Modification of a groundwater prospect zone index using remotely sensed data and the analytic network process in the eastern part of Lesvos island, Greece.

2020 ◽  
Vol 11 (4) ◽  
pp. 126-143
Author(s):  
Terpsichori MITSI ◽  
◽  
Demetre ARGIALAS ◽  
Konstantinos VAMVOUKAKIS ◽  
◽  
...  
Keyword(s):  
Ground Water ◽  
Analytic Network Process ◽  
Remotely Sensed ◽  
Groundwater Potential ◽  
Landsat 8 ◽  
Groundwater Prospect ◽  
Remotely Sensed Data ◽  
Potential Index ◽  
Network Process ◽  
Sentinel 2

Because of climate change and overpopulation, the demand for water is increasing. Groundwater constitutes an alternative renewable source of aquifer, so the spatial distribution of ground water provides important information on its qualitative and quantitative status. This paper develops a methodology for delineating potential ground water zones using remotely sensed data and GIS. The developed methodology was based on the empirical index GPI (MGPI – Modified Groundwater Potential Index) and was applied to the eastern part of Lesvos Island, Greece. To evaluate the criteria used for the result, the Analytic Network Process (ANP) was applied to weight each parameter. The dataset used consists of satellite images derived from Sentinel 2 and Landsat 8, which were combined with vector and raster data, to create the necessary thematic layers. To validate the results, existing ground water zones from the Municipal Water Company of Lesvos were used.

scholarly journals Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 279 ◽  
Author(s):  
Ernest William Mauya ◽  
Joni Koskinen ◽  
Katri Tegel ◽  
Jarno Hämäläinen ◽  
Tuomo Kauranne ◽  
...  
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Remotely Sensed ◽  
Small Scale ◽  
Remotely Sensed Data ◽  
Weighted Mean ◽  
Alos Palsar ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

scholarly journals Prediction of hookworm prevalence in southern India using environmental parameters derived from Landsat 8 remotely sensed data

2020 ◽  
Vol 50 (1) ◽  
pp. 47-54
Author(s):  
Alexandra V. Kulinkina ◽  
Rajiv Sarkar ◽  
Venkata R. Mohan ◽  
Yvonne Walz ◽  
Saravanakumar P. Kaliappan ◽  
...  
Keyword(s):  
Environmental Parameters ◽  
Remotely Sensed ◽  
Southern India ◽  
Landsat 8 ◽  
Remotely Sensed Data

scholarly journals Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia)

2021 ◽  
Vol 14 (21) ◽  
Author(s):  
Gáspár Albert ◽  
Seif Ammar
Keyword(s):  
Random Forest ◽  
Remotely Sensed ◽  
Geological Mapping ◽  
Vegetation Coverage ◽  
Remotely Sensed Data ◽  
Random Forest Classification ◽  
Radar Images ◽  
Forest Classification ◽  
Geological Map ◽  
Sentinel 2

Abstract Remotely sensed data such as satellite photos and radar images can be used to produce geological maps on arid regions, where the vegetation coverage does not have a significant effect. In central Tunisia, the Jebel Meloussi area has unique geological features and characteristic morphology (i.e. flat areas with dune fields in contrast with hills of folded and eroded stratigraphic sequences), which makes it an ideal area for testing new methods of automatic terrain classification. For this, data from the Sentinel 2 satellite sensor and the SRTM-based MERIT DEM (digital elevation model) were used in the present study. Using R scripts and the random forest classification method, modelling was performed on four lithological variables—derived from the different bands of the Sentinel 2 images—and two morphometric parameters for the area of the 1:50,000 geological map sheet no. 103. The four lithological variables were chosen to highlight the iron-bearing minerals since the spectral parameters of the Sentinel 2 sensors are especially useful for this purpose. The training areas of the classification were selected on the geological map. The results of the modelling identified Eocene and Cretaceous evaporite-bearing sedimentary series (such as the Jebs and the Bouhedma Formations) with the highest producer accuracy (> 60% of the predicted pixels match with the map). The pyritic argillites of the Sidi Khalif Formation were also recognized with the same accuracy, and the Quaternary sebhkas and dunes were also well predicted. The study concludes that the classification-based geological map is useful for field geologist prior to field surveys.

scholarly journals Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning

2021 ◽  
Vol 13 (16) ◽  
pp. 3166
Author(s):  
Jash R. Parekh ◽  
Ate Poortinga ◽  
Biplov Bhandari ◽  
Timothy Mayer ◽  
David Saah ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Mean Squared Error ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Remotely Sensed Data ◽  
Statistical Indices ◽  
Deep Learning Neural Network

