Remote Sensing and Hydrogeological Methodologies for Irrigation Canal Water Losses Detection: The Naviglio di Bra Test Site (NW-Italy)

2014 ◽  
pp. 19-22
Author(s):  
Luigi Perotti ◽  
Manuela Lasagna ◽  
Paolo Clemente ◽  
Giovanna Antonella Dino ◽  
Domenico Antonio De Luca
Keyword(s):  
Remote Sensing ◽  
Test Site ◽  
Irrigation Canal ◽  
Canal Water ◽  
Water Losses ◽  
Nw Italy

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals Research of rice crops in Krasnodar region by remote sensing data

2020 ◽  
Vol 175 ◽  
pp. 01004
Author(s):  
Sergey Garkusha ◽  
Mikhail Skazhennik ◽  
Evgeny Kiselev ◽  
Vitaliy Chizhikov ◽  
Alexey Petrushin
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Remote Sensing Data ◽  
Test Site ◽  
Experimental Methodology ◽  
Federal State ◽  
Automated Mapping ◽  
Yield Information ◽  
Geoinformation Modeling ◽  
Sentinel 2A

The concept of digitalization of agricultural production in the Russian Federation provides for the implementation of measures to develop and create a system of geographic information monitoring and decision support in crop production. The aim of the research was to conduct geoinformation monitoring of rice crops to develop methods for automated mapping of their condition and yield forecasting. The studies were carried out on a test site of the Federal State Budgetary Scientific Institution “Federal Scientific Rice Centre” with an area of 274 hectares. The survey was performed by a quadcopter with a MicaSense RedEdge-M multispectral camera mounted on a fixed suspension. The shooting period using an unmanned aerial vehicle (UAV) was limited to early June and additionally used the Sentinel-2A satellite. To assess the state of rice crops, the normalized relative vegetative index NDVI was used. Based on the NDVI distribution and yield information from the combine TUCANO 580 (CLAAS), a statistical analysis was carried out in fields 7 and 9. Testing of the experimental methodology for monitoring crops in 2019 on the basis of remote sensing of test plots and geoinformation modeling and the statistical apparatus should be considered satisfactory.

scholarly journals Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture

2020 ◽  
Vol 12 (16) ◽  
pp. 2660
Author(s):  
Philip Marzahn ◽  
Swen Meyer
Keyword(s):  
Remote Sensing ◽  
Soil Texture ◽  
Land Surface ◽  
Temporal Frequency ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Test Site ◽  
Soil Parameters ◽  
Sensing Data

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches.

Airborne remote sensing for detection of irrigation canal leakage

2010 ◽  
Vol 59 (5) ◽  
pp. 524-534 ◽  
Author(s):  
Yanbo Huang ◽  
Guy Fipps ◽  
Stephan J. Maas ◽  
Reginald S. Fletcher
Keyword(s):  
Remote Sensing ◽  
Irrigation Canal ◽  
Airborne Remote Sensing

Application of sky-view factor for the visualisation of historic landscape features in lidar-derived relief models

Antiquity ◽  
2011 ◽  
Vol 85 (327) ◽  
pp. 263-273 ◽  
Author(s):  
Žiga Kokalj ◽  
Klemen Zakšek ◽  
Krištof Oštir
Keyword(s):  
Remote Sensing ◽  
Point Source ◽  
Test Site ◽  
Lidar Data ◽  
View Factor ◽  
Diffuse Light ◽  
Sky View Factor ◽  
Usual Form ◽  
Aerial Mapping ◽  
Historic Landscape

Aerial mapping and remote sensing takes another step forward with this method of modelling lidar data. The usual form of presentation, hill shade, uses a point source to show up surface features. Sky-view factor simulates diffuse light by computing how much of the sky is visible from each point. The result is a greatly improved visibility — as shown here by its use on a test site of known topography in Slovenia.

scholarly journals Evaluation of Soil Degradation Based on High Resolution Remote Sensing Data

2005 ◽  
pp. 145-148
Author(s):  
Péter Burai ◽  
János Tamás
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Soil Salinity ◽  
Strong Correlation ◽  
Soil Degradation ◽  
Vegetation Index ◽  
Near Infrared ◽  
Salt Content ◽  
Remote Sensing Data ◽  
Test Site

Soil salinity is the main problem of soil degradation in the Grate Plain with cultivated area of 20% affected. Its influence is accelerated on the water managed and irrigated lands. Remote sensing can significantly contribute to detecting temporal changes of salt-related surface features. We have chosen a farm where intensive crop cultivation takes place as a test site as soil degradation can be intensive as a result of land use and irrigation. In order to evaluate soil salt content and biomass analysis, we gathered detailed data from an 100x250 m area. We analyzed the salinity property of the samples. In our research we used a TETRACAM ADC multispectral camera to take high resolution images (0,2-0,5 m) of low altitude (300-500 m). A Normalized Vegetation Index was computed from near infrared (750-950 nm) and red (620-750 nm) bands. This data was compared with the samples of investigated area. Analyzing the images, we evaluated image reliability, and the connection between the bands and the soil properties (pH, salt content). A strong correlation observed between NDVI and soil salinity (EC) makes the multispectral images suitable for construction of salinity map. A further strong correlation was determined between NDVI and yield.

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