Biomass Estimation Models for Cocoa (Theobroma cacao) Plantations in Ghana, West Africa

The role of cocoa systems for climate change mitigation and adaptation has increased substantially because of their capability to trap carbon dioxide from the atmosphere and deposited in the cocoa trees as carbon. Development of aboveground biomass (AGB) models for cocoa plantations is crucial for accurate estimation of carbon stocks in the cocoa systems, however, allometric models for estimating AGB for cocoa plantations remain a challenge for cocoa producing countries in Sub-Saharan Africa especially Ghana. The aim of this study is to develop allometric model that can be used for the estimation of AGB for cocoa plantations in Ghana, as well as West Africa. Destructive sampling was carried out on 110 cocoa trees obtained from the cocoa rehabilitation exercise for the development of the allometric models. Diameter at breast height (D), total tree height (H) and wood density (ρ) were used as predictors to develop seven models. The best model was selected based on coefficient of determination (R2), index of agreement (IA), root mean squared error (RMSE), bias (E%), mean absolute error (MAE) and corrected akaike information criterion (AICC) and percentage relative standard error (PRSE) of the estimated parameters. The selected model, which was the one with the predictors D and ρ, was given as;AGB = 0.7217ρ(D2)0.921. It was compared with the Yuliasmara et al. (2009) cocoa model using equivalence test and paired sample t-test. The two models were found to be equivalent within ±10% of their mean predictions (p < 0.0001) for one-tailed tests for both lower and upper limits, while the paired sample t-test rejected the null hypothesis with mean difference of 14.16 kg between the two models. This study is significant because it has provided a model to estimate AGB for the cocoa plantations in Ghana which is very important for the Ghana Cocoa-Forest REDD+ Programme and also can be used by other West African cocoa producing countries.

Application of Parametric and Non Parametric Classifiers for Assessing Land Use/Land Cover Categories in Cocoa Landscape of Juaboso and Bia West Districts of Ghana

Satellite image classification has been used for long time in the field of remote sensing since classification results are used in environmental research, agriculture, climate change and natural resource management. The cocoa landscape of Ghana is complex and diverse in nature, composing of mixture of closed forest, open forest, settlements, croplands and cocoa farms which make mapping the landscape difficult. The purpose of this research is to assess and compare the classification performances of three machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and a statistical classification algorithm: Maximum Likelihood (ML) to know which classifier is best suited for mapping the cocoa landscape of Ghana using Juaboso and Bia West districts of Ghana as study area. A representative sampling approach was adopted to collect 1246 sample points for the various Land Use/Land Cover (LULC) types. These sample points were divided at random into 869 which form 70% for classification and 377 which constitute 30% of the total sample points for validation. The Stacked sentinel-2 image, classification data and validation data storing the identities of the LULC classes were imported in R to run supervised classification for each classifier. The classification results show that the highest overall accuracy and kappa statistics were produced by the support vector machine (86.47%, 0.7902);next is the artificial neural network (85.15%, 0.7700), followed by the random forest (84.08%, 0.7559) and finally the maximum likelihood (78.51%, 0.6668). The final LULC map produced under this study can be used to monitor cocoa driven deforestation especially in the gazetted forest and game reserves. This map will also be very useful in the national forest monitoring framework for the REDD + cocoa landscape project.

Influence of Temperature and Frequency on Microwave Dielectric Properties of Lunar Regolith Simulant

