibrated coefficient of the GF-7 multispectral imagery was evaluated with the predicted top-of-atmosphere (TOA) radiance from the radiative transfer model. The results indicated that the absolute radiometric accuracy of GF-7 multispectral imagery is better than 5%. In order to monitor the radiometric stability of the GF-7 satellite multispectral sensor, a relative and absolute radiometric accuracy assessment campaign should be performed several times a year.
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Boldt, M., A. Thiele, K. Schulz, and S. Hinz. "SAR Image Segmentation Using Morphological Attribute Profiles." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 39–44. http://dx.doi.org/10.5194/isprsarchives-xl-3-39-2014.
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In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. <br><br> In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.
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Zhang, Weixing, Chandi Witharana, Anna Liljedahl, and Mikhail Kanevskiy. "Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial Imagery." Remote Sensing 10, no. 9 (2018): 1487. http://dx.doi.org/10.3390/rs10091487.
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The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is transforming low-centered polygons into high-centered polygons at an alarming rate. Accurate data on spatial distribution of ice-wedge polygons at a pan-Arctic scale are not yet available, despite the availability of sub-meter-scale remote sensing imagery. This is because the necessary spatial detail quickly produces data volumes that hamper both manual and semi-automated mapping approaches across large geographical extents. Accordingly, transforming big imagery into ‘science-ready’ insightful analytics demands novel image-to-assessment pipelines that are fueled by advanced machine learning techniques and high-performance computational resources. In this exploratory study, we tasked a deep-learning driven object instance segmentation method (i.e., the Mask R-CNN) with delineating and classifying ice-wedge polygons in very high spatial resolution aerial orthoimagery. We conducted a systematic experiment to gauge the performances and interoperability of the Mask R-CNN across spatial resolutions (0.15 m to 1 m) and image scene contents (a total of 134 km2) near Nuiqsut, Northern Alaska. The trained Mask R-CNN reported mean average precisions of 0.70 and 0.60 at thresholds of 0.50 and 0.75, respectively. Manual validations showed that approximately 95% of individual ice-wedge polygons were correctly delineated and classified, with an overall classification accuracy of 79%. Our findings show that the Mask R-CNN is a robust method to automatically identify ice-wedge polygons from fine-resolution optical imagery. Overall, this automated imagery-enabled intense mapping approach can provide a foundational framework that may propel future pan-Arctic studies of permafrost thaw, tundra landscape evolution, and the role of high latitudes in the global climate system.
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Hasan, A., M. R. Udawalpola, C. Witharana, and A. K. Liljedahl. "COUNTING ICE-WEDGE POLYGONS FROM SPACE: USE OF COMMERCIAL SATELLITE IMAGERY TO MONITOR CHANGING ARCTIC POLYGONAL TUNDRA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 67–72. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-67-2021.
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Abstract. The microtopography associated with ice wedge polygons (IWPs) governs the Arctic ecosystem from local to regional scales due to the impacts on the flow and storage of water and therefore, vegetation and carbon. Increasing subsurface temperatures in Arctic permafrost landscapes cause differential ground settlements followed by a series of adverse microtopographic transitions at sub decadal scale. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. Dramatic microtopographic transformation of low-centered into high-centered IWPs can be identified using sub-meter resolution commercial satellite imagery. In this exploratory study, we have employed a Deep Learning (DL)-based object detection and semantic segmentation method named the Mask R-CNN to automatically map IWPs from commercial satellite imagery. Different tundra vegetation types have distinct spectral, spatial, textural characteristics, which in turn decide the semantics of overlying IWPs. Landscape complexity translates to the image complexity, affecting DL model performances. Scarcity of labelled training images, inadequate training samples for some types of tundra and class imbalance stand as other key challenges in this study. We implemented image augmentation methods to introduce variety in the training data and trained models separately for tundra types. Augmentation methods show promising results but the models with separate tundra types seem to suffer from the lack of annotated data.
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Manos, Elias, Chandi Witharana, Amal S. Perera, and Anna K. Liljedahl. "A multi-objective comparison of CNN architectures in Arctic human-built infrastructure mapping from sub-meter resolution satellite imagery." International Journal of Remote Sensing 44, no. 24 (2023): 7670–705. http://dx.doi.org/10.1080/01431161.2023.2287563.
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Duchesne, Rocio R., Mark J. Chopping, and Ken D. Tape. "Capability of the CANAPI algorithm to derive shrub structural parameters from satellite imagery in the Alaskan Arctic." Polar Record 52, no. 2 (2015): 124–33. http://dx.doi.org/10.1017/s0032247415000509.
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ABSTRACTThe observed greening of Arctic vegetation and the expansion of shrubs in the last few decades probably have profound implications for the tundra ecosystem, including feedbacks to climate. Uncertainty surrounding this vegetation shift and its implications calls for monitoring of vegetation structural parameters, such as fractional cover of shrubs. In this study, CANAPI, a semi-automated image interpretation algorithm that identifies and traces crowns by locating its crescent-shaped sunlit portion, was evaluated for its ability to derive structural data for tall (> 0.5 m) shrubs in the Arctic. CANAPI estimates of shrub canopy parameters were obtained from high-resolution imagery at 26 sites (250 m x 250 m each) by adjusting the algorithm's parameters and filter settings for each site, such that the number of crowns delineated by CANAPI roughly matched those observed in the high-resolution imagery. The CANAPI estimates were then compared with field measurements to evaluate the algorithm's performance. CANAPI successfully retrieved fractional cover (R2= 0.83,P< 0.001), mean crown radius (R2= 0.81,P< 0.001), and total number of shrubs (R2= 0.54,P< 0.001). CANAPI performed best in sparse vegetation where shrub canopies were distinct, while it tended to underestimate shrub cover where shrubs were clustered. The CANAPI algorithm and the regression equations presented here can be used in Arctic tundra environments to derive vegetation parameters from any sub-meter panchromatic imagery.
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Witharana, Chandi, Mahendra R. Udawalpola, Anna K. Liljedahl, et al. "Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery." Remote Sensing 14, no. 17 (2022): 4132. http://dx.doi.org/10.3390/rs14174132.
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Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall, study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents.