The large scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such as the Landsat and Sentinel series. More recently, researchers have explored classification and regression techniques to model impervious surfaces. However, these modeling efforts are limited due to lack of labeled data for training and evaluation. This in turn requires significant effort for manual labeling of data and visual interpretation of results. In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks. The four model configurations labeled U-Net_SGD_Bands, U-Net_Adam_Bands, U-Net_Adam_Bands+SI, and VGG-19_Adam_Bands+SI resulted in a root mean squared error (RMSE) of 0.1582, 0.1358, 0.1375, and 0.1582 and an accuracy of 90.87%, 92.28%, 92.46%, and 90.11%, respectively, on the test set. The U-Net_Adam_Bands+SI Model, similar to the others mentioned above, is a deep learning neural network that combines Landsat 8 bands with statistical indices. This model performs the best among all four on statistical accuracy and produces qualitatively sharper and brighter predictions of impervious surfaces as compared to the other models.

scholarly journals Spark Sensing: A Cloud Computing Framework to Unfold Processing Efficiencies for Large and Multiscale Remotely Sensed Data, with Examples on Landsat 8 and MODIS Data

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Hai Lan ◽  
Xinshi Zheng ◽  
Paul M. Torrens
Keyword(s):  
Time Series ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Environmental Sciences ◽  
Modis Data ◽  
The Earth ◽  
Data Provision ◽  
Using Data ◽  
Local Cloud

Inquiry using data from remote Earth-observing platforms often confronts a straightforward but particularly thorny problem: huge amounts of data, in ever-replenishing supplies, are available to support inquiry, but scientists’ agility in converting data into actionable information often struggles to keep pace with rapidly incoming streams of data that amass in expanding archival silos. Abstraction of those data is a convenient response, and many studies informed purely by remotely sensed data are by necessity limited to a small study area with a relatively few scenes of imagery, or they rely on larger mosaics of images at low resolution. As a result, it is often challenging to thread explanations across scales from the local to the global, even though doing so is often critical to the science under pursuit. Here, a solution is proposed, by exploiting Apache Spark, to implement parallel, in-memory image processing with ability to rapidly classify large volumes of multiscale remotely sensed images and to perform necessary analysis to detect changes on the time series. It shows that processing on three different scales of Landsat 8 data (up to ~107.4 GB, five-scene, time series image sets) can be accomplished in 1018 seconds on local cloud environment. Applying the same framework with slight parameter adjustments, it processed same coverage MODIS data in 54 seconds on commercial cloud platform. Theoretically, the proposed scheme can handle all forms of remote sensing imagery commonly used in the Earth and environmental sciences, requiring only minor adjustments in parameterization of the computing jobs to adjust to the data. The authors suggest that the “Spark sensing” approach could provide the flexibility, extensibility, and accessibility necessary to keep inquiry in the Earth and environmental sciences at pace with developments in data provision.

GIS-based Multi-Criteria Analysis for Delineation of Groundwater Potential Zones

2020 ◽  
pp. 24-43
Author(s):  
Raghib Raza ◽  
Gajendra Kumar Chawla ◽  
Chandra Shekhar Dwivedi
Keyword(s):  
Ground Water ◽  
Water Potential ◽  
Groundwater Potential ◽  
Potential Zone ◽  
Groundwater Potential Zones ◽  
Analytic Hierarchy ◽  
Raster Calculator ◽  
Geospatial Technique ◽  
Network Process ◽  
Complete Study

In the present study, delineation of ground water potential in Jodhpur district, Rajasthan is framed by using geospatial technique (i.e., remote sensing and GIS) and with multi-criteria leadership (MCDM) procedure. The analytical network process (ANP) is one of the suitable strategies that makes the research workable for delineation methodically, and corporate the analytic hierarchy process (AHP) as an uncommon case. For proper value to recognize the ground water potential zone in Jodhpur, Rajasthan, the AHP and ANP methods are utilized to decide the values of different parameters and their classes. The AHP values are then connected in a direct mix into raster calculator to get five distinctive groundwater potential zones in the investigation region, to be specific as ‘very poor' (2052.0 km2), ‘poor' (4225.9 km2), ‘moderate' (6355.1 km2), ‘good' (6451.2 km2), and ‘very good' (3301.7 km2). It has been presumed that about 9752.9 km2 area of Jodhpur district has very good to good groundwater potential, which is about 43.56% of the complete study area.

