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

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.

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

Remote Sensing ◽

10.3390/rs13163166 ◽

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.

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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

Journal of Sensors ◽

10.1155/2018/2075057 ◽

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.

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

Remote Sensing ◽

10.3390/rs11161945 ◽

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.

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Extension of environmental parameters over the land surface by improved fuzzy classification of remotely sensed data

International Journal of Remote Sensing ◽

10.1080/01431160010006458 ◽

2001 ◽

Vol 22 (17) ◽

pp. 3597-3610 ◽

Cited By ~ 10

Author(s):

F. Maselli

Keyword(s):

Land Surface ◽

Environmental Parameters ◽

Remotely Sensed ◽

Fuzzy Classification ◽

Remotely Sensed Data

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CLASSIFICATION METHODS FOR REMOTELY SENSED DATA: LAND USE AND LAND COVER CLASSIFICATION USING VARIOUS COMBINATIONS OF BANDS

Jurnal Teknologi ◽

10.11113/jt.v74.4838 ◽

2015 ◽

Vol 74 (10) ◽

Author(s):

Nur Anis Mahmon ◽

Norsuzila Ya’acob ◽

Azita Laily Yusof ◽

Jasmee Jaafar

Keyword(s):

Land Use ◽

Land Cover ◽

Satellite Images ◽

Satellite Image ◽

Remotely Sensed ◽

Landsat 8 ◽

Remotely Sensed Data ◽

Ground Truth Data ◽

Sensing Applications ◽

Band Combinations

Land use and land cover (LU/LC) classification of remotely sensed data is an important field of research by which it is commonly used in remote sensing applications. In this study, the different types of classification techniques were compared using different RGB band combinations for classifying several satellite images of some parts of Selangor, Malaysia. For this objective, the classification was made using Landsat 8 satellite images and the Erdas Imagine software as the image processing package. From the classification output, the accuracy assessment and kappa statistic were evaluated to get the most accurate classifier. Optimal performance was identified by validating the classification results with ground truth data. From the results of the classified images, the Maximum Likelihood technique (overall accuracy 82.5%) was the highest and most applicable for satellite image classifications as compared with Mahalanobis Distance and Minimum Distance. Whereas for land use and land cover mapping, the RGB 4, 3, 2 band combinations were found to be more reliable. An accurate classification can produce a correct LU/LC map that can be used for various purposes.

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Detecting geothermal anomalies using Landsat 8 thermal infrared remotely sensed data

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2020.102283 ◽

2021 ◽

Vol 96 ◽

pp. 102283

Author(s):

Alexandra Gemitzi ◽

Paschalis Dalampakis ◽

George Falalakis

Keyword(s):

Thermal Infrared ◽

Remotely Sensed ◽

Landsat 8 ◽

Remotely Sensed Data

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A Comparison of Different Remotely Sensed Data for Classifying Bedrock Types in Canada’s Arctic: Application of the Robust Classification Method and Random Forests

Geoscience Canada ◽

10.12789/geocanj.2014.41.062 ◽

2014 ◽

Vol 41 (4) ◽

pp. 557 ◽

Cited By ~ 9

Author(s):

Jeff R. Harris ◽

Juan X. He ◽

Robert Rainbird ◽

Pouran Behnia

Keyword(s):