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McCarty, J. L., C. S. R. Neigh, M. L. Carroll, and M. R. Wooten. "Extracting smallholder cropped area in Tigray, Ethiopia with wall-to-wall sub-meter WorldView and moderate resolution Landsat 8 imagery." Remote Sensing of Environment 202 (December 2017): 142–51. http://dx.doi.org/10.1016/j.rse.2017.06.040.
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Cruz, C., A. C. Blanco, J. Babaan, J. A. Cruz, R. R. Sta. Ana, and E. Paringit. "LINEAR SPECTRAL UNMIXING OF SENTINEL-3 IMAGERY FOR URBAN LAND COVER - LAND SURFACE TEMPERATURE (LST) ANALYSIS: A CASE STUDY OF METRO MANILA, PHILIPPINES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 141–48. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-141-2019.
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Abstract. The advancement of remote sensing technologies is a huge advantage in various environmental applications including the monitoring of the rapid development in an urban area. This study aims to estimate the composition of the different classes (vegetation, impervious surfaces, soil) in Metro Manila, Philippines using a 300-meter spatial resolution Sentinel-3 Ocean and Land Colour Instrument image. The relationship between these land cover fractions with the spatial distribution of land surface temperature at this scale is evaluated. Sentinel-3 image has a higher spectral resolution (i.e. 21 bands), as compared with other Landsat and Sentinel missions, which is a requirement for an accurate cover mapping. Linear Spectral Unmixing (LSU), a sub-pixel classification method, was employed in identifying the fractional components in the image based on their spectral characteristics. Field survey using spectroradiometer was conducted to acquire spectral signatures of an impervious surface, vegetation, and soil which were used as the endmembers in the unmixing process. To assess the accuracy of the resulting vegetation fractional image, this was compared with a separate land cover pixel-based classification result using a 3-meter high spatial resolution PlanetScope image and with another vegetation index product of Sentinel-3. The results indicate that the recently available Sentinel-3 image can accurately estimate vegetation fraction with R2 = 0.84 and 0.99, respectively. In addition, the land surface temperature (LST) retrieved from Climate Engine is negatively correlated with the vegetation fraction cover (R2 = 0.81) and positively correlated with the impervious surface fraction cover (R2 = 0.66). Soil, on the other hand, has no correlation with the LST.
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Su, S., L. Fanara, X. Zhang, K. Gwinner, E. Hauber, and J. Oberst. "DETECTING THE SOURCES OF ICE BLOCK FALLS AT THE MARTIAN NORTH POLAR SCARPS BY ANALYSIS OF MULTI-TEMPORAL HIRISE IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 673–78. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-673-2021.
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Abstract. We have developed a method for automatically detecting the sources of ice block falls at the Martian north polar scarps. Multitemporal red-filter High Resolution Imaging Science Experiment (HiRISE) images were processed by using the open source NASA Ames Stereo Pipeline in combination with the USGS Integrated Software for Imagers and Spectrometers to produce 0.25 m resolution images as well as a 1 m resolution DTM. The multi-temporal HiRISE images were firstly ortho-rectified by the DTM, and then co-registered by using the Enhanced Correlation Coefficient Maximization (ECC) algorithm. We applied the change detection method on the well-aligned sub-meter scale HiRISE images, which were taken in Mars Year 29 and Mars Year 30, to investigate mass wasting at the scarp area centred at 85.0°N, 151.5°E. The idea of the change detection method is to identify changing shadow patterns based on the grayscale difference between the images. The final results show that erosion events occurred at the full length of this study’s scarp within one Mars Year. However, only the upper and lower part of the scarp show erosion activity, whereas the intermediate parts seem inactive, and this correlates with the slope.
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Issa, S. M., B. S. Dahy, and N. Saleous. "ACCURATE MAPPING OF DATE PALMS AT DIFFERENT AGE-STAGES FOR THE PURPOSE OF ESTIMATING THEIR BIOMASS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 461–67. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-461-2020.
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Abstract. The main objective of the current study was to produce an accurate map of date palm (DP) plantations in the Emirate of Abu Dhabi in United Arab Emirates (UAE) using moderate resolution Landsat-8 (OLI) imagery. The second objective was to be able to create a more detailed map depicting three different categories of date palms at three different age stages: young, medium, and mature. This was achieved using a hierarchical integrated approach: first, Landsat-8 OLI imagery were used to map mature date palms in the study area; second, an object oriented classification (OOC) approach was applied at the plantation level using sub-meter Worldview-2 imagery (WV-2) to depict and map medium and young aged palms. Three age-stage categories of date palms were mapped with acceptable accuracy. The primary outcome of this classification approach was the creation of detailed maps of date palms to be used as input to a remote sensing (RS) based biomass estimation model for the assessment of the above ground biomass (AGB) and carbon sequestered (CS) of date palms. Results were validated using existing ancillary data and field checks. DP were mapped with an overall accuracy of 94.5% which was considered high in similar conditions of drylands, while the overall kappa statistics was estimated at 0.888.
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Naughton, Joseph, and Walter McDonald. "Evaluating the Variability of Urban Land Surface Temperatures Using Drone Observations." Remote Sensing 11, no. 14 (2019): 1722. http://dx.doi.org/10.3390/rs11141722.
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Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote sensing studies are based upon satellite or aerial imagery that capture temperature at coarse resolutions that fail to capture the spatial complexities of urban land surfaces that can change at a sub-meter resolution. This study seeks to fill this gap by evaluating the spatial variability of land surface temperatures through drone thermal imagery captured at high-resolutions (13 cm). In this study, flights were conducted using a quadcopter drone and thermal camera at two case study locations in Milwaukee, Wisconsin and El Paso, Texas. Results indicate that land use types exhibit significant variability in their surface temperatures (3.9–15.8 °C) and that this variability is influenced by surface material properties, traffic, weather and urban geometry. Air temperature and solar radiation were statistically significant predictors of land surface temperature (R2 0.37–0.84) but the predictive power of the models was lower for land use types that were heavily impacted by pedestrian or vehicular traffic. The findings from this study ultimately elucidate factors that contribute to land surface temperature variability in the urban environment, which can be applied to develop better temperature mitigation practices to protect human and environmental health.
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Ganeva, Dessislava, Eugenia Roumenina, Petar Dimitrov, et al. "Preharvest Durum Wheat Yield, Protein Content, and Protein Yield Estimation Using Unmanned Aerial Vehicle Imagery and Pléiades Satellite Data in Field Breeding Experiments." Remote Sensing 16, no. 3 (2024): 559. http://dx.doi.org/10.3390/rs16030559.
