Using Grid Computing and Satellite Remote Sensing in Evapotranspiration Estimation

2015 ◽  
pp. 145-173
Author(s):  
Cristina Serban ◽  
Carmen Maftei
Keyword(s):  
Remote Sensing ◽  
Grid Computing ◽  
Land Surface ◽  
Essential Role ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Computing Services ◽  
Monitoring And Forecasting ◽  
Forecasting System ◽  
Evapotranspiration Estimation

The most advanced and applicable approach today in the development of environmental monitoring programs is the integration of remote sensing and Grid computing services into a monitoring and forecasting system that helps the analyst to understand the problem without being a remote sensing or computer expert. In this chapter we present the main features of Grid computing and how we can use it in conjunction with remote sensing to develop several applications that will estimate ET (Evapotranspiration), LST (Land Surface Temperature) and some vegetation indices (VI's) directly from a satellite image, these parameters playing an essential role in all activities related to water resources management.

Using Grid Computing and Satellite Remote Sensing in Evapotranspiration Estimation

Biometrics ◽  
2017 ◽  
pp. 994-1016
Author(s):  
Cristina Serban ◽  
Carmen Maftei
Keyword(s):  
Remote Sensing ◽  
Grid Computing ◽  
Land Surface ◽  
Essential Role ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Computing Services ◽  
Monitoring And Forecasting ◽  
Forecasting System ◽  
Evapotranspiration Estimation

The most advanced and applicable approach today in the development of environmental monitoring programs is the integration of remote sensing and Grid computing services into a monitoring and forecasting system that helps the analyst to understand the problem without being a remote sensing or computer expert. In this chapter we present the main features of Grid computing and how we can use it in conjunction with remote sensing to develop several applications that will estimate ET (Evapotranspiration), LST (Land Surface Temperature) and some vegetation indices (VI's) directly from a satellite image, these parameters playing an essential role in all activities related to water resources management.

scholarly journals Classification of Landsat-8 Imagery Based On Pca And Ndvi Methods

2019 ◽  
Vol 8 (10) ◽  
pp. 4321-4325
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Region Of Interest ◽  
The Other ◽  
Spectral Information ◽  
Landsat 8 ◽  
Statistical Parameters ◽  
Sensing Applications

Remote sensing is an important issue in satellite image classification. In developing a significant sustainable system in agriculture farming, the major concern for remote sensing applications is the crop classification mechanism. The other important application in remote sensing is urban classification which gives the information about houses, roads, buildings, vegetation etc. A superior indicator for the presence of vegetation can be computed from the vegetation indices of a satellite image. This indicator supports in describing the health of vegetation through the image attributes like greenness and density. The other parameter in detecting objects or region of interest is an image is the texture. A satellite image contains spectral information and can be represented by more spectral bands and classification is very tough task. Generally, Classification of individual pixels in satellite images is based on the spectral information. In this research paper Principle component analysis and combination of PCA and NDVI classification methods are applied on Landsat-8 images. These images are acquired from USGS. The performance of these methods is compared in statistical parameters such as Kappa coefficient, overall accuracy, user’s accuracy, precision accuracy and F1 accuracy. In this work existing method is PCA and proposed method is PCA+NDVI. Experimental results shows that the proposed method has better statistical values compared to existing method.

scholarly journals Pemetaan Wilayah Lahan Basah Berdasarkan Data Penginderaan Jauh Optik Dan Radar

2020 ◽  
Vol 4 (2) ◽  
pp. 48-61
Author(s):  
Rian Nurtyawan ◽  
Ervan Muktamar Hendarna
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Satellite Image ◽  
Soil Surface ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Radar Sensing

