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

NDVI Changes of the Nansi Lake in Shandong Province of China

2014 ◽  
Vol 919-921 ◽  
pp. 1659-1662
Author(s):  
Chun Ling Liang
Keyword(s):  
Remote Sensing ◽  
Seasonal Variation ◽  
Remote Sensing Data ◽  
Wetland Vegetation ◽  
Shandong Province ◽  
Nansi Lake ◽  
Sensing Data ◽  
Vegetation Indexes ◽  
Vegetation Activity ◽  
Variation Characteristics

The normalized difference vegetation indexes (NDVI) of the Nansi Lake wetland in Shandong province is are calculated on the basis of MSS, TM and ETM+ remote sensing data collected by the Landsat satellites. The characteristics of seasonal vegetation activity of the Nansi Lake during 1973-2011 were revealed. The seasonal variation characteristics of wetland vegetation in Nansi Lake are not identical in the last 40 years. The NDVI in spring (P=0.0216) first decreased and then increased; The NDVI in summer (P=0.0007) and winter (P=0.0189) present a significant decreasing trend. The variation of the NDVI in autumn is small.

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.

scholarly journals Relationship between remote sensing data and field-observed interril erosion

2018 ◽  
Vol 53 (3) ◽  
pp. 332-341 ◽  
Author(s):  
André Geraldo de Lima Moraes ◽  
Daniel Fonseca de Carvalho ◽  
Mauro Antonio Homem Antunes ◽  
Marcos Bacis Ceddia
Keyword(s):  
Remote Sensing ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Exponential Model ◽  
Landsat 8 ◽  
Sensing Data ◽  
Linear Spectral Mixture Analysis ◽  
Spectral Mixture ◽  
The Relationship

Abstract: The objective of this work was to evaluate the relationship between different remote sensing data, derived from satellite images, and interrill soil losses obtained in the field by using a portable rainfall simulator. The study was carried out in an area of a hydrographic basin, located in Médio Paraíba do Sul, in the state of Rio de Janeiro - one of the regions most affected by water erosion in Brazil. Evaluations were performed for different vegetation indices (NDVI, Savi, EVI, and EVI2) and fraction images (FI), derived from linear spectral mixture analysis (LSMA), obtained from RapidEye, Sentinel2A, and Landsat 8 OLI images. Vegetation indices are more adequate to predict soil loss than FI, highlighting EVI2, whose exponential model showed R2 of 0.74. The best prediction models are generated from the RapidEye image, which shows the highest spatial resolution among the sensors evaluated.

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