Evaluating high-resolution remote sensing data for reconstructing the recent evolution of supra glacial debris

2018 ◽  
Vol 42 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Roberto S Azzoni ◽  
Davide Fugazza ◽  
Andrea Zerboni ◽  
Antonella Senese ◽  
Carlo D’Agata ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
European Alps ◽  
Pixel Size ◽  
High Resolution Data ◽  
Medium Resolution ◽  
Debris Cover ◽  
Resolution Data ◽  
Sentinel 2

Over the last decades, the expansion of supraglacial debris on worldwide mountain glaciers has been reported. Nevertheless, works dealing with the detection and mapping of supraglacial debris and detailed analyses aimed at identifying the temporal and spatial trends affecting glacier debris cover are still limited. In this study, we used different remote sensing sources to detect and map the supraglacial debris cover, to analyze its evolution, and to assess the potential of different remote-sensed image data. We performed our analyses on the glaciers of Ortles-Cevedale Group (Stelvio Park, Italy), one of the most representative glacierized sectors of the European Alps. High-resolution airborne orthophotos (pixel size 0.5 m × 0.5 m) acquired during the summer season in the years 2003, 2007, and 2012 permitted to map in detail, with an error lower than ±5%, the supraglacial debris cover through a maximum likelihood classification. Our findings suggest that over the period 2003–2012, supraglacial debris cover increased from 16.7% to 30.1% of the total glacier area. On Forni Glacier we extended these quantification thanks to the availability of UAV (Unmanned Aerial Vehicle) orthophotos from 2014 and 2015 (pixel size 0.15 m × 0.15 m): this detailed analysis permitted to confirm debris is increasing on the glacier melting surface (+20.4%) and confirms the requirement of high-resolution data in debris mapping on Alpine glaciers. Finally, we also checked the suitability of medium-resolution Landsat ETM+ data and Sentinel 2 data to map debris in a typical Alpine glaciation scenario where small ice bodies (<0.5 km2) are the majority. The results we obtained suggest that medium-resolution data are not suitable for a detailed description and evaluation of supraglacial debris cover in the Alpine scenario, nevertheless Sentinel 2 proved to be appropriate for a preliminary mapping of the main debris features.

scholarly journals Ten simple rules for working with high resolution remote sensing data

2021 ◽  
Author(s):  
Maxwell Benjamin Joseph ◽  
Anna Spiers ◽  
Michael J. Koontz ◽  
Nayani Ilangakoon ◽  
Kylen Solvik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Environmental Science ◽  
Ad Hoc ◽  
Remote Sensing Data ◽  
Trial And Error ◽  
High Resolution Data ◽  
Sensing Data ◽  
Simple Rules ◽  
Resolution Data

Researchers in Earth and environmental science can extract incredible value from high resolution remote sensing data, but these data can be hard to use. Pain free use requires skills from remote sensing and the data sciences that are seldom taught together. In practice, many researchers teach themselves how to use high resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. Here we outline ten “rules” with examples from Earth and environmental science to help applied researchers work more effectively with high resolution data.

scholarly journals Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring

2019 ◽  
Author(s):  
Xiaowei Jia ◽  
Mengdie Wang ◽  
Ankush Khandelwal ◽  
Anuj Karpatne ◽  
Vipin Kumar
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Temporal Patterns ◽  
Small Scale ◽  
High Resolution Data ◽  
Sensing Data ◽  
Coarse Resolution ◽  
Sequential Models ◽  
Resolution Data

Effective and timely monitoring of croplands is critical for managing food supply. While remote sensing data from earth-observing satellites can be used to monitor croplands over large regions, this task is challenging for small-scale croplands as they cannot be captured precisely using coarse-resolution data. On the other hand, the remote sensing data in higher resolution are collected less frequently and contain missing or disturbed data. Hence, traditional sequential models cannot be directly applied on high-resolution data to extract temporal patterns, which are essential to identify crops. In this work, we propose a generative model to combine multi-scale remote sensing data to detect croplands at high resolution. During the learning process, we leverage the temporal patterns learned from coarse-resolution data to generate missing high-resolution data. Additionally, the proposed model can track classification confidence in real time and potentially lead to an early detection. The evaluation in an intensively cultivated region demonstrates the effectiveness of the proposed method in cropland detection.