ABSTRAKPada umumnya lahan basah dikelola menjadi area pertanian ataupun perkebunan. Fungsi lahan basah memiliki fungsi ekologis seperti pengendali banjir, pencegah intrusi air laut, erosi, pencemaran, dan pengendali iklim global. Data pengindraan jauh yang digunakan pengelolaan lahan basah yaitu pengindraan jauh optik dan radar. Tujuan dari penelitian ini adalah mengeksplorasi korelasi potensial dari data optik dan radar untuk mengamati dinamika pada kawasan lahan basah tersebut dan melakukan pemetaan. Metode yang digunakan pada pengindraan jauh optik yaitu LST (Land Surface Temperature) berdasarkan Citra Satelit Landsat-8 dan metode yang digunakan pada pengindraan jauh radar yaitu estimasi kelembaban tanah berdasarkan Citra Satelit Sentinel-1A. Hasil pengamatan dinamika dan pemetaan pada wilayah Kabupaten Bandung Raya memiliki nilai kelembaban tanah tertinggi pada Bulan Mei dengan nilai kelembapan tanah tanah rata-rata sebesar 20,9 % pada polarisasi VH. Suhu permukaan tanah terendah terjadi pada bulan Mei dengan nilai suhu rata-rata sebesar 19.5 °C. Kolerasi antara nilai kelembapan tanah tanah dan suhu permukaan tanah pada wilayah Kabupaten Bandung Raya berdasarkan metode koefisien determinasi sebesar R2=0.705 didapatkan bahwa semakin tinggi nilai kelembapan tanah tanah maka nilai suhu permukaan tanah akan semakin rendah.Kata kunci: Kawasan lahan basah, Pengindraan Jauh Optik, Pengindraan Jauh Radar, Pengamatan Dinamika, Pemetaan. ABSTRACTIn general wetlands managed become an area of agriculture or plantations. The extent of wetland that has been used can be damaged if it is not managed properly and integrated.. The purpose of this research is to explore the potential correlations between several parameters of optical and radar data to observe the dynamics of wetlands area and mapping the wetlands area. The methodology that was used in optical remote sensing is LST (Land Surface Temperature) based on Landsat-8 Satellite Image and the method used in remote radar sensing is estimation of soil moisture based on Sentinel-1A Satellite Image. The result of the observation in the area and mapping the dynamics in Bandung Raya District had the highest soil moisture values in May with 27% of soil water level in VH polarization and 78.1% in VV polarization and the lowest value in each month is 11.8% and the highest soil surface temperature in August with a value 37.9 ° C and the minimum value 19 ° C..Keywords: Wetland Area, Optical Remote Sensing, Remote Radar Sensing, Dynamics Observation, Mapping.

scholarly journals Mining and Restoration Monitoring of Rare Earth Element (REE) Exploitation by New Remote Sensing Indicators in Southern Jiangxi, China

2020 ◽  
Vol 12 (21) ◽  
pp. 3558
Author(s):  
Lifeng Xie ◽  
Weicheng Wu ◽  
Xiaolan Huang ◽  
Penghui Ou ◽  
Ziyu Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rare Earth ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Rapid Expansion ◽  
Open Pit ◽  
Open Pit Mining ◽  
Chemical Extraction

Rare earth elements (REEs) are widely used in various industries. The open-pit mining and chemical extraction of REEs in the weathered crust in southern Jiangxi, China, since the 1970s have provoked severe damages to the environment. After 2010, different restorations have been implemented by various enterprises, which seem to have a spatial variability in both management techniques and efficiency from one mine to another. A number of vegetation indices, e.g., normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and atmospherically resistant vegetation index (ARVI), can be used for this kind of monitoring and assessment but lack sensitivity to subtle differences. For this reason, the main objective of this study was to explore the possibility to develop new, mining-tailored remote sensing indicators to monitor the impacts of REE mining on the environment and to assess the effectiveness of its related restoration using multitemporal Landsat data from 1988 to 2019. The new indicators, termed mining and restoration assessment indicators (MRAIs), were developed based on the strong contrast of spectral reflectance, albedo, land surface temperature (LST) and tasseled cap brightness (TCB) of REE mines between mining and postmining restoration management. These indicators were tested against vegetation indices such as NDVI, EVI, SAVI and generalized difference vegetation index (GDVI), and found to be more sensitive. Of similar sensitivity to each other, one of the new indicators was employed to conduct the restoration assessment of the mined areas. Six typically managed mines with different restoration degrees and management approaches were selected as hotspots for a comparative analysis to highlight their temporal trajectories using the selected MRAI. The results show that REE mining had experienced a rapid expansion in 1988–2010 with a total mined area of about 66.29 km2 in the observed counties. With implementation of the post-2010 restoration measures, an improvement of varying degrees in vegetation cover in most mines was distinguished and quantified. Hence, this study with the newly developed indicators provides a relevant approach for assessing the sustainable exploitation and management of REE resources in the study area.

