scholarly journals Assessment of Coastal Aquaculture for India from Sentinel-1 SAR Time Series

2019 ◽  
Vol 11 (3) ◽  
pp. 357 ◽  
Author(s):  
Kumar Prasad ◽  
Marco Ottinger ◽  
Chunzhu Wei ◽  
Patrick Leinenkugel
Keyword(s):  
Time Series ◽  
Coastal Zone ◽  
Accuracy Assessment ◽  
Economic Value ◽  
Global Scale ◽  
Google Earth ◽  
Shape Information ◽  
Shuttle Radar Topographic Mission ◽  
Sar Data ◽  
Spatio Temporal

Aquaculture is one of the fastest growing primary food production sectors in India and ranks second behind China. Due to its growing economic value and global demand, India’s aquaculture industry experienced exponential growth for more than one decade. In this study, we extract land-based aquaculture at the pond level for the entire coastal zone of India using large-volume time series Sentinel-1 synthetic-aperture radar (SAR) data at 10-m spatial resolution. Elevation and slope from Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) data were used for masking inappropriate areas, whereas a coastline dataset was used to create a land/ocean mask. The pixel-wise temporal median was calculated from all available Sentinel-1 data to significantly reduce the amount of noise in the SAR data and to reduce confusions with temporary inundated rice fields. More than 3000 aquaculture pond vector samples were collected from high-resolution Google Earth imagery and used in an object-based image classification approach to exploit the characteristic shape information of aquaculture ponds. An open-source connected component segmentation algorithm was used for the extraction of the ponds based on the difference in backscatter intensity of inundated surfaces and shape metrics calculated from aquaculture samples as input parameters. This study, for the first time, provides spatial explicit information on aquaculture distribution at the pond level for the entire coastal zone of India. Quantitative spatial analyses were performed to identify the provincial dominance in aquaculture production, such as that revealed in Andhra Pradesh and Gujarat provinces. For accuracy assessment, 2000 random samples were generated based on a stratified random sampling method. The study demonstrates, with an overall accuracy of 0.89, the spatio-temporal transferability of the methodological framework and the high potential for a global-scale application.

scholarly journals Spatio-temporal distribution of malaria one year after the implementation of additional preventive strategies in Bandiagara, Mali

2020 ◽  
Author(s):  
Drissa Coulibaly ◽  
Boureima Guindo ◽  
Fayçal Maiga ◽  
Salimata Konate ◽  
Astou Diallo ◽  
...  
Keyword(s):  
Time Series ◽  
High Risk ◽  
Clinical Malaria ◽  
Google Earth ◽  
Low Risk ◽  
Geographical Information ◽  
Asymptomatic Malaria ◽  
Entire Study ◽  
North East ◽  
Spatio Temporal

Abstract BackgroundEvaluation of local transmission epidemiology to characterize malaria risk is essential for planning malaria control and elimination programmes. The use of geographical information systems (GIS) has been a major asset to this approach. This study aimed to characterize the local spatio-temporal pattern of malaria infection and clinical disease after implementation of Seasonal Malaria Chemoprevention (SMC) and Indoor Residual Spraying (IRS) in Bandiagara, a Malian town.MethodsFrom October 2017 to December 2018, an active and passive surveillance system was established in a cohort study of three hundred children aged from 6 months to 15 years old. Weekly time-series of clinical malaria and monthly time-series of asymptomatic Plasmodium carriage and rainfall were plotted. Numbers of clinical malaria episodes and asymptomatic parasite carriers were mapped using Quantum Geographic Information System (QGIS). Landscape features of Bandiagara were obtained from Google earth. Clusters of high or low risk were identified under SaTScan® software using a Bernoulli model. ResultsFrom October 2017 to December 2018, 167 clinical malaria cases were recorded, mostly from July to December, while asymptomatic parasite carriage was observed during the entire study period. Three clusters of clinical episodes were found. All were hotspots. They were located in the north-east, south and west. No low risk cluster was identified. Three significant high-risk clusters of asymptomatic parasite carriage were identified in the south, north-east and north. These clusters were located near standing water. ConclusionThis study confirms the seasonality of clinical malaria in Bandiagara. The continued presence of asymptomatic parasite carriers maintains malaria transmission. To advance malaria elimination, control strategies must also target hotspots of asymptomatic parasite carriage. There was a spatial heterogeneity of clinical and asymptomatic malaria. Despite the implementation of additional preventives strategies, the locations of high-risk clusters were stable.