scholarly journals Long-term analysis of irrational water use processes based on cartographic materials and remote sensing data

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Andrey Medvedev ◽  
Natalia Alekseenko ◽  
Natalia Telnova ◽  
Alexander Koshkarev
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Water Level ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Topographic Maps ◽  
High Resolution Data ◽  
Sensing Data ◽  
The Past ◽  
Resolution Data

<p><strong>Abstract.</strong> Assessment and monitoring of environmental features based on large-scale and ultra-high resolution data, including remote sensing data, which have advantages in the repeatability of information and the speed of processing of incoming data, often face issues of completeness and duration of time series in retrospective analysis. Cartographic materials and remote sensing data allow monitoring for rapidly changing natural and anthropogenic features in the study areas, but very often face a problem when an event or phenomenon occurred many years ago and it is necessary to make a complete chronology.</p><p>Ultra-high-resolution data, remote sensing data and the results of the subsequent geoinformation analysis are widely used to solve problems in a number of socio-economic areas of territorial development, in particular:</p><ul><li>in environmental studies &amp;ndash; identification of local sources of water pollution, the consequences of their impact onecosystems, synthetic assessment of the ecological state of the territories and their comfort;</li><li>in the management of various resources, including water &amp;ndash; determination of biological productivity of water bodies, identification of water bioresources, detection of anthropogenically provoked and natural changes in water mass,implementation for glaciological studies, etc.</li></ul><p>Within the framework of the current study, a multi-time analysis of the water area and the coastal strip of Lake Sevan (the Republic of Armenia) at an altitude of about 1900 m above sea level, was carried out. The lake has repeatedly beensubjected to changes in the water level of the reservoir in the past. The 1930s and in the period between 1949 to 1962 were noted by the most intense drop in water level (more than 10 meters). In the 1990s, there was a slight increase inthe level, and then until 2001, the level of the lake continued to decrease.</p><p>The main factors affecting aquatic ecosystems and the overall ecological status of the lake are:</p><ol><li>Repeated changes in the water level of the reservoir in the past and its expected fluctuations in the future.</li><li>The uncontrolled discharge of harmful substances caused great damage to the lake, which affected the water qualityand biodiversity of this unique natural site.</li><li>Untimely cleaning of flooded forests, which increases the risk of eutrophication of the lake.</li><li>The poorly organized system of waste disposal and unauthorized landfills of municipal solid waste, as well as animalwaste.</li><li>Unauthorized construction of recreational facilities and capital structures in the coastal and water protection zonewhich may be flooded.</li></ol><p> The information support of the study is based on the materials of satellite imagery from the worldview2, SPOT 5/6,Resurs-P, Canopus-B, materials from the international space station (ISS), materials of archival aerial photography anddata obtained from the UAVs, in combination with other map data sources in the range of scales 1&amp;thinsp;:&amp;thinsp;5&amp;thinsp;000 &amp;ndash; 1&amp;thinsp;:&amp;thinsp;100&amp;thinsp;000,including digital topographic maps, land use maps, statistical and literary data. In fact, cartographic materials andremote sensing data provide a time history of 75 years, from large-scale topographic maps of 1942&amp;ndash;1943 to highlydetailed images of 2017&amp;ndash;2018.</p><p>According to the results of the study, it was possible to establish the position of the coastline for different time periods.The period between 1949 and 1962, when there was the most critical drop in the water level, was especially interestingand had not been studied before. Archival aerial photographs for 1943 and 1963 allowed to reconstruct the position ofthe coastline for almost every year of irrational water use.</p>

scholarly journals Performance evaluation of super resolution algorithms in generating high resolution images using MSE and PSNR

2021 ◽  
Vol 10 (02) ◽  
pp. 25284-25291
Author(s):  
Palani Murugan ◽  
Vivek Kumar Gautam ◽  
V. Ramanathan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Optical Systems ◽  
High Resolution Data ◽  
Sensing Data ◽  
Resolution Data