scholarly journals Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data

2021 ◽  
Vol 13 (4) ◽  
pp. 818
Author(s):  
Sofia Junttila ◽  
Julia Kelly ◽  
Natascha Kljun ◽  
Mika Aurela ◽  
Leif Klemedtsson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Sensor Data ◽  
Spatial And Temporal Variation ◽  
Model Parameters ◽  
Co2 Fluxes ◽  
Sentinel 2

Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes.

scholarly journals A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling

2010 ◽  
Vol 7 (9) ◽  
pp. 2943-2958 ◽  
Author(s):  
B. Chen ◽  
Q. Ge ◽  
D. Fu ◽  
G. Yu ◽  
X. Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Model ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Consecutive Series ◽  
Fusion Model ◽  
Flux Footprint ◽  
Model Fusion ◽  
Water Index

Abstract. In order to use the global available eddy-covariance (EC) flux dataset and remote-sensing measurements to provide estimates of gross primary productivity (GPP) at landscape (101–102 km2), regional (103–106 km2) and global land surface scales, we developed a satellite-based GPP algorithm using LANDSAT data and an upscaling framework. The satellite-based GPP algorithm uses two improved vegetation indices (Enhanced Vegetation Index – EVI, Land Surface Water Index – LSWI). The upscalling framework involves flux footprint climatology modelling and data-model fusion. This approach was first applied to an evergreen coniferous stand in the subtropical monsoon climatic zone of south China. The EC measurements at Qian Yan Zhou tower site (26°44´48" N, 115°04´13" E), which belongs to the China flux network and the LANDSAT and MODIS imagery data for this region in 2004 were used in this study. A consecutive series of LANDSAT-like images of the surface reflectance at an 8-day interval were predicted by blending the LANDSAT and MODIS images using an existing algorithm (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model). The seasonal dynamics of GPP were then predicted by the satellite-based algorithm. MODIS products explained 60% of observed variations of GPP and underestimated the measured annual GPP (= 1879 g C m−2) by 25–30%; while the satellite-based algorithm with default static parameters explained 88% of observed variations of GPP but overestimated GPP during the growing seasonal by about 20–25%. The optimization of the satellite-based algorithm using a data-model fusion technique with the assistance of EC flux tower footprint modelling reduced the biases in daily GPP estimations from about 2.24 g C m−2 day−1 (non-optimized, ~43.5% of mean measured daily value) to 1.18 g C m−2 day−1 (optimized, ~22.9% of mean measured daily value). The remotely sensed GPP using the optimized algorithm can explain 92% of the seasonal variations of EC observed GPP. These results demonstrated the potential combination of the satellite-based algorithm, flux footprint modelling and data-model fusion for improving the accuracy of landscape/regional GPP estimation, a key component for the study of the carbon cycle.

scholarly journals Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing

2021 ◽  
Vol 13 (15) ◽  
pp. 3015
Author(s):  
Koffi Dodji Noumonvi ◽  
Gal Oblišar ◽  
Ana Žust ◽  
Urša Vilhar
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Fagus Sylvatica ◽  
Environmental Conditions ◽  
Tree Species ◽  
Land Surface ◽  
Vegetation Indices ◽  
Mixed Forest ◽  
Individual Tree ◽  
Mixed Forests