scholarly journals Mapping Paddy Fields in Japan by Using a Sentinel-1 SAR Time Series Supplemented by Sentinel-2 Images on Google Earth Engine

2020 ◽  
Vol 12 (10) ◽  
pp. 1622 ◽  
Author(s):  
Shimpei Inoue ◽  
Akihiko Ito ◽  
Chinatsu Yonezawa
Keyword(s):  
Time Series ◽  
Paddy Field ◽  
Water Resource Management ◽  
Mapping Method ◽  
Google Earth ◽  
Paddy Fields ◽  
Optical Images ◽  
Sar Data ◽  
Decision Tree Method ◽  
Sentinel 2

Paddy fields play very important environmental roles in food security, water resource management, biodiversity conservation, and climate change. Therefore, reliable broad-scale paddy field maps are essential for understanding these issues related to rice and paddy fields. Here, we propose a novel paddy field mapping method that uses Sentinel-1 synthetic aperture radar (SAR) time series that are robust for cloud cover, supplemented by Sentinel-2 optical images that are more reliable than SAR data for extracting irrigated paddy fields. Paddy fields were provisionally specified by using the Sentinel-1 SAR data and a conventional decision tree method. Then, an additional mask using water and vegetation indexes based on Sentinel-2 optical images was overlaid to remove non-paddy field areas. We used the proposed method to develop a paddy field map for Japan in 2018 with a 30 m spatial resolution. The producer’s accuracy of this map (92.4%) for non-paddy reference agricultural fields was much higher than that of a map developed by the conventional method (57.0%) using only Sentinel-1 data. Our proposed method also reproduced paddy field areas at the prefecture scale better than existing paddy field maps developed by a remote sensing approach.

scholarly journals Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

2019 ◽  
Vol 11 (10) ◽  
pp. 1235 ◽  
Author(s):  
Aaron M. Shew ◽  
Aniruddha Ghosh
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Accuracy Assessment ◽  
Vegetation Indices ◽  
National Level ◽  
Google Earth ◽  
Dry Season ◽  
Future Research ◽  
Cropping Patterns

In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise, and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes.

scholarly journals Identificación de áreas quemadas mediante el análisis de series de tiempo en el ámbito de computación en la nube

2018 ◽  
pp. 61 ◽  
Author(s):  
J.A. Anaya ◽  
W.F. Sione ◽  
A.M. Rodriguez-Montellano
Keyword(s):  
Accuracy Assessment ◽  
Global Scale ◽  
Google Earth ◽  
Burned Area ◽  
Amazon Forest ◽  
Burned Areas ◽  
Use Of Time ◽  
Chaco Region ◽  
Relative Form ◽  
Normalized Burn Ratio

<p>There are large omission errors in the estimation of burned area in map products that are generated at a global scale. This error is then inherited by other models, for instance, those used to report Greenhouse Gas Emissions using a “bottom up” approach. This study evaluates temporal methods to improve burned area detection using Landsat 5-TM and 8-OLI. In this process, the normalized burn ratio (NBR) was used to highlight burned areas and thresholds to classify burned and non-burned areas. In order to maximize the burned area detection two alternatives to the temporal dNBR method were evaluated: the relative form of the temporal difference RdNBR and the use of time series metrics. The processing, algorithm development and access to Landsat data was made on the Google Earth Engine GEE platform. Three regions of Latin America with large fire occurrence were selected: The Amazon Forest in Colombia, the transition from Chiquitano to Amazon Forest in Bolivia, and El Chaco Region in Argentina. The accuracy assessment of these new products was based on burned area protocols. The best model classified 85% of burned areas in the Chiquitano Forests of Bolivia, 63% of the burned areas of the Amazon Forests of Colombia and 69% of burned areas in El Chaco of Argentina.</p>

scholarly journals On the spatio-temporal analysis of hydrological droughts from global hydrological models

2011 ◽  
Vol 8 (1) ◽  
pp. 619-652 ◽  
Author(s):  
G. A. Corzo Perez ◽  
M. H. J. van Huijgevoort ◽  
F. Voß ◽  
H. A. J. van Lanen
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Large Scale ◽  
Southern Oscillation ◽  
Regional Scale ◽  
Spatial Coherence ◽  
Temporal Analysis ◽  
Global Scale ◽  
Scale Models ◽  
Spatio Temporal

