Variability of spectral reflectance and vegetation indices in tallgrass prairie: spatio-temporal analysis using semivariograms and close-range remote sensing

1998 ◽  
Author(s):  
D.G. Goodin ◽  
G.M. Henebry
Keyword(s):  
Remote Sensing ◽  
Tallgrass Prairie ◽  
Spectral Reflectance ◽  
Vegetation Indices ◽  
Temporal Analysis ◽  
Close Range ◽  
Spatio Temporal

scholarly journals A comparison of satellite-based indices for hazard assessment of MSW open dumps using spatial analysis

2018 ◽  
Vol 37 (3) ◽  
pp. 219-236 ◽  
Author(s):  
Khalid Mahmood ◽  
Zia Ul-Haq ◽  
Fiza Faizi ◽  
Syeda A. Batol
Keyword(s):  
Hazard Assessment ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Moving Average ◽  
Vegetation Indices ◽  
Temporal Analysis ◽  
Zone Size ◽  
Vegetation Health ◽  
Spatio Temporal ◽  
Hazardous Zone

This study compares the suitability of different satellite-based vegetation indices (VIs) for environmental hazard assessment of municipal solid waste (MSW) open dumps. The compared VIs, as bio-indicators of vegetation health, are normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI) that have been subject to spatio-temporal analysis. The comparison has been made based on three criteria: one is the exponential moving average (EMA) bias, second is the ease in visually finding the distance of VI curve flattening, and third is the radius of biohazardous zone in relation to the waste heap dumped at them. NDVI has been found to work well when MSW dumps are surrounded by continuous and dense vegetation, otherwise, MSAVI is a better option due to its ability for adjusting soil signals. The hierarchy of the goodness for least EMA bias is MSAVI> SAVI> NDVI with average bias values of 101 m, 203 m, and 270 m, respectively. Estimations using NDVI have been found unable to satisfy the direct relationship between waste heap and hazardous zone size and have given a false exaggeration of 374 m for relatively smaller dump as compared to the bigger one. The same false exaggeration for SAVI and MSAVI is measured to be 86 m and -14 m, respectively. So MSAVI is the only VI that has shown the true relation of waste heap and hazardous zone size. The best visualization of distance-dependent vegetation health away from the dumps is also provided by MSAVI.

scholarly journals Sustainable use of an extractivist reserve: a spatial-temporal analysis of environmental dynamics

2017 ◽  
Vol 4 (7) ◽  
pp. 195-201
Author(s):  
Joélia Natália Bezerra da Silva ◽  
Janaína Vital de Albuquerque ◽  
Luana de Oliveira Rodrigues
Keyword(s):  
Remote Sensing ◽  
Temporal Dynamics ◽  
Vegetation Indices ◽  
Sustainable Use ◽  
Temporal Analysis ◽  
Conservation Unit ◽  
Climatic Regions ◽  
Cardisoma Guanhumi ◽  
Surrounding Areas ◽  
Spatial Temporal Analysis

Due to its large territory, Brazil has different climatic regions, which determines biome variations and equally diverse ecosystems, of this variety of vegetal landscapes, accompanies the diversity of climates. In this context, results of studies carried out locally, which guide measures, decision-making laws and regulations that reach large scales in the territory, need to be carefully planned, because there is a high risk of disregarding environmental specificities of the studied areas. Therefore, this study aimed to analyze the environmental dynamics resulting from the impacts of the last decades that have affected the habitat of the guaiamum (Cardisoma guanhumi) in the Acaú-Goiana Extractivist Reserve (RESEX) and surrounding areas. The analysis of the spatial-temporal dynamics, in the RESEX and adjacent areas, was made from the vegetation indices (SAVI) through remote sensing. In this way, three images of the RESEX were analyzed, two from the year 2010 and one from 2015, in which the RESEX was already in full legal operation. It is noticeable that there are some areas within the Conservation Unit with small plots of exposed soil, which can demonstrate the occurrence of fires.

Spatio-temporal Analysis of Shoreline Positional Change of Ondo State Coastline Using Remote Sensing and GIS: A Case Study of Ilaje Coastline at Ondo State in Nigeria

2021 ◽  
Author(s):  
Akinola Adesuji Komolafe ◽  
Paul Ayodeji Apalara ◽  
Matthew Olomolatan Ibitoye ◽  
Abiodun Olufemi Adebola ◽  
Idowu Ezekiel Olorunfemi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Analysis ◽  
Remote Sensing And Gis ◽  
Positional Change ◽  
Ondo State ◽  
Spatio Temporal

scholarly journals SPATIO-TEMPORAL DYNAMIC OF LAND DEGRADATION USING REMOTE SENSING-BASED INDEX

AGROFOR ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Seyed Hamidreza SADEGHI ◽  
Fahimeh MIRCHOOLI ◽  
Abdulvahed KHALEDI DARVISHAN
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Net Primary Productivity ◽  
Temporal Analysis ◽  
Mean Value ◽  
Annual Rainfall ◽  
Landsat Images ◽  
Policy Makers ◽  
Use Efficiency ◽  
Spatio Temporal

Land degradation is the major issue which affect watershed sustainability and following social, economic and environmental of livelihood people. So, early detection of land degradation is necessary for policy-makers to make appropriate decision. In this way, remote sensing method is a candidate choice for assessments and monitoring. In this study, land degradation was assessed using Rain-Use Efficiency (RUE) in the Shazand Watershed, Iran in 1986, 1998, 2008 and 2016. Thus, annual rainfall was calculated using inverse distance weight (IDW), net primary productivity (NPP) were calculated using Landsat images. The results indicated that RUE had increasing and then decreasing trends which were 10.66, 33.77, 20.03 and 9.47 kg C ha-1 yr-1. The results also illustrate that the mean value of RUE in different land uses varied between the irrigated land and orchard that had the highest value and outcrop dominant areas and bareland had the lowest value of RUE among land use categories. It is also established that spatio-temporal analysis of RUE can provide valuable information about the trend of watershed’s sustainability over years.

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