Modeling the relationship between elevation, aspect and spatial distribution of vegetation in the Darab Mountain, Iran using remote sensing data

2015 ◽  
Vol 1 (4) ◽  
Author(s):  
Marzieh Mokarram ◽  
Dinesh Sathyamoorthy
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
The Relationship

Analysis of the relationship between inundation frequency and wetland vegetation in Dongting Lake using remote sensing data

Ecohydrology ◽  
2013 ◽  
Vol 7 (2) ◽  
pp. 717-726 ◽  
Author(s):  
Fan Deng ◽  
Xuelei Wang ◽  
Xiaobin Cai ◽  
Enhua Li ◽  
Liuzhi Jiang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Dongting Lake ◽  
Wetland Vegetation ◽  
Sensing Data ◽  
Inundation Frequency ◽  
The Relationship

scholarly journals Simulation of Evapotranspiration at a 3-Minute Time Interval Based on Remote Sensing Data and SEBAL Model

2020 ◽  
Vol 10 (14) ◽  
pp. 4919
Author(s):  
Guoqing Li ◽  
Alona Armstrong ◽  
Xueli Chang
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Time Interval ◽  
Sensing Data ◽  
Significant Linear Correlation ◽  
Sebal Model ◽  
Moderate Resolution Imaging Spectroradiometer

Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses GF-4 and moderate-resolution imaging spectroradiometer (MODIS) data in conjunction with the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET at a 3-min time interval in part of China and South Korea, and compares those simulation results with that from field measured data. According to the spatial distribution of ET derived from GF-4 and MODIS, the texture of ET derived from GF-4 is more obvious than that of MODIS, and GF-4 is able to express the variability of the spatial distribution of ET. Meanwhile, according to the value of ET derived from both GF-4 and MODIS, results from these two satellites have significant linear correlation, and ET derived from GF-4 is higher than that from MODIS. Since the temporal resolution of GF-4 is 3 min, the land surface ET at a 3-min time interval could be obtained by utilizing all available meteorological and remote sensing data, which avoids error associated with extrapolating instantaneously from a single image.

scholarly journals Estimation of the Relationship between Urban Park Characteristics and Park Cool Island Intensity by Remote Sensing Data and Field Measurement

Forests ◽  
2013 ◽  
Vol 4 (4) ◽  
pp. 868-886 ◽  
Author(s):  
Zhibin Ren ◽  
Xingyuan He ◽  
Haifeng Zheng ◽  
Dan Zhang ◽  
Xingyang Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Field Measurement ◽  
Remote Sensing Data ◽  
Urban Park ◽  
Sensing Data ◽  
The Relationship

scholarly journals RELATIONSHIPS BETWEEN RICE GROWTH PARAMETERS AND REMOTE SENSING DATA

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
I Wayan Nuarsa ◽  
Fumihiko Nishio
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristic ◽  
Vegetation Index ◽  
Growth Parameters ◽  
Life Stage ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Rice Growth ◽  
Vegetation Water ◽  
The Relationship

Rice is an agriculture plants that has the specific characteristic in the life stage due to the growth stage having different proportion of vegetation, water, and soil. Vegetation index is one of the satellite remote sensing parameter that is widely used to monitor the global vegetation cover. The objective of the study is to know the spectral characteristic of rice plant in the life stage and find the relationship between the rice growth parameters and the remote sensing data by the Landsat ETM data using the correlation and regression analysis. The result of study shows that the spectral characteristic of the rice before one month of age is defferent comparing after one month. All of the examined vegetation index has close linear relationship with rice coverage. Difference Vegetation Index (DVI) is the best vegetation index which estimates rice coverage with equation y = 1.762x + 2.558 and R degree value was 0.946. Rice age has a high quadratic relationship with all of evaluated vegetation index. Transformed Vegetation Index (TVI) is the best vegetation to predict the age of the rice. Formula y = 0.013x - 1.625x + 145.8 is the relationship form between the rice age and the TVI with R = 0.939. Peak of the vegetation index of rice is in the rice age of 2 months. This period is the transition of vegetative and generative stages. Keywords: Vegetation index, Rice growth, Spectral characteristic, Landsat ETM.

scholarly journals Use of Earth Remote Sensing data of monitoring the level of soil fertility

2020 ◽  
Vol 131 (2) ◽  
pp. 78-84
Author(s):  
K.S. Baktybekov ◽  
◽  
G.R. Kabzhanova ◽  
А.А. Aimbetov ◽  
M.T. Alibayeva ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Material Resources ◽  
Northern Kazakhstan ◽  
Spectral Ranges ◽  
Ground Monitoring ◽  
The Relationship

Ground monitoring of soil massifs takes a lot of time, labor and material resources, although it is the most accurate and detailed. When introducing complex methods of monitoring the soil cover, the inclusion of space technologies is mandatory.Remote sensing data carry objective information over large areas, obtained in various spectral ranges. The article discusses the possibility of using remote sensing data for mapping and monitoring changes in the soil cover of Northern Kazakhstan. On the basis of thematic processing of remote sensing data of native satellites, a spatial analysis of the content of main nutrients in the sowing layer of soils was carried out, the relationship was revealed between fertility indicators and the value of vegetation indices for test ranges of the territory of Northern Kazakhstan.

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