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Unmanned aerial vehicles (UAVs) are extensively used to gather remote sensing data, offering high image resolution and swift data acquisition despite being labor-intensive. In contrast, satellite-based remote sensing, providing sub-meter spatial resolution and frequent revisit times, could serve as an alternative data source for phenotyping. In this study, we separately evaluated pan-sharpened Pléiades satellite imagery (50 cm) and UAV imagery (2.5 cm) to phenotype durum wheat in small-plot (12 m × 1.10 m) breeding trials. The Gaussian process regression (GPR) algorithm, which provides predictions with uncertainty estimates, was trained with spectral bands and а selected set of vegetation indexes (VIs) as independent variables. Grain protein content (GPC) was better predicted with Pléiades data at the growth stage of 20% of inflorescence emerged but with only moderate accuracy (validation R2: 0.58). The grain yield (GY) and protein yield (PY) were better predicted using UAV data at the late milk and watery ripe growth stages, respectively (validation: R2 0.67 and 0.62, respectively). The cumulative VIs (the sum of VIs over the available images within the growing season) did not increase the accuracy of the models for either sensor. When mapping the estimated parameters, the spatial resolution of Pléiades revealed certain limitations. Nevertheless, our findings regarding GPC suggested that the usefulness of pan-sharpened Pléiades images for phenotyping should not be dismissed and warrants further exploration, particularly for breeding experiments with larger plot sizes.
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Kaiser, Soraya, Guido Grosse, Julia Boike, and Moritz Langer. "Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery." Remote Sensing 13, no. 14 (2021): 2802. http://dx.doi.org/10.3390/rs13142802.
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Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40–0.56 m.yr−1 for lake sizes of 0.10 ha–23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations.
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Xia, T., W. P. Kustas, M. C. Anderson, et al. "Mapping evapotranspiration with high resolution aircraft imagery over vineyards using one and two source modeling schemes." Hydrology and Earth System Sciences Discussions 12, no. 11 (2015): 11905–57. http://dx.doi.org/10.5194/hessd-12-11905-2015.
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Abstract. Thermal and multispectral remote sensing data from low-altitude aircraft can provide high spatial resolution necessary for sub-field (≤ 10 m) and plant canopy (≤ 1m) scale evapotranspiration (ET) monitoring. In this study, high resolution aircraft sub-meter scale thermal infrared and multispectral shortwave data are used to map ET over vineyards in central California with the Two Source Energy Balance (TSEB) model and with a simple model called DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature) which uses contextual information within the image to scale between radiometric land surface temperature (TR) values representing hydrologic limits of potential ET and a non-evaporative surface. Imagery from five days throughout the growing season is used for mapping ET at the sub-field scale. The performance of the two models is evaluated using tower-based energy flux measurements of sensible (H) and latent heat (LE) or ET. The comparison indicates that TSEB was able to derive reasonable ET estimates under varying conditions, likely due to the physically based treatment of the energy and the surface temperature partitioning between the soil/cover crop inter-row and vine canopy elements. On the other hand, DATTUTDUT performance was somewhat degraded presumably because the simple scaling scheme does not consider differences in the two sources (vine and inter-row) of heat and temperature contributions or the effect of surface roughness on the efficiency of heat exchange. Maps of the evaporative fraction (EF = LE/(H + LE)) from the two models had similar spatial patterns but different magnitudes in some areas within the fields on certain days. Large EF discrepancies between the models were found on two of the five days (DOY 162 and 219) when there were significant differences with the tower-based ET measurements, particularly using the DATTUTDUT model. These differences in EF between the models translate to significant variations in daily water use estimates for these two days for the vineyards. Model sensitivity analysis demonstrated the high degree of sensitivity of the TSEB model to the accuracy of the TR data while the DATTUTDUT model was insensitive as is the case with contextual-based models. However, study domain and spatial resolution will significantly influence the ET estimation from the DATTUTDUT model. Future work is planned for developing a hybrid approach that leverages the strengths of both modeling schemes and is simple enough to be used operationally with high resolution imagery.
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Kaiser, Soraya, Julia Boike, Guido Grosse, and Moritz Langer. "The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure." Remote Sensing 14, no. 23 (2022): 6107. http://dx.doi.org/10.3390/rs14236107.
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Ground subsidence and erosion processes caused by permafrost thaw pose a high risk to infrastructure in the Arctic. Climate warming is increasingly accelerating the thawing of permafrost, emphasizing the need for thorough monitoring to detect damages and hazards at an early stage. The use of unoccupied aerial vehicles (UAVs) allows a fast and uncomplicated analysis of sub-meter changes across larger areas compared to manual surveys in the field. In our study, we investigated the potential of photogrammetry products derived from imagery acquired with off-the-shelf UAVs in order to provide a low-cost assessment of the risks of permafrost degradation along critical infrastructure. We tested a minimal drone setup without ground control points to derive high-resolution 3D point clouds via structure from motion (SfM) at a site affected by thermal erosion along the Dalton Highway on the North Slope of Alaska. For the sub-meter change analysis, we used a multiscale point cloud comparison which we improved by applying (i) denoising filters and (ii) alignment procedures to correct for horizontal and vertical offsets. Our results show a successful reduction in outliers and a thorough correction of the horizontal and vertical point cloud offset by a factor of 6 and 10, respectively. In a defined point cloud subset of an erosion feature, we derive a median land surface displacement of −0.35 m from 2018 to 2019. Projecting the development of the erosion feature, we observe an expansion to NNE, following the ice-wedge polygon network. With a land surface displacement of −0.35 m and an alignment root mean square error of 0.99 m, we find our workflow is best suitable for detecting and quantifying rapid land surface changes. For a future improvement of the workflow, we recommend using alternate flight patterns and an enhancement of the point cloud comparison algorithm.
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Wang, Zifeng, Junguo Liu, Jinbao Li, and David Zhang. "Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2." Remote Sensing 10, no. 10 (2018): 1643. http://dx.doi.org/10.3390/rs10101643.
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Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.
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Xia, Ting, William P. Kustas, Martha C. Anderson, et al. "Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one- and two-source modeling schemes." Hydrology and Earth System Sciences 20, no. 4 (2016): 1523–45. http://dx.doi.org/10.5194/hess-20-1523-2016.
