Use of Remote Sensing and Field Data to Quantify the Performance and Resilience of Restored Louisiana Wetlands

Wetlands ◽  
2020 ◽  
Vol 40 (6) ◽  
pp. 2643-2658
Author(s):  
Glenn M. Suir ◽  
Charles E. Sasser ◽  
John M. Harris
Keyword(s):  
Remote Sensing ◽  
Field Data

Measuring stream habitat conditions: Can remote sensing substitute for field data?

2021 ◽  
pp. 147617
Author(s):  
Karina Dias-Silva ◽  
Thiago Bernardi Vieira ◽  
Talissa Pio de Matos ◽  
Leandro Juen ◽  
Juliana Simião-Ferreira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Field Data ◽  
Stream Habitat ◽  
Habitat Conditions

Use of geographic information systems and remote sensing as automated tool for variable rate irrigation prescriptions

2019 ◽  
Author(s):  
◽  
Anh Thi Tuan Nguyen
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Regression Models ◽  
Field Data ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Crop Growth ◽  
Variable Rate ◽  
Water Application ◽  
Center Pivot

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Economic as well as water shortage pressure on agricultural use of water has placed added emphasis on efficient irrigation management. Center pivot technology has made great improvement with variable rate irrigation (VRI) technology to vary water application spatially and temporally to maximize the economic and environmental return. Proper management of VRI systems depends on correctly matching the pivot application to specific field temporal and areal conditions. There is need for a tool to accurately and inexpensively define dynamic management zones, to sense within-field variability in real time, and control variable rate water application so that producers are more willing to adopt and utilize the advantages of VRI systems. This study included tests of the center pivot system uniformity performance in 2014 at Delta Research Center in Portageville, MO. The goal of this research was to develop MOPivot software with an algorithm to determine unique management areas under center pivot systems based on system design and limitations. The MOPivot tool automates prescriptions for VRI center pivot based on non-uniform water needs while avoiding potential runoff and deep percolation. The software was validated for use in real-time irrigation management in 2018 for VRI control system of a Valley 8000 center pivot planted to corn. The water balance model was used to manage irrigation scheduling. Field data, together with soil moisture sensor measurement of soil water content, were used to develop the regression model of remote sensing-based crop coefficient (Kc). Remote sensing vegetation index in conjunction with GDD and crop growth stages in regression models showed high correlation with Kc. Validation of those regression models was done using Centralia, MO, field data in 2016. The MOPivot successfully created prescriptions to match system capacity of the management zone based on system limitations for center pivot management. Along with GIS data sources, MOPivot effectively provides readily available graphical prescription maps, which can be edited and directly uploaded to a center pivot control panel. The modeled Kc compared well with FAO Kc. By combining GDD and crop growth in the models, these models would account for local weather conditions and stage of crop during growing season as time index in estimating Kc. These models with Fraction of growth (FrG) and cumulative growing degree days (cGDD) had a higher coefficient of efficiency, higher Nash-Sutcliffe coefficient of efficiency and higher Willmott index of agreement. Future work should include improvement in the MOPivot software with different crops and aerial remote sensing imagery to generate dynamic prescriptions during the season to support irrigation scheduling for real-time monitoring of field conditions.

scholarly journals ABOVE GROUND TREES BIOMASS OF LORE LINDU NATIONAL PARK-CENTRAL SULAWESI : A STUDY COMBINING FIELD MEASUREMENT AND REMOTE SENSING

Agromet ◽  
2010 ◽  
Vol 24 (1) ◽  
pp. 33
Author(s):  
Naimatu Solicha ◽  
Tania June ◽  
M. Ardiansyah ◽  
Antonius B. W.
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Field Data ◽  
Forest Cover ◽  
Satellite Image ◽  
Forest Biomass ◽  
Allometric Equation ◽  
Natural Forest ◽  
Forest Cover Type ◽  
Cover Type

Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.