In recent days, requirement of high spatial resolution remote sensing data in various fields has increased tremendously.  High resolution satellite remote sensing data is obtained with long focal length optical systems and low altitude. As fabrication of high-resolution optical system and accommodating on the satellite is a challenging task, various alternate methods are being explored to get high resolution imageries. Alternately the high-resolution data can be obtained from super resolution techniques. The super resolution technique uses single or multiple low-resolution mis-registered data sets to generate high resolution data set.  Various algorithms are employed in super resolution technique to derive high spatial resolution. In this paper we have compared two methods namely overlapping and interleaving methods and their capability in generating high resolution data are presented.

scholarly journals Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3

2021 ◽  
Vol 13 (9) ◽  
pp. 1754
Author(s):  
Jonathan Reith ◽  
Gohar Ghazaryan ◽  
Francis Muthoni ◽  
Olena Dubovyk
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
High Resolution ◽  
Land Degradation ◽  
Management Practices ◽  
Sustainable Development Goal ◽  
Development Goal ◽  
High Resolution Data ◽  
Medium Resolution ◽  
Resolution Data

Monitoring land degradation (LD) to improve the measurement of the sustainable development goal (SDG) 15.3.1 indicator (“proportion of land that is degraded over a total land area”) is key to ensure a more sustainable future. Current frameworks rely on default medium-resolution remote sensing datasets available to assess LD and cannot identify subtle changes at the sub-national scale. This study is the first to adapt local datasets in interplay with high-resolution imagery to monitor the extent of LD in the semiarid Kiteto and Kongwa (KK) districts of Tanzania from 2000–2019. It incorporates freely available datasets such as Landsat time series and customized land cover and uses open-source software and cloud-computing. Further, we compared our results of the LD assessment based on the adopted high-resolution data and methodology (AM) with the default medium-resolution data and methodology (DM) suggested by the United Nations Convention to Combat Desertification. According to AM, 16% of the area in KK districts was degraded during 2000–2015, whereas DM revealed total LD on 70% of the area. Furthermore, based on the AM, overall, 27% of the land was degraded from 2000–2019. To achieve LD neutrality until 2030, spatial planning should focus on hotspot areas and implement sustainable land management practices based on these fine resolution results.

scholarly journals Semantic Segmentation on Remotely-Sensed Images Using Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning

2018 ◽  
Author(s):  
Teerapong Panboonyuen ◽  
Kulsawasd Jitkajornwanich ◽  
Siam Lawawirojwong ◽  
Panu Srestasathiern ◽  
Peerapon Vateekul
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Remotely Sensed ◽  
Convolutional Network ◽  
Domain Specific ◽  
Medium Resolution ◽  
Remotely Sensed Images ◽  
Very High ◽  
Resolution Data

In remote sensing domain, it is crucial to automatically annotate semantics, e.g., river, building, forest, etc, on the raster images. Deep Convolutional Encoder Decoder (DCED) network is the state-of-the-art semantic segmentation for remotely-sensed images. However, the accuracy is still limited, since the network is not designed for remotely sensed images and the training data in this domain is deficient. In this paper, we aim to propose a novel CNN network for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose to apply a recent CNN network call ''Global Convolutional Network (GCN)'', since it can capture different resolutions by extracting multi-scale features from different stages of the network. Also, we further enhance the network by improving its backbone using larger numbers of layers, which is suitable for medium resolution remotely sensed images. Second, ''Channel Attention'' is presented into our network in order to select most discriminative filters (features). Third, ''Domain Specific Transfer Learning'' is introduced to alleviate the scarcity issue by utilizing other remotely sensed corpora with different resolutions as pre-trained data. The experiment was then conducted on two given data sets: ($i$) medium resolution data collected from Landsat-8 satellite and ($ii$) very high resolution data called ''ISPRS Vaihingen Challenge Data Set''. The results show that our networks outperformed DCED in terms of $F1$ for 17.48% and 2.49% on medium and very high resolution corpora, respectively.

scholarly journals On the accuracy of glacier outlines derived from remote-sensing data

2013 ◽  
Vol 54 (63) ◽  
pp. 171-182 ◽  
Author(s):  
F. Paul ◽  
N.E. Barrand ◽  
S. Baumann ◽  
E. Berthier ◽  
T. Bolch ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Reference Value ◽  
Remote Sensing Data ◽  
Automated Classification ◽  
Automated Mapping ◽  
Sensing Data ◽  
Medium Resolution ◽  
Debris Cover ◽  
Manual Correction