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Abstract. Thermal and multispectral remote sensing data from low-altitude aircraft can provide high spatial resolution necessary for sub-field (≤ 10 m) and plant canopy (≤ 1 m) scale evapotranspiration (ET) monitoring. In this study, high-resolution (sub-meter-scale) thermal infrared and multispectral shortwave data from aircraft are used to map ET over vineyards in central California with the two-source energy balance (TSEB) model and with a simple model having operational immediate capabilities called DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature). The latter uses contextual information within the image to scale between radiometric land surface temperature (TR) values representing hydrologic limits of potential ET and a non-evaporative surface. Imagery from 5 days throughout the growing season is used for mapping ET at the sub-field scale. The performance of the two models is evaluated using tower-based measurements of sensible (H) and latent heat (LE) flux or ET. The comparison indicates that TSEB was able to derive reasonable ET estimates under varying conditions, likely due to the physically based treatment of the energy and the surface temperature partitioning between the soil/cover crop inter-row and vine canopy elements. On the other hand, DATTUTDUT performance was somewhat degraded presumably because the simple scaling scheme does not consider differences in the two sources (vine and inter-row) of heat and temperature contributions or the effect of surface roughness on the efficiency of heat exchange. Maps of the evaporative fraction (EF = LE/(H + LE)) from the two models had similar spatial patterns but different magnitudes in some areas within the fields on certain days. Large EF discrepancies between the models were found on 2 of the 5 days (DOY 162 and 219) when there were significant differences with the tower-based ET measurements, particularly using the DATTUTDUT model. These differences in EF between the models translate to significant variations in daily water use estimates for these 2 days for the vineyards. Model sensitivity analysis demonstrated the high degree of sensitivity of the TSEB model to the accuracy of the TR data, while the DATTUTDUT model was insensitive to systematic errors in TR as is the case with contextual-based models. However, it is shown that the study domain and spatial resolution will significantly influence the ET estimation from the DATTUTDUT model. Future work is planned for developing a hybrid approach that leverages the strengths of both modeling schemes and is simple enough to be used operationally with high-resolution imagery.
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Ku, Mengjun, Hao Jiang, Kai Jia, et al. "Cropland Inundation Mapping in Rugged Terrain Using Sentinel-1 and Google Earth Imagery: A Case Study of 2022 Flood Event in Fujian Provinces." Agronomy 14, no. 1 (2024): 138. http://dx.doi.org/10.3390/agronomy14010138.
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South China is dominated by mountainous agriculture and croplands that are at risk of flood disasters, posing a great threat to food security. Synthetic aperture radar (SAR) has the advantage of being all-weather, with the ability to penetrate clouds and monitor cropland inundation information. However, SAR data may be interfered with by noise, i.e., radar shadows and permanent water bodies. Existing cropland data derived from open-access landcover data are not accurate enough to mask out these noises mainly due to insufficient spatial resolution. This study proposed a method that extracted cropland inundation with a high spatial resolution cropland mask. First, the Proportional–Integral–Derivative Network (PIDNet) was applied to the sub-meter-level imagery to identify cropland areas. Then, Sentinel-1 dual-polarized water index (SDWI) and change detection (CD) were used to identify flood area from open water bodies. A case study was conducted in Fujian province, China, which endured several heavy rainfalls in summer 2022. The result of the Intersection over Union (IoU) of the extracted cropland data reached 89.38%, and the F1-score of cropland inundation achieved 82.35%. The proposed method provides support for agricultural disaster assessment and disaster emergency monitoring.
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Qing, Yuanyuan, Jiang Zhu, Hongchuan Feng, Weixian Liu, and Bihan Wen. "Two-Way Generation of High-Resolution EO and SAR Images via Dual Distortion-Adaptive GANs." Remote Sensing 15, no. 7 (2023): 1878. http://dx.doi.org/10.3390/rs15071878.
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Synthetic aperture radar (SAR) provides an all-weather and all-time imaging platform, which is more reliable than electro-optical (EO) remote sensing imagery under extreme weather/lighting conditions. While many large-scale EO-based remote sensing datasets have been released for computer vision tasks, there are few publicly available SAR image datasets due to the high costs associated with acquisition and labeling. Recent works have applied deep learning methods for image translation between SAR and EO. However, the effectiveness of those techniques on high-resolution images has been hindered by a common limitation. Non-linear geometric distortions, induced by different imaging principles of optical and radar sensors, have caused insufficient pixel-wise correspondence between an EO-SAR patch pair. Such a phenomenon is not prominent in low-resolution EO-SAR datasets, e.g., SEN1-2, one of the most frequently used datasets, and thus has been seldom discussed. To address this issue, a new dataset SN6-SAROPT with sub-meter resolution is introduced, and a novel image translation algorithm designed to tackle geometric distortions adaptively is proposed in this paper. Extensive experiments have been conducted to evaluate the proposed algorithm, and the results have validated its superiority over other methods for both SAR to EO (S2E) and EO to SAR (E2S) tasks, especially for urban areas in high-resolution images.
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Lewis, Sarah A., Peter R. Robichaud, Andrew T. Hudak, Eva K. Strand, Jan U. H. Eitel, and Robert E. Brown. "Evaluating the Persistence of Post-Wildfire Ash: A Multi-Platform Spatiotemporal Analysis." Fire 4, no. 4 (2021): 68. http://dx.doi.org/10.3390/fire4040068.
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As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most ‘cost’ effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6–0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources.
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Shahtakhtinskiy, Aydin, Shuhab D. Khan, and Sara S. Rojas. "Quantifying the Impact of Hurricane Harvey on Beach−Dune Systems of the Central Texas Coast and Monitoring Their Changes Using UAV Photogrammetry." Remote Sensing 15, no. 24 (2023): 5779. http://dx.doi.org/10.3390/rs15245779.
