Using geospatial technologies to delineate Ground Water Potential Zones(GWPZ)in Mberengwa and Zvishavane District,Zimbabwe

The main objective of the study was to delineate Ground Water Potential Zones(GWPZ)in Mberengwa and Zvishavane districts,Zimbabwe,utilizing geospatial technologies and thematic mapping.Various factors,including geology,soil,rainfall,land use/land cover,drainage density,lineament density,slope,Terrain Ruggedness Index(TRI),and Terrain Wetness Index(TWI),were incorporated as thematic layers.The Multi Influencing Factor(MIF)and Analytical Hierarchical Process(AHP)techniques were employed to assign appropriate weights to these layers based on their relative significance,prioritizing GWPZ mapping.The integration of these weighted layers resulted in the generation of five GWPZ classes:Very high,high,moderate,low,and very low.The MIF method identified 3%of the area as having very high GWPZ,19%as having high GWPZ,40%as having moderate GWPZ,24%as having low GWPZ,and 14%as having very low GWPZ.The AHP method yielded 2%for very high GWPZ,14%for high GWPZ,37%for moderate GWPZ,37%for low GWPZ,and 10%for very low GWPZ.A strong correlation(ρof 0.91)was observed between the MIF results and groundwater yield.The study successfully identified regions with abundant groundwater,providing valuable target areas for groundwater exploitation and highvolume water harvesting initiatives.Accurate identification of these crucial regions is essential for effective decision-making,planning,and management of groundwater resources to alleviate water shortages.

Predicting Levels of Crude Protein, Digestibility, Lignin and Cellulose in Temperate Pastures Using Hyperspectral Image Data

Hyperspectral sensors provide the potential for direct estimation of pasture feed quality attributes. However, remote sensing retrieval of digestibility and fibre (lignin and cellulose) content of vegetation has proven to be challenging since tissue optical properties may not be propagated to the canopy level in mixed cover types. In this study, partial least squares regression on spectra from HyMap and Hyperion imagery were used to construct predictive models for estimation of crude protein, digestibility, lignin and cellulose concentration in temperate pastures. HyMap and Hyperion imagery and field spectra were collected over four pasture sites in southern Victoria, Australia. Co-incident field samples were analyzed with wet chemistry methods for crude protein, lignin and cellulose concentration, and digestibility was calculated from fiber determinations. Spectral data were subset based on sites and time of year of collection. Reflectance spectra were extracted from the hyperspectral imagery and collated for analysis. Six different transformations including derivatives and continuum removal were applied to the spectra to enhance absorption features sensitive to the quality attributes. The transformed reflectance spectra were then subjected to partial least squares regression, with full cross-validation “leave-one-out” technique, against the quality attributes to assess effects of the spectral transformations and post-atmospheric smoothing techniques to construct predictive models. Model performance between spectrometers, subsets and attributes were assessed using a coefficient of variation (CV), —the interquantile (IQ) range of the attribute values divided by the root mean square error of prediction (RMSEP) from the models. The predictive models with the highest CVs were obtained for digestibility for all spectra types, with HyMap the highest. However, models with slightly lower CVs were obtained for crude protein, lignin and cellulose. The spectral regions for diagnostic wavelengths fell within the chlorophyll well, red edge, and 2000-2300 nm ligno-cellulose-protein regions, with some wavelengths selected between the 1600 and 1800 nm region sensitive to nitrogen, protein, lignin and cellulose. The digestibility models with the highest CV’s had confidence intervals corresponding to ±5% digestibility, which constitutes approximately 30% of the measured range. The cellulose and lignin models with the highest CV’s also had similar confidence intervals but the slopes of the prediction lines were substantially less than 1:1 indicating reduced sensitivity. The predictive relationships established here could be applied to categorizing pasture quality into range classes and to determine whether pastures are above or below for example threshold values for livestock productivity benchmarks.

Relationship between Land Cover Changes with Water Quantity in Lake Victoria—A Case Study of Mara River Basin in Tanzania