AbstractDeriving glacier outlines from satellite data has become increasingly popular in the past decade. In particular when glacier outlines are used as a base for change assessment, it is important to know how accurate they are. Calculating the accuracy correctly is challenging, as appropriate reference data (e.g. from higher-resolution sensors) are seldom available. Moreover, after the required manual correction of the raw outlines (e.g. for debris cover), such a comparison would only reveal the accuracy of the analyst rather than of the algorithm applied. Here we compare outlines for clean and debris-covered glaciers, as derived from single and multiple digitizing by different or the same analysts on very high- (1 m) and medium-resolution (30 m) remote-sensing data, against each other and to glacier outlines derived from automated classification of Landsat Thematic Mapper data. Results show a high variability in the interpretation of debris-covered glacier parts, largely independent of the spatial resolution (area differences were up to 30%), and an overall good agreement for clean ice with sufficient contrast to the surrounding terrain (differences ∼5%). The differences of the automatically derived outlines from a reference value are as small as the standard deviation of the manual digitizations from several analysts. Based on these results, we conclude that automated mapping of clean ice is preferable to manual digitization and recommend using the latter method only for required corrections of incorrectly mapped glacier parts (e.g. debris cover, shadow).

scholarly journals Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery

2019 ◽  
Vol 33 ◽  
pp. 702-709 ◽  
Author(s):  
Tim G. J. Rudner ◽  
Marc Rußwurm ◽  
Jakub Fil ◽  
Ramona Pelich ◽  
Benjamin Bischke ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Spatial Information ◽  
High Resolution Data ◽  
Assistance Programs ◽  
Spatiotemporal Information ◽  
Novel Approach ◽  
Medium Resolution ◽  
Building Footprint ◽  
Resolution Data

We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our model significantly expedites the generation of satellite imagery-based flood maps, crucial for first responders and local authorities in the early stages of flood events. By incorporating multitemporal satellite imagery, our model allows for rapid and accurate post-disaster damage assessment and can be used by governments to better coordinate medium- and long-term financial assistance programs for affected areas. The network consists of multiple streams of encoder-decoder architectures that extract spatiotemporal information from medium-resolution images and spatial information from high-resolution images before fusing the resulting representations into a single medium-resolution segmentation map of flooded buildings. We compare our model to state-of-the-art methods for building footprint segmentation as well as to alternative fusion approaches for the segmentation of flooded buildings and find that our model performs best on both tasks. We also demonstrate that our model produces highly accurate segmentation maps of flooded buildings using only publicly available medium-resolution data instead of significantly more detailed but sparsely available very high-resolution data. We release the first open-source dataset of fully preprocessed and labeled multiresolution, multispectral, and multitemporal satellite images of disaster sites along with our source code.

scholarly journals EXTRACTION AND ANALYSIS OF TRADITIONAL CHINESE MEDICINE CROPS BASED ON MULTITEMPORAL HIGH RESOLUTION DATA-TAKING QIAOCHENG DISTRICT OF BOZHOU AS AN EXAMPLE

2018 ◽  
Vol XLII-3 ◽  
pp. 2153-2160
Author(s):  
H. Yu ◽  
J. He ◽  
H. Zhou ◽  
F. Guan ◽  
L. Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chinese Medicine ◽  
High Resolution ◽  
Traditional Chinese Medicine ◽  
High Resolution Data ◽  
Important Method ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Crop Planting ◽  
Resolution Data

Remote sensing technology has become an important method to rapidly acquireing of planting layout and composition of regional crops.It is very important to accurately master the planting area of Chinese medicine crops in the Characteristic planting area because it relations to accurately master the cultivation of Chinese medicine crops, formulate related policies and adjustment of crop planting structure.The author puts forward a method of using remote sencing technology for momitoring Chinese medicine which has good applicability and generalization. This paper took Qiaocheng District of Bozhou as an example to Verify the feasibility of the method, providing a reference for solving the problem of interpretation and extraction of Chinese medicinal materials in the region.

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