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Historically, the Texas Gulf Coast has been affected by many tropical storms and hurricanes. The most recent severe impact was caused by Hurricane Harvey, which made landfall in August 2017 on the central Texas coast. We evaluated the impact of Hurricane Harvey on the barrier islands of the central Texas coast, including San Jose Island, Mustang Island, and North Padre Island. We used public data sets, including 1 m resolution bare-earth digital elevation models (DEMs), derived from airborne lidar acquisitions before (2016) and after (2018) Hurricane Harvey, and sub-meter scale aerial imagery pre- and post-Harvey to evaluate changes at a regional scale. Shoreline proxies were extracted to quantify shoreline retreat and/or advance, and DEM differencing was performed to quantify net sediment erosion and accretion or deposition. Unmanned aerial vehicle surveys were conducted at each island to produce high-resolution (cm scale) imagery and topographic data used for morphological and change analyses of beaches and dunes at the local scale. The results show that Hurricane Harvey caused drastic local shoreline retreat, reaching 59 m, and significant erosion levels of beach−dune elements immediately after its landfall. Erosion and recovery processes and their levels were influenced by the local geomorphology of the beach−foredune complexes. It is also observed that local depositional events contributed to their post-storm rebuilding. This study aims to enhance the understanding of major storm impacts on coastal areas and help in future protection planning of the Texas coast. It also has broader implications for coastlines on Earth affected by major storms.
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Palaseanu-Lovejoy, Monica, Marina Bisson, Claudia Spinetti, Maria Fabrizia Buongiorno, Oleg Alexandrov, and Thomas Cecere. "High-Resolution and Accurate Topography Reconstruction of Mount Etna from Pleiades Satellite Data." Remote Sensing 11, no. 24 (2019): 2983. http://dx.doi.org/10.3390/rs11242983.
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The areas characterized by dynamic and rapid morphological changes need accurate topography information with frequent updates, especially if these are populated and involve infrastructures. This is particularly true in active volcanic areas such as Mount (Mt.) Etna, located in the northeastern portion of Sicily, Italy. The Mt. Etna volcano is periodically characterized by explosive and effusive eruptions and represents a potential hazard for several thousands of local people and hundreds of tourists present on the volcano itself. In this work, a high-resolution, high vertical accuracy digital surface model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set. We believe that this is the first time that the ASP using Pleiades imagery has been applied to Mt. Etna with sub-meter vertical root mean square error (RMSE) results. The model covers an area of about 400 km2 with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical RMSE of 0.78 m. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short amount of processing time, making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions.
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Zhu, Xiaoyong, Xinming Tang, Guo Zhang, Bin Liu, and Wenmin Hu. "Accuracy Comparison and Assessment of DSM Derived from GFDM Satellite and GF-7 Satellite Imagery." Remote Sensing 13, no. 23 (2021): 4791. http://dx.doi.org/10.3390/rs13234791.
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Digital Surface Model (DSM) derived from high resolution satellite imagery is important for various applications. GFDM is China’s first civil optical remote sensing satellite with multiple agile imaging modes and sub-meter resolution. Its panchromatic resolution is 0.5 m and 1.68 m for multi-spectral images. Compared with the onboard stereo viewing instruments (0.8 m for forward image, 0.65 m for back image, and 2.6 m for back multi-spectrum images) of GF-7, a mapping satellite of China in the same period, their accuracy is very similar. However, the accuracy of GFDM DSM has not yet been verified or fully characterized, and the detailed difference between the two has not yet been assessed either. This paper evaluates the DSM accuracy generated by GFDM and GF-7 satellite imagery using high-precision reference DSM and the observations of Ground Control Points (GCPs) as the reference data. A method to evaluate the DSM accuracy based on regional DSM errors and GCPs errors is proposed. Through the analysis of DSM subtraction, profile lines, strips detection and residuals coupling differences, the differences of DSM overall accuracy, vertical accuracy, horizontal accuracy and the strips errors between GFDM DSM and GF-7 DSM are evaluated. The results show that the overall accuracy of both is close while the vertical accuracy is slightly different. When regional DSM is used as the benchmark, the GFDM DSM has a slight advantage in elevation accuracy, but there are some regular fluctuation strips with small amplitude. When GCPs are used as the reference, the elevation Root Mean Square Error (RMSE) of GFDM DSM is about 0.94 m, and that of GF-7 is 0.67 m. GF-7 DSM is more accurate, but both of the errors are within 1 m. The DSM image residuals of the GF-7 are within 0.5 pixel, while the residuals of GFDM are relatively large, reaching 0.8 pixel.
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Zhang, Chunmei, Chunmei Wang, Yongqing Long, et al. "Comparative Analysis of Gully Morphology Extraction Suitability Using Unmanned Aerial Vehicle and Google Earth Imagery." Remote Sensing 15, no. 17 (2023): 4302. http://dx.doi.org/10.3390/rs15174302.
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Gully erosion is considered to be a highly destructive form of soil erosion, often leading to the occurrence of natural calamities like landslides and mudslides. Remote sensing images have been extensively utilized in gully erosion research, and the suitability of extracting gully morphology parameters in various topographic regions needs to be clarified. Based on field measurements, this paper focuses on two widely used high-resolution remote sensing images: Unmanned Aerial Vehicle (UAV) and Google Earth (GE) imagery. It systematically examines the accuracy of gully morphological characteristic extraction using remote sensing in two regions with different terrain characteristics. The results show the following: (1) Compared to interpreting wide gullies with unclear shoulder lines, centimeter-level UAV imagery is more suitable for interpreting narrow gullies with clear shoulder lines. Conversely, the interpretability of sub-meter-level GE imagery is exactly the opposite. (2) The error in interpreting gully head points (GHPs) based on UAV images is less than 1 m, while the errors in gully length (GL), width (GW), perimeter (GP) and area (GA) are all below 3%, and these errors are hardly affected by gully morphology. (3) The error of GHPs based on GE images is concentrated within the range of 1–3 m. Meanwhile, the errors associated with GL, GP and GA are less than 10%. Conversely, the error of GW exceeds 11%. Furthermore, the aforementioned errors tend to increase as the gully width decreases and the complexity of the gully shoulder line increases. These findings shed light on the suitability of two commonly used remote sensing images for gully morphology extraction and provide valuable guidance for image selection in future research endeavors in this field.
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Bhuiyan, Md Abul Ehsan, Chandi Witharana, and Anna K. Liljedahl. "Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types." Journal of Imaging 6, no. 12 (2020): 137. http://dx.doi.org/10.3390/jimaging6120137.