Random Forest ◽

Remotely Sensed ◽

Classification Method ◽

Training Dataset ◽

Landsat 8 ◽

Remotely Sensed Data ◽

Processing Application ◽

Robust Classification ◽

Landsat 7 ◽

Spot 5

The Geological Survey of Canada, under the Remote Predictive Mapping project of the Geo-mapping for Energy and Minerals program, Natural Resources Canada, has the mandate to produce up-to-date geoscience maps of Canada’s territory north of latitude 60°. Over the past three decades, the increased availability of space-borne sensors imaging the Earth’s surface using increasingly higher spatial and spectral resolutions has allowed geologic remote sensing to evolve from being primarily a qualitative discipline to a quantitative discipline based on the computer analysis of digital images. Classification of remotely sensed data is a well-known and common image processing application that has been used since the early 1970s, concomitant with the launch of the first Landsat (ERTS) earth observational satellite. In this study, supervised classification is employed using a new algorithm known as the Robust Classification Method (RCM), as well as a Random Forest (RF) classifier, to a variety of remotely sensed data including Landsat-7, Landsat-8, Spot-5, Aster and airborne magnetic imagery, producing predictions (classifications) of bedrock lithology and Quaternary cover in central Victoria Island, Northwest Territories. The different data types are compared and contrasted to evaluate how well they classify various lithotypes and surficial materials; these evaluations are validated by confusion analysis (confusion matrices) as well as by comparing the generalized classifications with the newly produced geology map of the study area. In addition, three new Multiple Classification System (MCS) methods are proposed that leverage the best characteristics of all remotely sensed data used for classification. Both RCM (using the maximum likelihood classification algorithm, or MLC) and RF provide good classification results; however, RF provides the highest classification accuracy because it uses all 43 of the raw and derived bands from all remotely sensed data. The MCS classifications, based on the generalized training dataset, show the best agreement with the new geology map for the study area.SOMMAIREDans le cadre de son projet de Télécartographie prédictive du Programme de géocartographie de l’énergie et des minéraux de Ressources naturelles Canada, la Commission géologique du Canada a le mandat de produire des cartes géoscientifiques à jour du territoire du Canada au nord de la latitude 60°. Au cours des trois dernières décennies, le nombre croissant des détecteurs aérospatiaux aux résolutions spatiales et spectrales de plus en plus élevées a fait passer la télédétection géologique d’une discipline principalement qualitative à une discipline quantitative basée sur l'analyse informatique d’images numériques. La classification des données de télédétection est une application commune et bien connue de traitement d'image qui est utilisée depuis le début des années 1970, parallèlement au lancement de Landsat (ERST) le premier satellite d'observation de la Terre. Dans le cas présent, nous avons employé une méthode de classification dirigée en ayant recours à un nouvel algorithme appelé Méthode de classification robuste (MRC), ainsi qu’au classificateur Random Forest (RF), appliqués à une variété de données de télédétection dont celles de Landsat-7, Landsat-8, Spot-5, Aster et d’imagerie magnétique aéroportée, pour produire des classifications prédictives de la lithologie du substratum rocheux et de la couverture Quaternaire du centre de l'île Victoria, dans les Territoires du Nord-Ouest. Les différents types de données sont comparés et contrastés pour évaluer dans quelle mesure ils classent les divers lithotypes et matériaux de surface; ces évaluations sont validés par analyse de matrices de confusion et par comparaison des classifications généralisées des nouvelles cartes géologiques de la zone d'étude. En outre, trois nouvelles méthodes par système de classification multiple (MCS) sont proposées qui permettent d’exploiter les meilleures caractéristiques de toutes les données de télédétection utilisées pour la classification. Tant la méthode MRC (utilisant l'algorithme de classification de vraisemblance maximale ou MLC que la méthode RF donne de bons résultats de classification; toutefois c’est la méthode RF qui offre la précision de classification la plus élevée car elle utilise toutes les 43 les bandes de données brutes et dérivées de toutes les données de télédétection. Les classifications MCS, basées sur le jeu de données généralisées d’apprentissage, montrent le meilleur accord avec la nouvelle carte géologique de la zone d'étude.

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Applying Remotely Sensed Environmental Information to Model Mosquito Populations

Sustainability ◽

10.3390/su13147655 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7655

Author(s):

Maria Kofidou ◽

Michael de Courcy Williams ◽

Andreas Nearchou ◽

Stavroula Veletza ◽

Alexandra Gemitzi ◽

...

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Performance Metrics ◽

Environmental Changes ◽

Environmental Parameters ◽

Remotely Sensed ◽

Control Measures ◽

Mosquito Population ◽

Landsat 8 ◽

Ann Model

Vector borne diseases have been related to various environmental parameters and environmental changes like climate change, which impact their propagation in time and space. Remote sensing data have been used widely for monitoring environmental conditions and changes. We hypothesized that changes in various environmental parameters may be reflected in changes in mosquito population size, thus impacting the temporal and spatial patterns of vector diseases. The aim of this study is to analyze the effect of environmental variables on mosquito populations using the remotely sensed Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from Landsat 8, along with other factors, such as altitude and water covered areas surrounding the examined locations. Therefore, a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) model was developed and tested for its ability to predict mosquito populations. The model was applied in NE Greece using mosquito population data from 17 locations where mosquito traps were placed from June to October 2019. All performance metrics indicated a high predictive ability of the model. LST was proved to be the factor with the highest relative importance in the prediction of mosquito populations, whereas the developed model can predict mosquito populations 13 days ahead to allow a substantial window for appropriate control measures.

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