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.

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 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 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.

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 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 for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose to apply a recent CNN 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 Combining Data from Multiple Sources to Evaluate Spatial Variations in the Economic Costs of PM2.5-Related Health Conditions in the Beijing–Tianjin–Hebei Region

2019 ◽  
Vol 16 (20) ◽  
pp. 3994 ◽  
Author(s):  
Zhang ◽  
Hu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Population Density ◽  
Health Outcomes ◽  
Economic Costs ◽  
High Resolution Data ◽  
Geographical Variations ◽  
Combining Data ◽  
Bth Region ◽  
Resolution Data

Fine particulate matter, known as PM2.5, is closely related to a range of adverse health outcomes and ultimately imposes a high economic cost on the society. While we know that the costs associated with PM2.5-related health outcomes are not uniform geographically, a few researchers have considered the geographical variations in these costs because of a lack of high-resolution data for PM2.5 and population density. Satellite remote sensing provides highly precise, high-resolution data about how PM2.5 and population density vary spatially, which can be used to support detailed health-related assessments. In this study, we used high-resolution PM2.5 concentration and population density based on remote sensing data to assess the effects of PM2.5 on human health and the related economic costs in the Beijing–Tianjin–Hebei (BTH) region in 2016 using exposure-response functions and the relationship between health and economic costs. The results showed that the PM2.5-related economic costs were unevenly distributed and as with the population density, the costs were mainly concentrated in urban areas. In 2016, the economic costs of PM2.5-related health endpoints amounted to 4.47% of the total gross domestic product in the BTH region. Of the health endpoints, the cost incurred by premature deaths accounted for more than 80% of the total economic costs associated with PM2.5. The results of this study provide new and detailed information that could be used to support the implementation of national and regional policies to reduce air pollution.

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