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We developed a high-throughput mapping workflow, which centers on deep learning (DL) convolutional neural network (CNN) algorithms on high-performance distributed computing resources, to automatically characterize ice-wedge polygons (IWPs) from sub-meter resolution commercial satellite imagery. We applied a region-based CNN object instance segmentation algorithm, namely the Mask R-CNN, to automatically detect and classify IWPs in North Slope of Alaska. The central goal of our study was to systematically expound the DLCNN model interoperability across varying tundra types (sedge, tussock sedge, and non-tussock sedge) and image scene complexities to refine the understanding of opportunities and challenges for regional-scale mapping applications. We corroborated quantitative error statistics along with detailed visual inspections to gauge the IWP detection accuracies. We found promising model performances (detection accuracies: 89% to 96% and classification accuracies: 94% to 97%) for all candidate image scenes with varying tundra types. The mapping workflow discerned the IWPs by exhibiting low absolute mean relative error (AMRE) values (0.17–0.23). Results further suggest the importance of increasing the variability of training samples when practicing transfer-learning strategy to map IWPs across heterogeneous tundra cover types. Overall, our findings demonstrate the robust performances of IWPs mapping workflow in multiple tundra landscapes.
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Berthier, E., C. Vincent, E. Magnússon, et al. "Glacier topography and elevation changes from Pléiades very high resolution stereo images." Cryosphere Discussions 8, no. 5 (2014): 4849–83. http://dx.doi.org/10.5194/tcd-8-4849-2014.
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Abstract. In response to climate change, most glaciers are losing mass and hence contribute to sea-level rise. Repeated and accurate mapping of their surface topography is required to estimate their mass balance and to extrapolate/calibrate sparse field glaciological measurements. In this study we evaluate the potential of Pléiades sub-meter stereo imagery to derive digital elevation models (DEMs) of glaciers and their elevation changes. Our five validation sites are located in Iceland, the European Alps, the Central Andes, Nepal and Antarctica. For all sites, nearly simultaneous field measurements were collected to evaluate the Pléiades DEMs. For Iceland, the Pléiades DEM is also compared to a Lidar DEM. The vertical biases of the Pléiades DEMs are less than 1 m if ground control points (GCPs) are used, but reach up to 6 m without GCPs. Even without GCPs, vertical biases can be reduced to a few decimetres by horizontal and vertical co-registration of the DEMs to reference altimetric data on ice-free terrain. Around these biases, the vertical precision of the Pléiades DEMs is ±1 m and even ±0.5 m on the flat glacier tongues (1-sigma confidence level). We also demonstrate the high potential of Pléiades DEMs for measuring seasonal, annual and multi-annual elevation changes with an accuracy of 1 m or better. The negative glacier-wide mass balances of the Argentière Glacier and Mer de Glace (−1.21 ± 0.16 and −1.19 ± 0.16 m.w.e. yr−1, respectively) are revealed by differencing SPOT5 and Pléiades DEMs acquired in August 2003 and 2012 demonstrating the continuing rapid glacial wastage in the Mont-Blanc area.
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Shean, David E., Ian R. Joughin, Pierre Dutrieux, Benjamin E. Smith, and Etienne Berthier. "Ice shelf basal melt rates from a high-resolution digital elevation model (DEM) record for Pine Island Glacier, Antarctica." Cryosphere 13, no. 10 (2019): 2633–56. http://dx.doi.org/10.5194/tc-13-2633-2019.
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Abstract. Ocean-induced basal melting is responsible for much of the Amundsen Sea Embayment ice loss in recent decades, but the total magnitude and spatiotemporal evolution of this melt is poorly constrained. To address this problem, we generated a record of high-resolution digital elevation models (DEMs) for Pine Island Glacier (PIG) using commercial sub-meter satellite stereo imagery and integrated additional 2002–2015 DEM and altimetry data. We implemented a Lagrangian elevation change (Dh∕Dt) framework to estimate ice shelf basal melt rates at 32–256 m resolution. We describe this methodology and consider basal melt rates and elevation change over the PIG ice shelf and lower catchment from 2008 to 2015. We document the evolution of Eulerian elevation change (dh∕dt) and upstream propagation of thinning signals following the end of rapid grounding line retreat around 2010. Mean full-shelf basal melt rates for the 2008–2015 period were ∼82–93 Gt yr−1, with ∼200–250 m yr−1 basal melt rates within large channels near the grounding line, ∼10–30 m yr−1 over the main shelf, and ∼0–10 m yr−1 over the North shelf and South shelf, with the notable exception of a small area with rates of ∼50–100 m yr−1 near the grounding line of a fast-flowing tributary on the South shelf. The observed basal melt rates show excellent agreement with, and provide context for, in situ basal melt-rate observations. We also document the relative melt rates for kilometer-scale basal channels and keels at different locations on the ice shelf and consider implications for ocean circulation and heat content. These methods and results offer new indirect observations of ice–ocean interaction and constraints on the processes driving sub-shelf melting beneath vulnerable ice shelves in West Antarctica.
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Ardelean, Florina, Alexandru Onaca, Marinela-Adriana Chețan, et al. "Assessment of Spatio-Temporal Landscape Changes from VHR Images in Three Different Permafrost Areas in the Western Russian Arctic." Remote Sensing 12, no. 23 (2020): 3999. http://dx.doi.org/10.3390/rs12233999.
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Our study highlights the usefulness of very high resolution (VHR) images to detect various types of disturbances over permafrost areas using three example regions in different permafrost zones. The study focuses on detecting subtle changes in land cover classes, thermokarst water bodies, river dynamics, retrogressive thaw slumps (RTS) and infrastructure in the Yamal Peninsula, Urengoy and Pechora regions. Very high-resolution optical imagery (sub-meter) derived from WorldView, QuickBird and GeoEye in conjunction with declassified Corona images were involved in the analyses. The comparison of very high-resolution images acquired in 2003/2004 and 2016/2017 indicates a pronounced increase in the extent of tundra and a slight increase of land covered by water. The number of water bodies increased in all three regions, especially in discontinuous permafrost, where 14.86% of new lakes and ponds were initiated between 2003 and 2017. The analysis of the evolution of two river channels in Yamal and Urengoy indicates the dominance of erosion during the last two decades. An increase of both rivers’ lengths and a significant widening of the river channels were also observed. The number and total surface of RTS in the Yamal Peninsula strongly increased between 2004 and 2016. A mean annual headwall retreat rate of 1.86 m/year was calculated. Extensive networks of infrastructure occurred in the Yamal Peninsula in the last two decades, stimulating the initiation of new thermokarst features. The significant warming and seasonal variations of the hydrologic cycle, in particular, increased snow water equivalent acted in favor of deepening of the active layer; thus, an increasing number of thermokarst lake formations.