An Integrated Field Data and Remote Sensing Approach for impact assessment of human activities on epifauna macro‐benthos biodiversity along Western coast of Aqaba Gulf

Ecohydrology ◽  
2021 ◽  
Author(s):  
Hussein A. El‐Naggar ◽  
El‐Sayed S. Salem ◽  
Sameh B. El‐Kafrawy ◽  
Mansour A. Bashar ◽  
Walaa M. Shaban ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Impact Assessment ◽  
Human Activities ◽  
Field Data ◽  
Western Coast ◽  
Macro Benthos

scholarly journals A Remote Sensing–based Intensity-Duration Curve, Faifa Mountains, Saudi Arabia

2018 ◽  
Author(s):  
Sita Karki ◽  
Mohamed Sultan ◽  
Saleh A. Al-Sefry ◽  
Hassan M. Alharbi ◽  
Mustafa Kemal Emil ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Early Warning ◽  
Field Data ◽  
Early Warning Systems ◽  
Google Earth ◽  
Archival Data ◽  
Warning Systems ◽  
Temporal And Spatial ◽  
Red Sea Hills

Abstract. Construction of intensity-duration (ID) curves and early warning systems for landslides (EWSL) are hampered by the paucity of temporal and spatial archival data. We developed methodologies that could be used for the construction of an ID curve that could be used for the construction of an EWSL over the Faifa Mountains in the Red Sea Hills. The developed methodologies relies on temporal, readily available, archival Google Earth and Sentinel-1 imagery, precipitation measurements, and limited field data. These methodologies accurately distinguished landslide-producing storms from non–landslide producing ones and identified the locations of these landslides with an accuracy of 60 %.

scholarly journals Determination of Hydrothermal Prospects in Paipa Geothermal Region (Boyacá, Colombia) Using Remote Sensing and Field Data

2019 ◽  
Vol 23 (4) ◽  
pp. 265-282
Author(s):  
Rafael Andrés Calderón-Chaparro ◽  
German Vargas-Cuervo
Keyword(s):  
Remote Sensing ◽  
Soil Temperature ◽  
Field Data ◽  
Hot Springs ◽  
Landsat 8 ◽  
Geothermal Resources ◽  
Temperature Index ◽  
Landsat 7 ◽  
Geothermal Region

Geothermal resources (e.g. hot springs) are found with the help of field techniques, such as geological, geochemistry and geophysical. These techniques in some occasions are difficult to apply because of the limit access to the research area, rising operational costs and constrained spatially the exploration areas. The thermal infrared (TIR) remote sensing is an important tool for the exploration of geothermal resources, due to the low cost and high efficiency in the study of large geographic areas. The aim of this study is to use thermal imagery of satellite remote sensing and combined with geological-geophysical data, for spatial determination of exploratory prospects of hot springs in the geothermal region of Paipa, Boyacá. The images used in this study are from satellites Landsat-7 ETM+, Landsat-8 OLI/TIRS, MODIS, ALOS-PALSAR and Pléiades. Also, field data is used, such as soil temperature, surface temperature, air temperature, relative humidity, atmospheric pressure and thermal imagery of surface geothermal manifestations. The Landsat thermal bands were radiometrically calibrated, then atmospherically and surface emissivity corrected, applying single channel and split window algorithms, for Landsat-7 ETM+ and Landsat-8 TIRS, respectively. The field data helped to correct the thermal bands. And the soil temperature data are used to create a subsurface temperature map at 1-meter depth. Once primary and secondary data is had, in a geographic information system (GIS) is implemented an unweighted spatial model, which use four input indicators (satellite temperature index, soil temperature index, structural lineaments index and iso-resistivity index) to determine the areas with higher probability to find geothermal fluids. Six prospects are highlighted for hydrothermal fluid extraction, in which two of them are already known. Results allow to concluded that thermal remote sensing are useful to map geothermal anomalies in the Paipa region, and by using these anomalies plus geological-geophysical information is possible to determine exact exploration areas.

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