Detecting pruning of individual stems using Airborne Laser Scanning data captured from an Unmanned Aerial Vehicle

Modern forest management involves implementing optimal pruning regimes. These regimes aim to achieve the highest quality timber in the shortest possible rotation period. Although a valuable addition to forest management activities, tracking the application of these treatments in the field to ensure best practice management is not economically viable. This paper describes the use of Airborne Laser Scanner (ALS) data to track the rate of pruning in a Eucalyptus globulus stand. Data is obtained from an Unmanned Aerial Vehicle (UAV) and we describe automated processing routines that provide a cost-effective alternative to field sampling. We manually prune a 500 m² plot to 2.5 m above the ground at rates of between 160 and 660 stems/ha. Utilising the high density ALS data, we first derived crown base height (CBH) with an RMSE of 0.60 m at each stage of pruning. Variability in the measurement of CBH resulted in both false positive (mean rate of 11%) and false negative detection (3.5%), however, detected rates of pruning of between 96% and 125% of the actual rate of pruning were achieved. The successful automated detection of pruning within this study highlights the suitability of UAV laser scanning as a cost-effective tool for monitoring forest management activities.     

区域农用地变化遥感调查技术框架研究

介绍了区域农用地变化遥感调查技术框架,重点从业务应用的角度分析了分类系统、变化信息提取与表达、小地物面积扣除和数据处理流程等关键技术,对存在的问题进行了进一步探讨。     

Institutional interaction to address greenhouse gas emissions from international transport: ICAO, IMO and the Kyoto Protocol

In response to Article 2.2 of the Kyoto Protocol, the International Maritime Organisation (IMO) and the International Civil Aviation Organisation (ICAO) have begun to consider greenhouse gas (GHG) emissions from international aviation and shipping. However, neither ICAO nor IMO have taken any effective action on the issue yet and progress can be characterised as slow. The lack of action has so far not been made up for by measures within the climate change regime or by individual countries. An important motivation for the efforts of ICAO and IMO so far has been the potential regulatory competition with the climate change regime. However, given the lack of political will to act on the issue within the latter, this motivation has not been very forceful. Against this backdrop, I argue that there are in particular three options for furthering progress within ICAO and IMO, namely (1) enhancing the threat of regulation of GHG emissions from international transport under the climate change regime; (2) undertaking unilateral domestic action by various countries (in particular the EU); and (3) furthering a learning process within ICAO and IMO. Furthermore, a closer coordination of efforts under ICAO, IMO and the climate change regime could facilitate and accelerate progress.     

陆地表层碳循环模型研究及其趋势

对国外陆地表层碳循环ＯＢＭ、ＴＥＭ、ＣＡＳＡ、ＣＡＲＡＩＢ、ＤＥＭＥＴＥＲ－１、ＩＭＡＧＥ２．０、ＳＩＢ２等模型进行了评述,同时也简要介绍了国内陆地表层碳循环模型的研究。通过对国内外研究现状的对比分析,我们可以看到国外陆地表层碳循环模型的发展特点：已经从静态模型转向动态模型,综合考虑了动力学特点,并且集成陆地表层碳循环的各个过程,与气候模型耦合研究陆地表层对全球变化的响应,建立生态生理模型和决定植被对气候反应的植物生态生理机制;研究陆地生态系统与大气之间的动态响应和相互作用,揭示其中的反馈机制;加强各种情景研究,同时包括人为的影响,尤其是土地利用和土地覆盖变化对陆地碳存储的影响,预测未来全球陆地表层的动态变化及其反馈;由于遥感数据已经成为全球变化研究重要的数据来源,遥感、地理信息系统技术在陆地表层碳循环研究中得到了重视和应用。这些正是我国陆地表层碳循环模型研究应注意和加强的方面。     