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Zhu, Xiaoxiao, Zhikun Ren, Sheng Nie, et al. "DEM Generation from GF-7 Satellite Stereo Imagery Assisted by Space-Borne LiDAR and Its Application to Active Tectonics." Remote Sensing 15, no. 6 (2023): 1480. http://dx.doi.org/10.3390/rs15061480.
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China’s first optical stereo mapping satellite with a sub-meter resolution, GaoFen-7 (GF-7), launched in November 2019, shows significant potential for providing high-resolution topographic and geomorphic data for quantitative research on active tectonics. However, no studies have evaluated the capability of the GF-7-generated digital elevation model (DEM) for quantitatively studying active tectonics. This study aimed to validate the accuracy of the DEMs extracted from GF-7 stereo imagery, with or without ground control points (GCPs), and evaluated the potential of applying GF-7 DEMs to active tectonics. First, GF-7 stereo images were processed to obtain DEMs with a spatial resolution of 2 m, utilizing three different methods, including block adjustment without GCPs, block adjustment with the aid of Google Earth images and SRTM DEM, and block adjustment with GCPs derived from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data. These three generated DEMs were called GF-7 DEMMethod1, GF-7 DEMMethod2, and GF-7 DEMMethod3, respectively, and were verified by the airborne LiDAR data in the Hasishan section of the Haiyuan fault. Second, the capability of the GF-7 DEMs for identifying active faults, fault scarps, and horizontal offsets was evaluated. Finally, 8 vertical and 13 horizontal offsets were measured based on three different GF-7 DEMs, and airborne LiDAR data were used to verify the measurements’ accuracies. The results indicated that the accuracy of GF-7 DEMMethod1 was the worst and that of GF-7 DEMMethod3 was superior to that of GF-7 DEMMethod2. The GF-7 DEMs could effectively identify the apparent fault scarps and horizontal offsets. The RMSE values of the vertical offsets measured based on GF-7 DEMMethod1, GF-7 DEMMethod2, and GF-7 DEMMethod3 were 0.55 m, 0.55 m, and 0.41 m, respectively. The horizontal offsets yielded RMSE values of 3.98 m, 2.52 m, and 1.37 m, respectively. These findings demonstrated that vertical and horizontal offsets could be accurately measured using the DEMs generated from GF-7 stereo images. Meanwhile, our study indicated that the GCPs derived from ICESat-2 data could be utilized to improve the accuracies of the GF-7 DEM, and the measurements of vertical and horizontal offsets.
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Novo-Fernández, Alís, Carlos A. López-Sánchez, Asunción Cámara-Obregón, Marcos Barrio-Anta, and Iyán Teijido-Murias. "Estimating Forest Variables for Major Commercial Timber Plantations in Northern Spain Using Sentinel-2 and Ancillary Data." Forests 15, no. 1 (2024): 99. http://dx.doi.org/10.3390/f15010099.
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In this study, we used Spanish National Forest Inventory (SNFI) data, Sentinel-2 imagery and ancillary data to develop models that estimate forest variables for major commercial timber plantations in northern Spain. We carried out the analysis in two stages. In the first stage, we considered plots with and without sub-meter geolocation, three pre-processing levels for the Sentinel-2 images and two machine learning algorithms. In most cases, geometrically, radiometrically, atmospherically and topographically (L2A-ATC) corrected images and the random forest algorithm provided the best results, with topographic correction producing a greater gain in model accuracy as the average slope of the plots increased. Our results did not show any clear impact of the geolocation accuracy of SNFI plots on results, suggesting that the usual geolocation accuracy of SNFI plots is adequate for developing forest models with data obtained from passive sensors. In the second stage, we used all plots together with L2A-ATC-corrected images to select five different groups of predictor variables in a cumulative process to determine the influence of each group of variables in the final RF model predictions. Yield variables produced the best fits, with R2 ranging from 0.39 to 0.46 (RMSE% ranged from 44.6% to 61.9%). Although the Sentinel-2-based estimates obtained in this research are less precise than those previously obtained with Airborne Laser Scanning (ALS) data for the same species and region, they are unbiased (Bias% was always below 1%). Therefore, accurate estimates for one hectare are expected, as they are obtained by averaging the values of 100 pixels (model resolution of 10 m pixel−1) with an expected error compensation. Moreover, the use of these models will overcome the temporal resolution problem associated with the previous ALS-based models and will enable annual updates of forest timber resource estimates to be obtained.
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Harvey, Mark C., Danielle K. Hare, Alex Hackman, et al. "Evaluation of Stream and Wetland Restoration Using UAS-Based Thermal Infrared Mapping." Water 11, no. 8 (2019): 1568. http://dx.doi.org/10.3390/w11081568.
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Large-scale wetland restoration often focuses on repairing the hydrologic connections degraded by anthropogenic modifications. Of these hydrologic connections, groundwater discharge is an important target, as these surface water ecosystem control points are important for thermal stability, among other ecosystem services. However, evaluating the effectiveness of the restoration activities on establishing groundwater discharge connection is often difficult over large areas and inaccessible terrain. Unoccupied aircraft systems (UAS) are now routinely used for collecting aerial imagery and creating digital surface models (DSM). Lightweight thermal infrared (TIR) sensors provide another payload option for generation of sub-meter-resolution aerial TIR orthophotos. This technology allows for the rapid and safe survey of groundwater discharge areas. Aerial TIR water-surface data were collected in March 2019 at Tidmarsh Farms, a former commercial cranberry peatland located in coastal Massachusetts, USA (41°54′17″ N 70°34′17″ W), where stream and wetland restoration actions were completed in 2016. Here, we present a 0.4 km2 georeferenced, temperature-calibrated TIR orthophoto of the area. The image represents a mosaic of nearly 900 TIR images captured by UAS in a single morning with a total flight time of 36 min and is supported by a DSM derived from UAS-visible imagery. The survey was conducted in winter to maximize temperature contrast between relatively warm groundwater and colder ambient surface environment; lower-density groundwater rises above cool surface waters and thus can be imaged by a UAS. The resulting TIR orthomosaic shows fine detail of seepage distribution and downstream influence along the several restored channel forms, which was an objective of the ecological restoration design. The restored stream channel has increased connectivity to peatland groundwater discharge, reducing the ecosystem thermal stressors. Such aerial techniques can be used to guide ecological restoration design and assess post-restoration outcomes, especially in settings where ecosystem structure and function is governed by groundwater and surface water interaction.