Phenological events are good indicators of the effects of climate change, since phenological phases are sensitive to changes in environmental conditions. Although several national phenological networks monitor the phenology of different plant species, direct observations can only be conducted on individual trees, which cannot be easily extended over large and continuous areas. Remote sensing has often been applied to model phenology for large areas, focusing mostly on pure forests in which it is relatively easier to match vegetation indices with ground observations. In mixed forests, phenology modelling from remote sensing is often limited to land surface phenology, which consists of an overall phenology of all tree species present in a pixel. The potential of remote sensing for modelling the phenology of individual tree species in mixed forests remains underexplored. In this study, we applied the seasonal midpoint (SM) method with MODIS GPP to model the start of season (SOS) and the end of season (EOS) of six different tree species in Slovenian mixed forests. First, substitute locations were identified for each combination of observation station and plant species based on similar environmental conditions (aspect, slope, and altitude) and tree species of interest, and used to retrieve the remote sensing information used in the SM method after fitting the best of a Gaussian and two double logistic functions to each year of GPP time series. Then, the best thresholds were identified for SOS and EOS, and the results were validated using cross-validation. The results show clearly that the usual threshold of 0.5 is not best in most cases, especially for estimating the EOS. Despite the difficulty in modelling the phenology of different tree species in a mixed forest using remote sensing, it was possible to estimate SOS and EOS with moderate errors as low as <8 days (Fagus sylvatica and Tilia sp.) and <10 days (Fagus sylvatica and Populus tremula), respectively.

scholarly journals The use of Landsat 8 remote sensing data for assessing the temperature conditions of the Larus ridibundus L. habitat

2020 ◽  
Vol 9 (4) ◽  
pp. 184-191
Author(s):  
Sergey Arkadyevich Shurakov ◽  
Aleksey Nikolaevich Chashchin
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Close Correlation ◽  
Primary Data ◽  
Landsat 8 ◽  
Sensing Data ◽  
Temperature Conditions

This paper discusses the possibilities of using Landsat 8 remote sensing data for assessing the temperature conditions of aquatic landscapes when studying the abundance and density of gulls. The study of the ornithological situation was carried out on the territory of the Perm international airport of the Perm Region, where the black-headed gull is an unfavorable factor in the safety of passenger aircraft flights. Within the boundaries of the region, 5 reservoirs were identified. A method for calculating the surface temperature from a multispectral satellite image of the Landsat 8 series is described in detail with the presentation of primary data sources, atmospheric parameters and obtaining raster coverage with a resolution of 30 meters per pixel. The tool used for the calculation is the Land Surface Temperature module of the QGIS software. The paper presents maps of temperature within the area of conducted ornithological surveys and the density of gulls. The densities of birds for individual bodies of water are calculated using the Spatial Analyst module of the ArcGIS program with the kernel density tool. According to the research results, a close correlation was established between the attractiveness of reservoirs for gulls and water temperature. The correlation coefficients were 0,83 and 0,71, respectively, with the abundance and density of gulls.

scholarly journals Characterising Rice (Oryza indica) Ecology in East and Southern Eastern Plateau of Odisha using Multi Source Remote Sensing

2021 ◽  
pp. 37-47
Author(s):  
M. Satya Swarupa Rani ◽  
Anima Biswal ◽  
B. S. Rath
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Ecological Condition ◽  
Water Index ◽  
Normalised Difference Vegetation Index ◽  
Spectral Vegetation Indices ◽  
Block Level