Remote sensing of grassland communities in Mongolian Steppe combining multi-source data and machine learning classification algorithms

2020 ◽  
Author(s):  
Thanh Noi Phan ◽  
Yun Jäschke ◽  
Oyundari Chuluunkhuyag ◽  
Munkhzul Oyunbileg ◽  
Karsten Wesche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Training Sample ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Machine Learning Classification ◽  
Steppe Region ◽  
Training Samples ◽  
Grassland Communities ◽  
Sentinel 2

<p>In this study, we investigate the performance of machine learning classification approaches and different remotely sensed data sources for identifying and mapping three types of grassland communities found in the Mongolian Steppe region (Artemisia, Caragana and grass dominated steppes). The Mongolian steppe is intensively used as pasture and provides the economic basis for approximately 1 million herders. The grassland types differ in their forage values, which is why a spatially-explicit estimation of their occurrence is of high importance. We compared different sensors, classifiers, and training-sample strategies to identify the most effective approaches for mapping these communities. Ten datasets were used: Landsat 8 OLI (30 m), pan-sharpened Landsat 8 (15 m), Landsat 8 Surface Reflectance (30 m), Sentinel 2 (10 m), Sentinel 2 (20 m), Worldview 3 (0.5 m and 1.2 m), integrated Landsat 8 and Sentinel 2 (30 m), temporal Landsat 8, and temporal Sentinel 2. The two foremost classifiers at producing high accuracy of land cover classification, SVM and RF, were applied with the same training datasets. The training samples were collected in a manner so that they could be used for different spatial resolutions (i.e., ranging from 0.5 to 30 m) with the least effect from mixed training samples and spatial autocorrelation. The results of this study indicate that remote sensing is a viable method for the identification of different grassland communities in the Mongolian Steppe region.</p><p> </p>

scholarly journals Sentinel-2 Data for Land Cover/Use Mapping: A Review

2020 ◽  
Vol 12 (14) ◽  
pp. 2291 ◽  
Author(s):  
Darius Phiri ◽  
Matamyo Simwanda ◽  
Serajis Salekin ◽  
Vincent R. Nyirenda ◽  
Yuji Murayama ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Positive Impact ◽  
European Space Agency ◽  
Remotely Sensed ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Free Access ◽  
Remotely Sensed Data ◽  
Sentinel 2

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

scholarly journals Assimilation of Remotely-Sensed LAI into WOFOST Model with the SUBPLEX Algorithm for Improving the Field-Scale Jujube Yield Forecasts

2019 ◽  
Vol 11 (16) ◽  
pp. 1945
Author(s):  
Tiecheng Bai ◽  
Shanggui Wang ◽  
Wenbo Meng ◽  
Nannan Zhang ◽  
Tao Wang ◽  
...  
Keyword(s):  
Developmental Stages ◽  
Vegetation Indices ◽  
Remotely Sensed ◽  
High Yield ◽  
Bias Error ◽  
Growth Stages ◽  
Landsat 8 ◽  
Statistical Regression ◽  
Field Scale ◽  
Remotely Sensed Data

In order to enhance the simulated accuracy of jujube yields at the field scale, this study attempted to employ SUBPLEX algorithm to assimilate remotely sensed leaf area indices (LAI) of four key growth stages into a calibrated World Food Studies (WOFOST) model, and compare the accuracy of assimilation with the usual ensemble Kalman filter (EnKF) assimilation. Statistical regression models of LAI and Landsat 8 vegetation indices at different developmental stages were established, showing a validated R2 of 0.770, 0.841, 0.779, and 0.812, and a validated RMSE of 0.061, 0.144, 0.180, and 0.170 m2 m−2 for emergence, fruit filling, white maturity, and red maturity periods. The results showed that both SUBPLEX and EnKF assimilations significantly improved yield estimation performance compared with un-assimilated simulation. The SUBPLEX (R2 = 0.78 and RMSE = 0.64 t ha−1) also showed slightly better yield prediction accuracy compared with EnKF assimilation (R2 = 0.73 and RMSE = 0.71 t ha−1), especially for high-yield and low-yield jujube orchards. SUBPLEX assimilation produced a relative bias error (RBE, %) that was more concentrated near zero, being lower than 10% in 80.1%, and lower than 20% in 96.1% for SUBPLEX, 72.4% and 96.7% for EnKF, respectively. The study provided a new assimilation scheme based on SUBPLEX algorithm to employ remotely sensed data and a crop growth model to improve the field-scale fruit crops yield estimates.

Sign in / Sign up

Export Citation Format

Share Document