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Yuan, Jiayu, Zhiwei Wu, Shun Li, Ping Kang, and Shihao Zhu. "Multi-Feature-Based Identification of Subtropical Evergreen Tree Species Using Gaofen-2 Imagery and Algorithm Comparison." Forests 14, no. 2 (2023): 292. http://dx.doi.org/10.3390/f14020292.
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The species and distribution of trees in a forest are critical to the understanding of forest ecosystem processes and the development of forest management strategies. Subtropical forest landscapes feature a complex canopy structure and high stand density. Studies on the effects of classification algorithms on the remote sensing-based identification of tree species are few. GF-2 is the first satellite in China with sub-meter accuracy which has the high resolution and short replay cycle. Here, we considered three representative tree types (Masson pine, Chinese fir, and broadleaved evergreen trees) in the southern subtropical evergreen broadleaved forest region of China as research objects. We quantitatively compared the effects of five machine learning algorithms, including the backpropagation neural network, k-nearest neighbour, polytomous logistic regression, random forest (RF) and support vector machine (SVM), and four features (vegetation index, band reflectance, textural features, and topographic factors) on tree species identification using Gaofen-2 panchromatic and multispectral remote sensing images and field survey data. All five classification algorithms could effectively identify major tree species in subtropical forest areas (overall accuracy [OA] > 87.40%, kappa coefficient > 81.08%). The SVM model exhibited the best identification ability (OA = 90.27%, kappa coefficient = 85.37%), followed by RF (OA = 88.90%, Kappa coefficient = 83.30%). The combination of band reflectance, vegetation index, and the topographic factor performed exhibited the best, followed by the combination of band reflectance, vegetation index, textural feature, and topographic factor. In addition, we find that the classifier constructed by a single feature is not as effective as the combination of multiple feature factors. The addition of topographic factors can significantly improve the ability of tree species identification. According to the results of the five classifiers, the separability of the three tree species was good. The producer’s accuracy and user’s accuracy of Masson pine were more than 90%, and the evergreen broad-leaved tree and Chinese fir were more than 80%. The commission errors and omission errors of the three tree species were evergreen broadleaved tree > Chinese fir > Masson pine. The variable importance assessment results showed that the normalized difference greenness index, altitude, and the modified soil-adjusted vegetation index were the key variables. The results of this study used GF-2 to accurately identify the main tree species of subtropical evergreen forests in China, which can help forest managers to regularly monitor tree species composition and provide theoretical support for forest managers to formulate policies, monitor sustainable plans for wood mining, and forest conservation and management measures.
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Dimyati, Akhmad E. Firlli, Lili Somantri, and Nanin Trianawati Sugito. "Klasifikasi Berbasis Objek Citra Satelit Sentinel 2 untuk Pemetaan Perubahan Lahan di Kecamatan Parongpong Kabupaten Bandung Barat." Jurnal Geografi : Media Informasi Pengembangan dan Profesi Kegeografian 19, no. 1 (2022): 24–28. http://dx.doi.org/10.15294/jg.v19i1.33958.
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Kecamatan Parongpong merupakan kawasan pinggiran kota dimana wilayah ini menjadi salah satu wilayah favorit dalam pemilihan wilayah pemukiman, di sisi lain wilayah ini masih di dominasi oleh lahan pertanian. Pentingnya monitoring dalam Perubahan lahan di suatu wilayah terutama wilayah pinggiran kota adalah sangat penting. Penggunaan data citra satelit resolusi menengah akan menjadi pilihan yang baik karena aksesnya yang mudah dan ketersediaan data gratis. Sentinel-2 adalah salah satu Citra satelit resolusi menengah yang dapat diakses gratis dimana memiliki resolusi spasial 10 meter dalam saluran tampak. Metode yang digunakan adalah penginderaan jauh dengan pendekatan OBIA (Object-Based Image Analysis) dengan data temporal selama dua tahun pada tahun 2017-2019. Uji akurasi Citra menghasilkan nilai Akurasi Total (Overal Accuracy) sebesar 94,8 % dan nilai Kappa Accuracy diperoleh sebesar 95,04%. Dari hasil Penelitian menunjukan bahwa Tingkat perubahan Penggunaan lahan yang terjadi di Kecamatan Parongpong pada kurun waktu dua tahun yaitu dimulai pada tahun 2017 sampai dengan tahun 2019 memiliki laju perubahan pada rentang nilai antara -0,21% sampai dengan 0,17 %. Pengurangan penggunaan lahan terbesar pada semak Belukar dan lahan kosong berkurang sebesar -8,9 Ha dan penambahan terbesar pada penggunaan lahan Pemukiman dan Tempat Kegiatan Sebesar 7,27 Ha. Pola Perubahan yang terjadi hanya pada empat jenis Penggunaan lahan Hutan, Semak Belukar ladang dimana pola perubahan yang terjadi adalah penggunaan lahan hutan menjadi pemukiman dan ladang. Sementara semak belukar menjadi ladang dan Pemukiman. Parongpong sub-district is a suburban area where this area is one of the favorite areas in selecting residential areas, on the other hand this area is still dominated by agricultural land. The importance of monitoring in land change in an area, especially in suburban areas is very important. The use of medium resolution satellite imagery data would be a good choice because of the easy access and availability of free data. Sentinel-2 is one of the free medium resolution satellite imagery which has a spatial resolution of 10 meters in the visible channel. The method used is remote sensing with the OBIA (Object-Based Image Analysis) approach with temporal data for two years in 2017-2019. Image accuracy test resulted in an Overal Accuracy value of 94.8% and a Kappa Accuracy value of 95.04%. The results of the study show that the rate of land use change that occurred in Parongpong Subdistrict in a period of two years, starting in 2017 to 2019, has a rate of change in the value range between -0.21% to 0.17%. The largest reduction in land use was in scrub scrub and empty land by -8.9 Ha and the largest increase in land use for Settlements and Activities by 7.27 Ha. Patterns of change that occur are only in four types of forest land use, bush scrub, where the pattern of change that occurs is the use of forest land to settlement and fields. Meanwhile, shrubs become fields and settlements.