The dielectric constant of the lunar regolith can directly influence the reflection coefficient and the trans-mission coefficient of the Moon′s surface, and plays an important role in the Moon research. In order to study the di-electric properties of the lunar regolith, the lunar regolith simulant was made according to the making procedure of the CAS-1 simulant made by Chinese Academy of Sciences. Then the dielectric constants of the lunar regolith simulant were measured with 85070E Aiglent Microwave Network Analyzer in the frequency ranging from 0.2 GHz to 20.0 GHz and at temperature of 25.1℃, 17.7℃, 13.1℃, 11.5℃, 9.6℃, 8.0℃, 4.1℃, -0.3℃, -4.7℃, -9.5℃, -18.7℃, -27.7℃, and -32.6℃, respectively. The Odelevsky model was employed to remove the influence of water in the air on the final effective dielectric constants. The results indicate that frequency and temperature have apparent influences on the dielectric constants of the lunar regolith simulant. The real parts of the dielectric constants increase fast over the range of 0.2 GHz to 3.0 GHz, but decrease slowly over the range of 4.0 GHz to 20.0 GHz. The opposite phenomenon occurs in the imaginary parts. The influences of the frequency and temperature on the brightness temperature were also estimated based on the radiative transfer equation. The result shows that the variation of the frequency and temperature results in great changes of the microwave brightness temperature emitting from the lunar regolith.

基于嫦娥一号卫星微波辐射计数据的月球Cabeus撞击坑水冰含量研究

月球极地水冰存在与否、存在形式和存在数量等科学问题,是当前月球科学研究的重要目标之一.2009年10月9日,美国半人马座火箭和卫星相继撞击月球南极Cabeus撞击坑,证实了月球极地水冰的存在,但对其含量、分布范围等的研究还有争议.极地水冰的存在会改变极地月壤的介电常数,而微波辐射计是获取介质介电常数的最有效的工具.因此,根据37GHz频率条件下的月壤被动微波辐射传输数值模拟结果,改进月壤辐射传输模型.对比分析了Odelevsky模型、Wagner和Landau-Lifshitz模型、Clausius模型等8个常用的混合介电常数计算模型,表明Lichtenecker模型与改进的Dobson模型得到的介电常数值相差最大,折射模型得到的结果偏大,Odelevsky模型、强起伏定理与Wagner和Landau-Lifshitz模型、Clausius模型、Bruggeman-Hanai模型得到的结果非常接近.基于Odelevsky模型和改进的月壤辐射传输模拟,建立了月壤体积含冰量与微波辐射亮温的关系.并根据嫦娥一号卫星获取的相应的微波辐射计数据,进行了Cabeus撞击坑水冰含量反演研究.结果表明,改进的辐射传输模型适用于高频条件下的一层月壤辐射传输模拟;月球南极Cabeus撞击坑地区月壤微波辐射亮温为69.93K(37GHz),相应的体积含冰量约为2.8%.

Research on water ice content in Cabeus crater using the data from the microwave radiometer onboard Chang’e-1 satellite

The existence, formation and content of water ice in the lunar permanently shaded region is one of the important questions for the current Moon study. On October 9, 2009, the LCROSS mission spacecraft impacted the Moon, and the initial result verified the existence of water on the Moon. But the study on formation and content of water ice is still under debate. The existence of water ice can change the dielectric constants of the lunar regolith, and a microwave radiometer is most sensitive to the dielectric parameters. Based on this, in this paper, the radiation transfer model is improved according to the simulation results in high frequency. Then the mixture dielectric constant models, including Odelevsky model, Wagner and landau-Lifshitz model, Clau-sius model, Gruggeman-Hanai model, etc., are analyzed and compared. The analyzing results indicate that the biggest difference occurs between Lichtenecker model and the improved Dobson model. The values estimated by refractive model are the second biggest in all the models. And the results from Odelevsky model, strong fluctuation model, Wagner and Landau –Lifshitz model, Clausius model and Bruggeman-Hanai model are very near to each other. Thereafter, the relation between volume water ice content and microwave brightness temperature is constructed with Odelevsky mixing dielectric model and the improved radiative transfer simulation, and the volume water ice content in Cabeus crater is retrieved with the data from microwave radiometer onboard Chang’e-1 satellite. The results present that the improved radiative transfer model is proper for the brightness temperature simulation of the one infinite regolith layer in high frequency. The brightness temperature in Cabeus crater is 69.93 K (37 GHz), and the corresponding volume water ice content is about 2.8%.