Abstract. The recent concerns for world-wide extreme events related to climate change phenomena have motivated the development of large scale models that simulate the global water cycle. In this context, analyses of extremes is an important topic that requires the adaptation of methods used for river basin and regional scale models. This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are distinguished and defined as non-contiguous and contiguous drought area analyses (i.e. NCDA and CDA). The NCDA presents time series of percentages of areas in drought at the global scale and for pre-defined regions of known hydroclimatology. The CDA is introduced as a complementary method that generates information on the spatial coherence of drought events at the global scale. Spatial drought events are found through CDA by clustering patterns (contiguous areas). In this study the global hydrological model WaterGAP was used to illustrate the methodology development. Global gridded time series (resolution 0.5°) simulated with the WaterGAP model from land points were used. The NCDA and CDA were applied to identify drought events in subsurface runoff. The percentages of area in drought calculated with both methods show complementary information on the spatial and temporal events for the last decades of the 20th century. The NCDA provides relevant information on the average number of droughts, duration and severity (deficit volume) for pre-defined regions (globe, 2 selected climate regions). Additionally, the CDA provides information on the number of spatially linked areas in drought as well as their geographic location on the globe. An explorative validation process shows that the NCDA results capture the overall spatio-temporal drought extremes over the last decades of the 20th century. Events like the El Niño Southern Oscillation (ENSO) in South America and the pan-European drought in 1976 appeared clearly in both analyses. The methodologies introduced provide an important basis for the global characterization of droughts, model inter-comparison, and spatial events validation.

scholarly journals Patch-Based Assessments of Shifting Cultivation Detected by Landsat Time Series Images in Myanmar

2018 ◽  
Vol 10 (9) ◽  
pp. 3350
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Shigejiro Yoshida
Keyword(s):  
Time Series ◽  
Shifting Cultivation ◽  
Accuracy Assessment ◽  
Google Earth ◽  
Visual Interpretation ◽  
High Resolution Satellite Images ◽  
And Function ◽  
The Tropics ◽  
Time Series Images

Shifting cultivation is a widely practiced agriculture system in the tropics. Regardless of the dominant land use, the dynamics of shifting cultivation over large areas are of limited knowledge. We conducted patch-based assessments and characterization of shifting cultivation extracted from already developed dataset, which detected shifting cultivation by a trajectory-based analysis using annual Landsat TM/ETM+/OLI time series images from 2000 to 2014 in Myanmar. An accuracy assessment was conducted in terms of the size and number of cleared areas compared with reference polygons of shifting cultivation, which were manually delineated by visual interpretation using Landsat and high-resolution satellite images from Google Earth™ in the selected areas. The producer’s and user’s accuracies in detecting the number of shifting cultivation patches were 78.1% and 88.4%, respectively. In whole study area, the probability of disturbances caused by shifting cultivation was significantly affected by distance to the nearest village, indicating the importance of accessibility from residences. The number of shifting cultivation patches showed a decreasing trend in this region and it will lead to less cleared forests such as located far from residences. These dynamics of shifting cultivation have possibility to affect the mosaic patterns of landscape and function maintained in the landscape in this region.

scholarly journals On the spatio-temporal analysis of hydrological droughts from global hydrological models

2011 ◽  
Vol 15 (9) ◽  
pp. 2963-2978 ◽  
Author(s):  
G. A. Corzo Perez ◽  
M. H. J. van Huijgevoort ◽  
F. Voß ◽  
H. A. J. van Lanen
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Large Scale ◽  
Southern Oscillation ◽  
Spatial Coherence ◽  
Temporal Analysis ◽  
Global Scale ◽  
Hydrological Models ◽  
Context Analysis ◽  
Spatio Temporal

Abstract. The recent concerns for world-wide extreme events related to climate change have motivated the development of large scale models that simulate the global water cycle. In this context, analysis of hydrological extremes is important and requires the adaptation of identification methods used for river basin models. This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are classified as non-contiguous and contiguous drought area analyses (i.e. NCDA and CDA). The NCDA presents time series of percentages of areas in drought at the global scale and for pre-defined regions of known hydroclimatology. The CDA is introduced as a complementary method that generates information on the spatial coherence of drought events at the global scale. Spatial drought events are found through CDA by clustering patterns (contiguous areas). In this study the global hydrological model WaterGAP was used to illustrate the methodology development. Global gridded time series of subsurface runoff (resolution 0.5°) simulated with the WaterGAP model from land points were used. The NCDA and CDA were developed to identify drought events in runoff. The percentages of area in drought calculated with both methods show complementary information on the spatial and temporal events for the last decades of the 20th century. The NCDA provides relevant information on the average number of droughts, duration and severity (deficit volume) for pre-defined regions (globe, 2 selected hydroclimatic regions). Additionally, the CDA provides information on the number of spatially linked areas in drought, maximum spatial event and their geographic location on the globe. Some results capture the overall spatio-temporal drought extremes over the last decades of the 20th century. Events like the El Niño Southern Oscillation (ENSO) in South America and the pan-European drought in 1976 appeared clearly in both analyses. The methodologies introduced provide an important basis for the global characterization of droughts, model inter-comparison of drought identified from global hydrological models and spatial event analyses.

