Land Suitability Assessment of the Proposed Uranium Mining Area in North-East Botswana

2013 ◽  
pp. 617-631 ◽  
Author(s):  
Oagile Dikinya
Keyword(s):  
Mining Area ◽  
Uranium Mining ◽  
Land Suitability ◽  
North East ◽  
Suitability Assessment ◽  
Land Suitability Assessment ◽  
Uranium Mining Area

scholarly journals Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 223
Author(s):  
Rubaiya Binte Mostafiz ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Vegetation Indices ◽  
Land Suitability ◽  
Assessment System ◽  
Yield Estimation ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

scholarly journals The Mineral Chemistry of Chlorites and Its Relationship with Uranium Mineralization from Huangsha Uranium Mining Area in the Middle Nanling Range, SE China

Minerals ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 199 ◽  
Author(s):  
Dehai Wu ◽  
Jiayong Pan ◽  
Fei Xia ◽  
Guangwen Huang ◽  
Jing Lai
Keyword(s):  
Mining Area ◽  
Uranium Mining ◽  
Uranium Mineralization ◽  
Formation Mechanisms ◽  
Chemical Compositions ◽  
Type I ◽  
Low Oxygen ◽  
Nanling Range ◽  
The Relationship ◽  
Uranium Mining Area

The Huangsha uranium mining area is located in the Qingzhangshan uranium-bearing complex granite of the Middle Nanling Range, Southeast China. This uranium mining area contains three uranium deposits (Liangsanzhai, Egongtang, and Shangjiao) and multiple uranium occurrences, showing favorable mineralization conditions and prospecting potential for uranium mineral resources. Chloritization is one of the most important alteration types and prospecting indicators in this mining area. This study aims to unravel the formation environment of chlorites and the relationship between chloritization and uranium mineralization, based on detailed field work and petrographic studies of the wallrock and ore samples from the Huangsha uranium mining area. An electron probe microanalyzer (EPMA) was used in this study to analyze the paragenetic association, morphology, and chemical compositions of chlorite, to classify chemical types and to calculate formation temperatures and n(Al)/n(Al + Mg + Fe) values of chlorite. The formation mechanism and the relationship with uranium mineralization of the uranium mining area are presented. Some conclusions from this study are: (1) There are five types of chlorites, including the chlorite formed by the alteration of biotite (type-I), by the metasomatism of feldspar with Fe–Mg hydrothermal fluids (type-II), chlorite vein/veinlet filling in fissures (type-III), chlorite closely associated with uranium minerals (type-IV), and chlorite transformed from clay minerals by adsorbing Mg- and Fe-components (type-V). (2) The chlorite in the Huangsha uranium mining area belongs to iron-rich chlorite and is mainly composed of chamosite, partly clinochlore, which are the products of multiple stages of hydrothermal action. The original rocks are derived from argillite, and their formation temperatures vary from 195.7 °C to 283.0 °C, with an average of 233.2 °C, suggesting they formed under a medium to low temperature conditions. (3) The chlorites were formed under reducing conditions with low oxygen fugacity and relatively high sulfur fugacity through two formation mechanisms: dissolution–precipitation and dissolution–migration–precipitation; (4) The chloritization provided the required environment for uranium mineralization, and promoted the activation, migration, and deposition of uranium.

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