全球变化科学的进展

全球变化科学对揭示和理解人类赖以生存的地球系统运转的机制、变化规律及人类活动对地球表层环境系统的影响具有重要的意义。从深海氧同位素曲线的建立与米兰科维奇理论的发展、高分辨率冰芯记录的研究、短周期气候变化、突变事件的发现、南北半球古气候对比的研究以及我国学者对过去全球变化研究的贡献等角度论述了地球环境变化历史的研究进展；并在对温室效应和全球气候变暖、臭氧洞、土地利用和土地覆盖的变化以及水资源等研究和现代观测资料分析的基础上，阐述了人类活动对地球环境的影响；提出了当前全球变化科学研究的发展趋势以及在新世纪全球变化科学所面临的挑战。     

Kriging and Semivariogram Deconvolution in the Presence of Irregular Geographical Units

This paper presents a methodology to conduct geostatistical variography and interpolation on areal data measured over geographical units (or blocks) with different sizes and shapes, while accounting for heterogeneous weight or kernel functions within those units. The deconvolution method is iterative and seeks the point-support model that minimizes the difference between the theoretically regularized semivariogram model and the model fitted to areal data. This model is then used in area-to-point (ATP) kriging to map the spatial distribution of the attribute of interest within each geographical unit. The coherence constraint ensures that the weighted average of kriged estimates equals the areal datum.This approach is illustrated using health data (cancer rates aggregated at the county level) and population density surface as a kernel function. Simulations are conducted over two regions with contrasting county geographies: the state of Indiana and four states in the Western United States. In both regions, the deconvolution approach yields a point support semivariogram model that is reasonably close to the semivariogram of simulated point values. The use of this model in ATP kriging yields a more accurate prediction than a naïve point kriging of areal data that simply collapses each county into its geographic centroid. ATP kriging reduces the smoothing effect and is robust with respect to small differences in the point support semivariogram model. Important features of the point-support semivariogram, such as the nugget effect, can never be fully validated from areal data. The user may want to narrow down the set of solutions based on his knowledge of the phenomenon (e.g., set the nugget effect to zero). The approach presented avoids the visual bias associated with the interpretation of choropleth maps and should facilitate the analysis of relationships between variables measured over different spatial supports.     

Satellite monitoring of spatio-temporal dynamics of China’s coastal zone eco-environments: preliminary analysis on the relationship between the environment,climate change and human behavior

In this paper, temporal dynamics of eco-environmental changes in coastal areas of China during 1981–2000 are investigated based on four key surface parameters including normalized difference vegetation index (NDVI), thermal index, moisture index and surface broadband albedo derived from quantitative remote sensing techniques and meteorological data. Firstly, land surface temperature (LST) and land surface broadband albedo are retrieved by the split-window algorithms and high-order polynomial regression method, respectively, using NOAA/AVHRR series images. Then, moisture index and thermal index, indicators of climate and moisture conditions in the study area, are computed from meteorological data and LST using principal component analysis (PCA). Finally, long-term dynamics of these eco-environmental factors and the reasons responsible for these changes are analyzed further. The results show that during the years from 1981 to 2000, the study area experienced a gradual increase in annual NDVI and climate factors and a decrease in surface annual broadband albedo, which indicates that the coastal thermal and moisture conditions and the subsistence conditions of natural vegetation have changed to a considerable extent. According to the results, a warming and wetting tendency over the last two decades is obvious in the China’s coastal zone that are mainly due to land use changes as of growing urbanization, exhaust emissions from industries and transportations and, partly global climate change. Uncontrolled rapid development of the study area may be blamed for these negative changes as a major driving force. The positive feedback mechanisms between albedo, NDVI and climate factors also partly explain these changes. This study suggests that the method integrating biophysical parameters retrieved from remote sensed images and meteorologic data provides a novel and feasible way to monitor large scale eco-environmental changes.