scholarly journals Long Time-Series Mapping and Change Detection of Coastal Zone Land Use Based on Google Earth Engine and Multi-Source Data Fusion

2021 ◽  
Vol 14 (1) ◽  
pp. 1 ◽  
Author(s):  
Dong Chen ◽  
Yafei Wang ◽  
Zhenyu Shen ◽  
Jinfeng Liao ◽  
Jiezhi Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Change Detection ◽  
Coastal Zone ◽  
Google Earth ◽  
Coastal Zones ◽  
Long Time Series ◽  
Coastal Aquaculture ◽  
Long Time

Human activities along with climate change have unsustainably changed the land use in coastal zones. This has increased demands and challenges in mapping and change detection of coastal zone land use over long-term periods. Taking the Bohai rim coastal area of China as an example, in this study we proposed a method for the long time-series mapping and change detection of coastal zone land use based on Google Earth Engine (GEE) and multi-source data fusion. To fully consider the characteristics of the coastal zone, we established a land-use function classification system, consisting of cropland, coastal aquaculture ponds (saltern), urban land, rural settlement, other construction lands, forest, grassland, seawater, inland fresh-waters, tidal flats, and unused land. We then applied the random forest algorithm, the optimal classification method using spatial morphology and temporal change logic to map the long-term annual time series and detect changes in the Bohai rim coastal area from 1987 to 2020. Validation shows an overall acceptable average accuracy of 82.30% (76.70–85.60%). Results show that cropland in this region decreased sharply from 1987 (53.97%) to 2020 (37.41%). The lost cropland was mainly transformed into rural settlements, cities, and construction land (port infrastructure). We observed a continuous increase in the reclamation with a stable increase at the beginning followed by a rapid increase from 2003 and a stable intermediate level increase from 2013. We also observed a significant increase in coastal aquaculture ponds (saltern) starting from 1995. Through this case study, we demonstrated the strength of the proposed methods for long time-series mapping and change detection for coastal zones, and these methods support the sustainable monitoring and management of the coastal zone.

scholarly journals Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China’s Eastern Coastal Zone circa 2015

2019 ◽  
Vol 11 (8) ◽  
pp. 924 ◽  
Author(s):  
Zhang ◽  
Dong ◽  
Liu ◽  
Gao ◽  
Hu ◽  
...  
Keyword(s):  
Time Series ◽  
Coastal Zone ◽  
Tidal Flat ◽  
Large Scale ◽  
Tidal Flats ◽  
Google Earth ◽  
Landsat 8 ◽  
Intertidal Flats ◽  
Multi Temporal ◽  
Google Earth Engine

Accurate and up-to-date tidal flat mapping is of much importance to learning how coastal ecosystems work in a time of anthropogenic disturbances and rising sea levels, which will provide scientific instruction for sustainable management and ecological assessments. For large-scale and high spatial-resolution mapping of tidal flats, it is difficult to obtain accurate tidal flat maps without multi-temporal observation data. In this study, we aim to investigate the potential and advantages of the freely accessible Landsat 8 Operational Land Imager (OLI) imagery archive and Google Earth Engine (GEE) for accurate tidal flats mapping. A novel approach was proposed, including multi-temporal feature extraction, machine learning classification using GEE and morphological post-processing. The 50 km buffer of the coastline from Hangzhou Bay to Yalu River in China’s eastern coastal zone was taken as the study area. From the perspective of natural attributes and unexploited status of tidal flats, we delineated a broader extent comprising intertidal flats, supratidal barren flats and vegetated flats, since intertidal flats are major component of the tidal flats. The overall accuracy of the resultant map was about 94.4% from a confusion matrix for accuracy assessment. The results showed that the use of time-series images can greatly eliminate the effects of tidal level, and improve the mapping accuracy. This study also proved the potential and advantage of combining the GEE platform with time-series Landsat images, due to its powerful cloud computing platform, especially for large scale and longtime tidal flats mapping.

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