Land use change and its drivers in Kurt Bahir wetland,north-western Ethiopia

The effects of land-use change on ecosystem services in Kurt Bahir wetland were investigated during 2013 to 2014 using LANDSAT satellite images from 1973, 1986, 2002 and 2013 to identify and map changes in land-use classes over time. To understand perceptions of changes in ecosystem services that resulted from observed land-use changes discussions were held with local key informants and focus group participants. Accuracy assessment was done on the 2013 classified image. In 1973, the land-use classes of the wetland comprised wetland and grazing land, with area coverages of 764.5 and 268.4 ha, respectively. The land-use classes in 1986 were wetland, grazing land and cultivated land, with area coverages of 585.7, 198.3 and 248.9 ha, respectively. The land use of the study area in 2002 was similar to that of 1986. In 2013–2014, however, there was a substantial increase in cultivation and grazing lands at the expense of the wetland, as compared to 2002. Respondents confirmed that free grazing, the harvesting of vegetation, deforestation and expansion of cultivated land were the major threats. This study indicated that land-use change is the main driver of wetland degradation and loss. It is recommended that government organisations should give due emphasis to overcome the anthropogenic effects and to develop a management plan for the sustainable utilisation of the wetland’s ecosystem services.     

‘This Painting Becomes His Body for Life’: Transforming Relations in Yolŋu Initiation and Funeral Rituals

ABSTRACT

Among the most striking images produced in north-east Arnhem Land today are the paintings given to young boys during their first initiation ceremony (dhapi). Skilfully applied on their chest over several hours, while singing and dancing proceeds on the ceremonial grounds nearby, these body paintings act as relational matrixes which locate the initiands within a socio-cosmic web of connections. At the other end of the male ritual life-cycle, the bodies of the deceased undergo a similar process of transfiguration, as they are made to resemble the groups’ most sacred objects, seen to instantiate the powers of specific ancestral beings. In the context of these rituals, the links between clans, places, and ancestral beings are expressed by being made visible on and around the body. Pragmatically composed and displayed for all to see, I suggest that Yol?u ritual images appear as ‘matter(s) of relations’ par excellence, materialising various sets of social relationships. This paper examines the material logics behind this transfiguration process which, by turning people into ancestors, transform the relations between individuals and groups, between humans and non-human beings, and between the living and the spirits of the dead.     

基于深度学习街景影像解译和景感生态学的视域环境定量解读

城市物理环境带给居民丰富而生动的视觉意象，目前许多文献结果表明其宜人性与公共福祉以及健康状况息息相关。景感生态为探究城市物理环境与居民感知信息之间的联系提供了指导依据，通过人本尺度的定量手段解读城市环境中视觉、听觉和味觉等多维度感知信息。秉承景感生态学的基本原理，引入一种结合街景大数据和深度学习的城市环境量化手段，以北京市六环范围为例，将景感视率作为测度对人本视角下的城市环境展开定量解读。在全面把控多维景感要素的同时，旨在实现以人为本的城市物理环境优化设计，从而满足人们对生活品质提升的实际需求。实验结果显示：（1）从视觉感受的宏观表现来看，北京四环路范围内建成环境的"闭合感"较强，而对绿植的感知程度相对偏弱，因而需要开展存量环境设计并优化视域界面结构；（2）以景感视率作为特征值进行聚类得出3类主导空间（绿色空间、灰色空间和蓝色空间），可针对灰色空间着重部署垂直绿化资源，提高城市视觉绿化的可感知性，从而营造舒适宜人的绿色氛围、促进公众身心健康；（3）为景感生态学提供了基于大数据思维的数据集和定量方法补充。综上，以街景影像和景感生态视角对北京市中心城区的视域环境展开定量分析，采用了先进的深度学习框架（Detectron2）并结合经典的机器学习方法（K-Means）对人本视域内多维景感要素的空间分布特征进行解读。借助景感生态规划可以有针对性的改善城市视域界面的感知质量，提升智能管理水平，帮助城市规划设计人员和管理者从人本视角提升城市公共环境品质和风貌。     

