Geostatistical Modeling of Air Temperature Using Thermal Remote Sensing
Masoud Minaei,
Foad Minaei
Issue:
Volume 1, Issue 4, November 2017
Pages:
103-109
Received:
15 March 2017
Accepted:
15 May 2017
Published:
12 July 2017
DOI:
10.11648/j.ajese.20170104.11
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Abstract: Geographic Information Systems and spatial interpolation are the most often used geographic sciences for spatial analysis and visualization of temperature to use in hydrological studies. According to dependency of nature of thermal bands data to temperature, using thermal remote sensing images as auxiliary data can be useful in air temperature spatial interpolation. In light of these considerations, we used Landsat thermal bands together with Kriging and Co-kriging geostatistical methods for four seasons to interpolate mean temperature in Northeast of Iran as a region with low density of gauge distribution. Using Landsat (instead of for instance MODIS) is firstly to provide requirement of mentioned science. Secondly, help to provide deeper understand in case of “climatic neighborhood” concept. To assess the efficiency of the method cross validation indicators were used. Thermal images used in this study increase the accuracy for the winter and autumn in comparison to unused outputs. The provided results for spring and summer were good too. Also, the spatial impacts of thermal images on the results of autumn and spring are significant. This research indicated that using thermal images as auxiliary data have potential to improve spatial prediction accuracy and quality. At the end, we know that number of our observation stations are too low and considering the Kriging requirements like normal distribution and stationarity is toilsome but we should consider that this problem exist in the regions with low density of gauges and should find a way to enhance the air temperature interpolation in these cases.
Abstract: Geographic Information Systems and spatial interpolation are the most often used geographic sciences for spatial analysis and visualization of temperature to use in hydrological studies. According to dependency of nature of thermal bands data to temperature, using thermal remote sensing images as auxiliary data can be useful in air temperature spat...
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Determination of Cover-Crop Management Factor (C) for Selected Sites in Imo State Using Remote Sensing Technique and (GIS)
Okore Okay Okorafor,
Christopher Oluwakunmi Akinbile,
Adebayo Jonathan Adeyemo
Issue:
Volume 1, Issue 4, November 2017
Pages:
110-116
Received:
19 June 2017
Accepted:
3 July 2017
Published:
1 August 2017
DOI:
10.11648/j.ajese.20170104.12
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Abstract: Human interference through various activities such as road construction, mineral exploration, lumbering, excavation and rural-urban migration has placed a high demand on the availability of land for agriculture and exposed croplands to extensive degradation and erosion. This study therefore aims at determining the cover-crop management factor (C) for selected sites in Imo State representing different soil groups by the use of remote sensing (RS) and geographical information system (GIS) tools. Satellite Images of the study area were analyzed using ArcGIS 10.1 software on a raster distribution array to generate maps for normalized differential vegetative index (NDVI), Land use land cover (LULC) and crop-cover management factor (C). From the maps generated for NDVI values for the sites were between -0.1035-0.386 and the C-factor values were between 0.33-1.34, thus placing the study area within a region of medium vegetative cover. The location with the lowest NDVI was Okigwe while the highest NDVI value was observed in Ohaji. Though the area lies within the tropical rainforest zone, the vegetation is unevenly distributed thereby creating an enabling environment for soil detachment and sediment transport through runoff from heavy downpours resulting from absence of soil surface resistance. The C-factor values obtained therefore encourages tree planting exercises, forest regeneration activities, shrub development and balanced vegetation maintenance so as to create limited soil surface to encourage soil erodibility and runoff so as to allow agricultural activities which will guarantee food security and sustainable environmental management.
Abstract: Human interference through various activities such as road construction, mineral exploration, lumbering, excavation and rural-urban migration has placed a high demand on the availability of land for agriculture and exposed croplands to extensive degradation and erosion. This study therefore aims at determining the cover-crop management factor (C) f...
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Application Use of Water Quality Index (WQI) and Multivariate Analysis for Nokoué Lake Water Quality Assessment
Josué Esdras Babadjidé Zandagba,
Firmin Mahoutin Adandedji,
Bruno Enagnon Lokonon,
Amédée Chabi,
Oswald Dan,
Daouda Mama
Issue:
Volume 1, Issue 4, November 2017
Pages:
117-127
Received:
21 November 2017
Accepted:
4 December 2017
Published:
3 January 2018
DOI:
10.11648/j.ajese.20170104.13
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Abstract: Due to its location and multiple uses, Nokoué Lake, is subject to multiple attacks impairing water quality. The present work aims at assessing the water quality index (WQI) on this surface water, by monitoring twenty sampling locations for a period of rainy and dry season in 2016. For calculating the WQI, seven parameters, namely, pH, dissolved oxygen, turbidity, electrical conductivity, Biochemical oxygen demand, nitrite and nitrate were considered. Statistical tests and conclusions were made on the basis of a multiparametric model. Thus, to evaluate significant differences among the sites for all water quality variables, data was analyzed using one-way analysis of variance (ANOVA) at 0.05% level of significance. Multivariate analysis of the water quality data sets was performed using Hierarchical Cluster analysis (HCA) and Principal Component Analysis (PCA). The results showed that WQI values ranged from 93.96 (good water quality) in rainy season to 100.73 (bad water quality) in dry season. The values of physicochemical parameters significantly increased from rainy to dry season. Water quality of Nokoué Lake can be categorized into "Good water" during the rainy season to "Poor water" during the dry season. Application of the WQI is suggested as a very helpful tool that enables decision makers to evaluate water quality.
Abstract: Due to its location and multiple uses, Nokoué Lake, is subject to multiple attacks impairing water quality. The present work aims at assessing the water quality index (WQI) on this surface water, by monitoring twenty sampling locations for a period of rainy and dry season in 2016. For calculating the WQI, seven parameters, namely, pH, dissolved oxy...
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