应用集成的遥感识别技术进行土地利用变化分析

以厦门市为研究区域,以1988-1998年为时间跨度,利用Landsat5TM遥感数据开展土地覆盖变化识别中多种遥感数据处理方法的集成应用研究。以后分类比较法的结果为基础,运用改进的差值法定义的"变化"目标进行修正,将两种方法有机集成综合地确定土地覆盖变化。根据变化前与变化后覆盖不同但土地利用方式相同或类似的原则进行合并处理,最后得到厦门市10年间土地利用结构变化各种成因类型及其数量。结果表明,10年间厦门市因城市化引起的土地覆盖变化为590．83km²,变化强度为31.14％,引起厦门市土地利用结构发生变化主要有 8种成因机制类型,面积达351.99km²,变化强度为18.55％。     

A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques

Normally, to detect surface water changes, water features are extracted individually using multi-temporal satellite data, and then analyzed and compared to detect their changes. This study introduced a new approach for surface water change detection, which is based on integration of pixel level image fusion and image classification techniques. The proposed approach has the advantages of producing a pansharpened multispectral image, simultaneously highlighting the changed areas, as well as providing a high accuracy result. In doing so, various fusion techniques including Modified IHS, High Pass Filter, Gram Schmidt, and Wavelet-PC were investigated to merge the multi-temporal Landsat ETM+ 2000 and TM 2010 images to highlight the changes. The suitability of the resulting fused images for change detection was evaluated using edge detection, visual interpretation, and quantitative analysis methods. Subsequently, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML) classification techniques were applied to extract and map the highlighted changes. Furthermore, the applicability of the proposed approach for surface water change detection was evaluated in comparison with some common change detection methods including image differencing, principal components analysis, and post classification comparison. The results indicate that Lake Urmia lost about one third of its surface area in the period 2000–2010. The results illustrate the effectiveness of the proposed approach, especially Gram Schmidt-ANN and Gram Schmidt-SVM for surface water change detection.     

Detecting the changes in rural communities in Taiwan by applying multiphase segmentation on FORMOSA-2 satellite imagery

Agricultural activities mainly occur in rural areas; recently, ecological conservation and biological diversity are being emphasized in rural communities to promote sustainable development for rural communities, especially for rural communities in Taiwan. Therefore, since 2005, many rural communities in Taiwan have compiled their own development strategies in order to create their own unique characteristics to attract people to visit and stay in rural communities. By implementing these strategies, young people can stay in their own rural communities and the rural communities are rejuvenated. However, some rural communities introduce artificial construction into the community such that the ecological and biological environments are significantly degraded. The strategies need to be efficiently monitored because up to 67 rural communities have proposed rejuvenation projects. In 2015, up to 440 rural communities were estimated to be involved in rural community rejuvenations. How to monitor the changes occurring in those rural communities participating in rural community rejuvenation such that ecological conservation and ecological diversity can be satisfied is an important issue in rural community management. Remote sensing provides an efficient and rapid method to achieve this issue. Segmentation plays a fundamental role in human perception. In this respect, segmentation can be used as the process of transforming the collection of pixels of an image into a group of regions or objects with meaning. This paper proposed an algorithm based on the multiphase approach to segment the normalized difference vegetation index, NDVI, of the rural communities into several sub-regions, and to have the NDVI distribution in each sub-region be homogeneous. Those regions whose values of NDVI are close will be merged into the same class. In doing so, a complex NDVI map can be simplified into two groups: the high and low values of NDVI. The class with low NDVI values corresponds to those regions containing roads, buildings, and other manmade construction works and the class with high values of NDVI indicates that those regions contain vegetation in good health. In order to verify the processed results, the regional boundaries were extracted and laid down on the given images to check whether the extracted boundaries were laid down on buildings, roads, or other artificial constructions. In addition to the proposed approach, another approach called statistical region merging was employed by grouping sets of pixels with homogeneous properties such that those sets are iteratively grown by combining smaller regions or pixels. In doing so, the segmented NDVI map can be generated. By comparing the areas of the merged classes in different years, the changes occurring in the rural communities of Taiwan can be detected. The satellite imagery of FORMOSA-2 with 2-m ground resolution is employed to evaluate the performance of the proposed approach. The satellite imagery of two rural communities (Jhumen and Taomi communities) is chosen to evaluate environmental changes between 2005 and 2010. The change maps of 2005–2010 show that a high density of green on a patch of land is increased by 19.62 ha in Jhumen community and conversely a similar patch of land is significantly decreased by 236.59 ha in Taomi community. Furthermore, the change maps created by another image segmentation method called statistical region merging generate similar processed results to multiphase segmentation.