Non-destructive analysis of root growth in porous media

A technique is described for non-destructive observations and analysis of root growth in granular media, using time-lapse video in conjunction with a pressure cell. The pressure cell consists of one half of a traditional triaxial cell with a flat clear perspex front allowing growth of the root to be monitored using a video camera. The cell is connected to an external pressure supply, which is used to regulate precisely the confining pressure, and hence, the physical impedance to root growth. Preliminary results of the growth of root axes and the emergence of laterals of peas over a range of physical impedances are presented to illustrate the potential of this technique in studying root growth.     

An automatic method for removing empty camera trap images using ensemble learning

Camera traps often produce massive images, and empty images that do not contain animals are usually overwhelming. Deep learning is a machine‐learning algorithm and widely used to identify empty camera trap images automatically. Existing methods with high accuracy are based on millions of training samples (images) and require a lot of time and personnel costs to label the training samples manually. Reducing the number of training samples can save the cost of manually labeling images. However, the deep learning models based on a small dataset produce a large omission error of animal images that many animal images tend to be identified as empty images, which may lead to loss of the opportunities of discovering and observing species. Therefore, it is still a challenge to build the DCNN model with small errors on a small dataset. Using deep convolutional neural networks and a small‐size dataset, we proposed an ensemble learning approach based on conservative strategies to identify and remove empty images automatically. Furthermore, we proposed three automatic identifying schemes of empty images for users who accept different omission errors of animal images. Our experimental results showed that these three schemes automatically identified and removed 50.78%, 58.48%, and 77.51% of the empty images in the dataset when the omission errors were 0.70%, 1.13%, and 2.54%, respectively. The analysis showed that using our scheme to automatically identify empty images did not omit species information. It only slightly changed the frequency of species occurrence. When only a small dataset was available, our approach provided an alternative to users to automatically identify and remove empty images, which can significantly reduce the time and personnel costs required to manually remove empty images. The cost savings were comparable to the percentage of empty images removed by models.     

高原草被退化程度的遥感定量监测——以甘肃省玛曲县为例

以加权草被盖度作为草被退化的一种标识值,来标记草场沙化和毒杂草侵蚀的程度,并以机器辨识方法从TM/ETM数据中定量提取加权草被盖度现状及变迁信息。变迁研究的时段从1994年至2008年。为了使图像辨识特征量对高寒草被盖度敏感,提出了植被指数分级密度、植被相对饱和度分级密度等8个新的数学描述符,逐一进行了与加权草被盖度的相关性分析或分割实验;并通过它们的组合训练学习机和实现了对不同盖度草被的划分。通过野外采样数据检核,这种分类的准确率接近或达到80%。在草被盖度正确分类的基础上,通过调整减少不同时相图像照度差异的影响,进一步实现了加权草被盖度变迁信息的自动化提取。     

基于TM遥感影像的森林资源线性规划与优化配置研究

山东黄河流域是黄河入海的最后区域,也是水土流失最为严重的地区之一,基于遥感影像和数学方法优化配置森林资源对区域尺度的水土保持具有重要意义。选取2000年、2006年时相相近的TM遥感影像作为数据源,采用监督分类法、专家分类法以及GPS、GIS等技术对森林资源进行分类、统计、验证和分析。在层次分析法确定森林资源、森林景观、环境和社会经济等指标因子权重的基础上,利用线性规划法对森林资源的空间格局进行优化。结果表明,区域森林资源空间分布极不均匀,且主要集中于鲁中南山地及丘陵地区。6a间,森林资源增长相对缓慢,宜林荒山荒地的面积增加了8.2%,到2006年,其面积高达238955.7 hm2。对宜林荒山荒地进行线性优化后,森林类型和结构得到明显改善,土地利用结构信息墒降低了8.4%,森林资源和土地利用空间结构有序度明显提高。最后,对立地条件不同的地区采取了相应的树种配置措施。