Classification and mapping of the vegetation using field observations and remote sensing

2003 ◽  
pp. 19-48
Author(s):  
Nuri Trigo Boix ◽  
Aurora Chimal Hernandez ◽  
Gerrit W. Heil ◽  
Roland Bobbink ◽  
Betty Verduyn
Keyword(s):  
Remote Sensing ◽  
Field Observations

Sub-bend scale flow–sediment interaction of meander bends — A combined approach of field observations, close-range remote sensing and computational modelling

Geomorphology ◽  
2015 ◽  
Vol 238 ◽  
pp. 119-134 ◽  
Author(s):  
Elina Kasvi ◽  
Matti Vaaja ◽  
Harri Kaartinen ◽  
Antero Kukko ◽  
Anttoni Jaakkola ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computational Modelling ◽  
Field Observations ◽  
Combined Approach ◽  
Close Range ◽  
Meander Bends

scholarly journals Field Observations During the Fifth Microwave Water and Energy Balance Experiment (MicroWEX-5): from March 9 through May 26, 2006

EDIS ◽  
2007 ◽  
Vol 2007 (17) ◽  
Author(s):  
Joaquin Casanova ◽  
Fei Yan ◽  
Mi-young Jang ◽  
Juan Fernandez ◽  
Jasmeet Judge ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Energy Balance ◽  
Root Zone ◽  
Microwave Remote Sensing ◽  
Field Observations ◽  
Biological Engineering

Circular 1514, a 47-page illustrated report by Joaquin Casanova, Fei Yan, Mi-young Jang, Juan Fernandez, Jasmeet Judge, Clint Slatton, Kai-Jen Calvin Tien, Tzu-yun Lin, Orlando Lanni, and Larry Miller, presents the results of experiments using microwave remote sensing to determine root-zone soil moisture at UF/IFAS PSREU. Published by the UF Department of Agricultural and Biological Engineering, May 2007. CIR1514/AE407: Field Observations During the Fifth Microwave Water and Energy Balance Experiment: from March 9 through May 26, 2006 (ufl.edu)

scholarly journals Beyond Bioproductivity: Engaging Local Perspectives in Land Degradation Monitoring and Assessment

2018 ◽  
Author(s):  
Stefanie Herrmann ◽  
Abdoul Aziz Diouf ◽  
Ibrahima Sall
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Vegetation Indices ◽  
Temporal Trends ◽  
Photo Elicitation ◽  
Field Observations ◽  
Crowd Sourcing ◽  
Vegetative Cover ◽  
Degradation Monitoring ◽  
Land Condition

Land degradation monitoring and assessment in the Sahel zone takes advantage of and relies substantially on temporal trends of remote sensing-based vegetation indices, which are proxies for the bioproductivity of the land. However, prior studies have shown that negative or positive trends in bioproductivity are not necessarily associated with degradation or improvement of land condition. We argue that remote sensing-based indices, while having contributed much to dismantling an outdated desertification narrative, are themselves falling short of capturing the whole picture and need to be accompanied by field observations that are relevant to local land users. We used the participatory photo elicitation method in three sites in order to elicit local pastoralists’ perspectives on land degradation and identify the indicators that they use to characterize pasture quality, while empowering them to lead the discussion. The discussion revealed indicators far beyond bioproductivity, including livestock performance as well as composition and quality of the herbaceous and woody vegetative cover, invasive species, soil quality and water availability. We found that the pastoralists’ interest, knowledge and field observations could potentially be harnessed using a crowd-sourcing approach in order to produce a geospatially explicit dataset of land degradation, which would be complementary to the remote sensing-based maps of trends in bioproductivity and could serve as a reference for the development of more targeted remote sensing-based indicators of land degradation

The development of the Odden ice tongue in the Greenland Sea during winter 1993 from remote sensing and field observations

1996 ◽  
Vol 101 (C8) ◽  
pp. 18213-18235 ◽  
Author(s):  
P. Wadhams ◽  
J. C. Comiso ◽  
E. Prussen ◽  
S. Wells ◽  
M. Brandon ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Field Observations ◽  
Greenland Sea

Identifying urban vegetation stress factors based on open access remote sensing imagery and field observations

2020 ◽  
Vol 55 ◽  
pp. 101032 ◽  
Author(s):  
Irina Cârlan ◽  
Bogdan-Andrei Mihai ◽  
Constantin Nistor ◽  
André Große-Stoltenberg
Keyword(s):  
Remote Sensing ◽  
Open Access ◽  
Stress Factors ◽  
Urban Vegetation ◽  
Field Observations ◽  
Remote Sensing Imagery ◽  
Vegetation Stress

scholarly journals Methods of calculating landslide volume using remote sensing data

2020 ◽  
Vol 149 ◽  
pp. 02009
Author(s):  
Maira Razakova ◽  
Alexandr Kuzmin ◽  
Igor Fedorov ◽  
Rustam Yergaliev ◽  
Zharas Ainakulov
Keyword(s):  
Remote Sensing ◽  
Sea Level ◽  
Remote Sensing Data ◽  
Field Studies ◽  
Aerial Photographs ◽  
Field Observations ◽  
Sensing Data ◽  
Quantitative Estimates ◽  
Landslide Volume ◽  
Engineering Protection

The paper considers the issues of calculating the volume of the landslide from remote sensing data. The main methods of obtaining information during research are field observations. The most important results of field studies are quantitative estimates, such as the volume of the embankment resulting from a landslide, morphometric indicators, etc. The study of a remote and remote object was carried out by remote methods using aerial photographs in the Ile Alatau foothills at 1,600 meters above sea level. The obtained materials from the mudflow survey will be useful in developing solutions to mitigate the effects of disasters and in the design of measures for engineering protection from landslides.

scholarly journals Soil-Landscape Modeling and Remote Sensing to Provide Spatial Representation of Soil Attributes for an Ethiopian Watershed

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Nurhussen Mehammednur Seid ◽  
Birru Yitaferu ◽  
Kibebew Kibret ◽  
Feras Ziadat
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Soil Properties ◽  
Vegetation Index ◽  
Soil Depth ◽  
Statistical Prediction ◽  
Field Observations ◽  
Linear Regression Models ◽  
Alternative Source ◽  
Soil Attributes

Information about the spatial distribution of soil properties is necessary for natural resources modeling; however, the cost of soil surveys limits the development of high-resolution soil maps. The objective of this study was to provide an approach for predicting soil attributes. Topographic attributes and the normalized difference vegetation index (NDVI) were used to provide information about the spatial distribution of soil properties using clustering and statistical techniques for the 56 km2Gumara-Maksegnit watershed in Ethiopia. Multiple linear regression models implemented within classified subwatersheds explained 6–85% of the variations in soil depth, texture, organic matter, bulk density, pH, total nitrogen, available phosphorous, and stone content. The prediction model was favorably comparable with the interpolation using the inverse distance weighted algorithm. The use of satellite images improved the prediction. The soil depth prediction accuracy dropped gradually from 98% when 180 field observations were used to 65% using only 25 field observations. Soil attributes were predicted with acceptable accuracy even with a low density of observations (1-2 observations/2 km2). This is because the model utilizes topographic and satellite data to support the statistical prediction of soil properties between two observations. Hence, the use of DEM and remote sensing with minimum field data provides an alternative source of spatially continuous soil attributes.

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