Rice is the most important crop of Odisha occupying 41.24% of net sown area in Kharif season and contributing 65.85 % of total food grain production of Odisha state and this is being cultivated in various types environmental and ecological condition. Assessment of rice phenology is prime for management and yield prediction. In view of characterizing rice ecology in East and South Eastern Plateau from 2008 – 2018 to know the time series analysis , remote sensing tools were used . MODIS can0 acquire data over a wide area with high spatial and temporal resolutions easily providing regional scale information .In order to study the seasonal /annual as well as spatial variability of kharif rice vigour and wetness spectral vegetation indices like NDVI(Normalised Difference Vegetation Index),LSWI(Land surface water index) derived from 15 day composite 250 m data were analysed at block level for Odisha state. For studying the start of season variability, SASI index was used. The season maximum NDVI, LSWI were computed for the year 2008-2018 for kharif rice in East and Southern eastern coastal plain zone of Odisha and graphs were generated which shows the variability of the kharif rice vigour and wetness.

scholarly journals Integrating effective drought index (EDI) and remote sensing derived parameters for agricultural drought assessment and prediction in Bundelkhand region of India

2014 ◽  
Vol XL-8 ◽  
pp. 89-100 ◽  
Author(s):  
S. K. Padhee ◽  
B. R. Nikam ◽  
S. P. Aggarwal ◽  
V. Garg
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Drought Index ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Agricultural Drought ◽  
Predictive Capability ◽  
Drought Assessment ◽  
Soil Moisture Anomaly

Drought is an extreme condition due to moisture deficiency and has adverse effect on society. Agricultural drought occurs when restraining soil moisture produces serious crop stress and affects the crop productivity. The soil moisture regime of rain-fed agriculture and irrigated agriculture behaves differently on both temporal and spatial scale, which means the impact of meteorologically and/or hydrological induced agriculture drought will be different in rain-fed and irrigated areas. However, there is a lack of agricultural drought assessment system in Indian conditions, which considers irrigated and rain-fed agriculture spheres as separate entities. On the other hand recent advancements in the field of earth observation through different satellite based remote sensing have provided researchers a continuous monitoring of soil moisture, land surface temperature and vegetation indices at global scale, which can aid in agricultural drought assessment/monitoring. Keeping this in mind, the present study has been envisaged with the objective to develop agricultural drought assessment and prediction technique by spatially and temporally assimilating effective drought index (EDI) with remote sensing derived parameters. The proposed technique takes in to account the difference in response of rain-fed and irrigated agricultural system towards agricultural drought in the Bundelkhand region (The study area). <br><br> The key idea was to achieve the goal by utilizing the integrated scenarios from meteorological observations and soil moisture distribution. EDI condition maps were prepared from daily precipitation data recorded by Indian Meteorological Department (IMD), distributed within the study area. With the aid of frequent MODIS products viz. vegetation indices (VIs), and land surface temperature (LST), the coarse resolution soil moisture product from European Space Agency (ESA) were downscaled using linking model based on Triangle method to a finer resolution soil moisture product. EDI and spatially downscaled soil moisture products were later used with MODIS 16 days NDVI product as key elements to assess and predict agricultural drought in irrigated and rain-fed agricultural systems in Bundelkhand region of India. Meteorological drought, soil moisture deficiency and NDVI degradation were inhabited for each and every pixel of the image in GIS environment, for agricultural impact assessment at a 16 day temporal scale for Rabi seasons (October&ndash;April) between years 2000 to 2009. Based on the statistical analysis, good correlations were found among the parameters EDI and soil moisture anomaly; NDVI anomaly and soil moisture anomaly lagged to 16 days and these results were exploited for the development of a linear prediction model. The predictive capability of the developed model was validated on the basis of spatial distribution of predicted NDVI which was compared with MODIS NDVI product in the beginning of preceding Rabi season (Oct&ndash;Dec of 2010).The predictions of the model were based on future meteorological data (year 2010) and were found to be yielding good results. The developed model have good predictive capability based on future meteorological data (rainfall data) availability, which enhances its utility in analyzing future Agricultural conditions if meteorological data is available.

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