LV(Lake Victoria)is valuable to the East African sub region and Africa in general,sources of water for LV are from its catchment areas and tributaries e.g.Kagera and Mara Rivers on Tanzania part.Apparently,catchment areas in proximities of LV and on MR(Mara River),indeed on MRB(Mara River Basin)in particular,are experiencing increased anthropogenic activities such as mining,fishing,settlements,agriculture etc.,which lead to increased water usage,land degradation and environmental pollution.Such activities threaten the sustainability of the environment surrounding MRB and impliedly LV and its ecosystem.The level of water in LV is reported to be declining threatening its extinction.This paper is reporting on a study undertaken to establish the relationship between land cover changes with ground water discharge from specifically MRB into LV over the period of 24 years,i.e.1986 to 2010.Methodology used is assessment of vegetation changes by using remote sensing through analysis of TM(Thematic Mapper)Landsat Images of 1986,1994,2002 and 2010 ETM(Enhanced Thematic Mapper)Landsat images,from which respective land cover change maps were generated and compared with ground water levels from MRB.Results indicates that there is a significant decline of land cover and ground water flowing into LV from MRB,and that there is positive correlation between land cover changes and the quantity of ground water flowing from MRB to LV.This phenomenon is common to all tributaries of LV,thus leading to decline of water in LV.It is recommended that relevant government institutions should endeavor formulating policies to control excessive use of wetlands and drylands in the proximity of LV and MRB in particular,such that the flow of water to LV may be sustained.

Assessment of Supervised Classifiers for Land Cover Categorization Based on Integration of ALOS PALSAR and Landsat Data

Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) (traditional supervised classifier) in forest resources and land cover categorization, based on combination of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat Thematic Mapper (TM) data, in Northern Tanzania. Various data categories based on Landsat TM surface reflectance, ALOS PALSAR backscattering and their derivatives were generated for various classification scenarios. Then a separate and joint processing of Landsat and ALOS PALSAR data were executed using SVM, NN, RF and ML classifiers. The overall classification accuracy (OA), kappa coefficient (KC) and F1 score index values were computed. The result proves the robustness of SVM and RF in classification of forest resource and land cover using mere Landsat data and integration of Landsat and PALSAR (average OA = 92% and F1 = 0.7 to 1). A two sample t-statistics was utilized to evaluate the performance of the classifiers using different data categories. SVM and RF indicate there is no significance difference at 5% significance level. SVM and RF show a significant difference when compared to NN and ML. Generally, the study suggests that parametric classifiers indicate better performance compared to parametric classifier.

Sustainable Environmental Management:A Case of Assessment of Land Degradation Using Space Technologies in Longido District,Arusha Tanzania

This study was conducted to assess land degradation in Longido District,Arusha,Tanzania using remote sensing techniques.Biophysical degradation indicators i.e.land use/land cover,land productivity level and soil erosion were used.Specifically,Landsat Satellite images of 1995 and 2015,together with soil data and digital elevation model were applied.Land cover maps of the study area over the study years were produced by supervised classification method.Soil erosion was assessed using RUSLE(Revised Universal Soil Loss Equation)model producing soil erosion map of Longido district,the inputs into the RUSLE model were rainfall,erosivity factor,soil erodibility factor,slope steepness and slope length factor,cover management factor and support practice factor.Biophysical land degradation map was produced by applying weighted overlay technique whereby soil erosion was given more weight followed by land use/land cover of 2015 and land productivity level of 2015.The findings show that about 38% of Longido district areas are highly vulnerable to land degradation which is above the international allowable level.It is being concluded that Longido District is at high risk of failure to sustain livelihood of and resilient for its communities,the earth in general,so it is timely for the district authorities to take steps towards mitigating further land degradation.It is being recommended that sustainable conservation and management strategies as well as policies must be affected by district authorities including farmers and pastoralists to improvise land degradation friendly cultivation and grazing methods.

A Review of the Causes of Land Use Conflicts between Farmers and Pastoralists in Tanzania and a Proposal for Resolutions

Land use conflicts are complex disputes that contribute at large in terms of negative social and economic impacts within the heterogeneous societies.The mechanisms of success for land use conflict resolution still need further research because of various mindsets of the people.In this paper,the issues of land conflicts between farmers and pastoralists in Tanzania mainland which could lead to low economic development are reviewed and the general causes and effects of land use conflicts are outlined.Poor land governance,inappropriate of land use plans,inadequate land policies,land tenure insecurity,corruption and population increases are cited as being among of the main offenders fuelling land use conflicts in Tanzania.

测高-重力边值问题的局部剪裁解

给出了基于最小方差原理的测高 重力超定边值问题解的局部建模剪裁解法 ,并采用由卫星测高资料计算的 6′× 6′海洋重力异常和 3 0′× 3 0′大地水准面数据 ,计算了展开到 1 80 0阶次的中国南海及邻近海域的测高 重力试验模型。与其他模型比较表明 ,这种由超定剪裁法解得到的局部模型能够较好地改善重力场模型中波长部分的精度。

利用地理挖掘概念推动地理知识获取的可行性研究:第一篇

总结了近阶段一系列的多媒体用于检索各种为空间信息提供图形接口的方式。在20世纪 80年代这种方法导致产生了交互式的原型激光碟,用于评价媒体在信息发布上的有效性,随后提出并发展了地理挖掘概念。