Estimating Grassland Biomass - Potentials and Limitations of Point Cloud Analysis

As buildings have almost come to a saturation point in most developed countries, the management and maintenance of existing buildings have become the major problem of the field. Building Information Modeling (BIM) is the underlying technology to solve this problem. It is a 3D semantic representation of building construction and facilities that contributes to not only the design phase but also the construction and maintenance phases, such as life-cycle management and building energy performance measurement. This study aims at the processes of creating as-built BIM models, which are constructed after the design phase. Point cloud, a set of points in 3D space, is an intermediate product of as-built BIM models that is often acquired by 3D laser scanning and photogrammetry. A raw point cloud typically requires further procedures, e.g. registration, segmentation, classification, etc. In terms of segmentation and classification, machine learning methodologies are trending due to the enhanced speed of computation. However, supervised machine learning methodologies require labelling the training point clouds in advance, which is time-consuming and often leads to inevitable errors. And due to the complexity and uncertainty of real-world environments, the attributes of one point vary from the attributes of others. These situations make it difficult to analyze how one single attribute contributes to the result of segmentation and classification. This study developed a method of producing point clouds from a fast-generating 3D virtual indoor environment using procedural modeling. This research focused on two attributes of simulated point clouds, point density and the level of random errors. According to Silverman (1986), point density is associated with the point features around each output raster cell. The number of points within a neighborhood divided the area of the neighborhood is the point density. However, in this study, there was a little different. The point density was defined as the number of points on a surface divided by the surface area. And the unit is points per square meters (pts/m2). This research compared the performances of a machine learning segmentation and classification algorithm on ten different point cloud datasets. The mean loss and accuracy of segmentation and classification were analyzed and evaluated to show how the point density and level of random errors affect the performance of the segmentation and classification models. Moreover, the real-world point cloud data were used as additional data to evaluate the applicability of produced models.

Using Fast Point Feature Histograms feature from point cloud for 3D object recognition or registration, Fast Point Feature Histograms feature descriptor is arbitrarily and inefficiently calculated by subjectively adjusting the neighborhood radius, and the whole process can’t be completed automatically. An adaptive neighborhood-selection Fast Point Feature Histograms point cloud feature extraction algorithm has proposed to solve this problem. Firstly, we estimate the point cloud density of many pairs of point clouds. Secondly, compute the neighborhood radius to extract the Fast Point Feature Histograms features for Sample Consensus Initial Alignment registration, and count the radius and the density when the registration performance is optimal, and then the cubic spline interpolation fitting is used to obtain the function expression of the radius and the density. Finally, the Fast Point Feature Histograms feature extraction algorithm has combined with the function to form adaptive neighborhood-selection Fast Point Feature Histograms feature extraction algorithm. The experimental results have shown that the proposed algorithm can adaptively choose the appropriate neighborhood radius according to the density of point cloud, and improve the Fast Point Feature Histograms feature matching performance. At the same time, it is improved the computing speed to a better value, which is of guiding significance.

The vast majority of European grasslands strongly depend on the regular removal of aboveground biomass by agricultural land use, mostly grazing or mowing or a combination of both. These specific management schemes have strong influence on plant diversity and vegetation composition, depending on their particular characteristics and their intensity. For example, the presence or absence of fertilization will favour some species over others, changing plant communities accordingly. Additionally, the farmer's choice of a specific management scheme will also depend on the abiotic site conditions. This leads to a complex set of associated factors potentially affecting the structure and diversity of grasslands. In this study, we compiled a unique dataset of 169 differently managed grasslands (in total 202 plots), which were sampled in five regions across Germany. For each plot, we documented management characteristics, measured plant diversity and functional group composition, recorded endangered species according to red lists, and calculated Ellenberg indicator values. We assessed patterns in vegetation composition and diversity in relation to the particular management scheme, which was categorized as meadow, meadow with autumn or winter grazing (with mowing as predominant management), mown pasture (where mowing and grazing are used at roughly equal intensity), seasonal pasture (with grazing as predominant management) and year-round pasture. Our study showed that grasslands of different management schemes significantly differed in diversity, structure and functional composition. However, it also became obvious that vegetation composition was not strictly distinguished by management alone. Local and regional characteristics such as soil conditions, size of the grassland species pool or land-use history, often played a more prominent role than land use alone. Assumingly, the interplay of those local and regional characteristics with the pro portion of grazing and mowing at a particular site inhibit clear differences among our predefined management schemes. Nevertheless, species richness was the lowest in year-round pastures, moderate in meadows and highest in seasonal pastures. In contrast, year-round pastures harboured the highest mean numbers of endangered species. The dependency of a certain management scheme on site-specific environmental factors such as soil fertility, further complicated the clear separation of management effects from those of the environmental background. In summary, modern grassland management strongly shaped grassland vegetation, but today's combination of different management practices complicated the assessment of specific land-use effects on plant diversity. Thus, neither mowing nor grazing turned out to be ``the one and only'' management for nature conservation. Although our results challenge long-term prognoses for future vegetation development under modern grassland management, we clearly showed that low-intensity management and the absence of fertilization promoted plant diversity, with higher values in pastures compared to meadows and mown pastures.

• Impacts of land use intensity (LUI) on the diversity of soil communities depend on land use type. • Different agricultural practices drive soil communities composition in arable vs grassland soils. • Understudied taxa like protists are a key element of soil network responses to LUI. • Land use type, rather than LUI, influences the complexity of the soil metacommunity network. While there is an emerging body of research showing the consequences of land use intensity on soil biodiversity, most studies focus on biodiversity responses to a single or a limited number of agricultural practices in controlled settings or at a single field site, neglecting that multiple practices are simultaneously applied by farmers in real agroecosystems. The combined effects of various agricultural practices have, until now, been largely overlooked in agroecosystems. Here, we conducted a field soil sampling campaign on 87 farms with two land use types (39 arable fields and 48 grasslands) to investigate the relationship between land use intensity, determined by various agricultural practices, and multiple soil communities (bacteria, fungi, protists and invertebrates) at regional scales. We found that land use intensity influenced the diversity and community composition of various soil taxa differently, and these impacts strongly depended on land use type. Soil fungi were most susceptible to land use intensity in both arable fields and grasslands. Specifically, irrigation and pest control were the main practices shaping soil communities in arable fields, while phosphorus and nitrogen fertilization were the main practices structuring soil communities in grasslands. Furthermore, an increase in land use intensity led to greater soil network complexity in arable fields. Our findings reveal regional on-farm patterns of land use intensity effects on various soil communities and identified key agricultural practices that structure soil communities. A key strength of this study is that these patterns can be generalized because the samples were collected from 87 farmlands where multiple agricultural practices were implemented simultaneously. Overall, this work provides a comprehensive perspective on the different responses of multiple soil communities and their associations to land use intensity in agroecosystems.

To simplify the point cloud data while preserving features, one novel algorithm based on the curvature criterion is put forward. The whole point cloud is divided into a series of initial sub-clusters with the 3D grid subdivision method, and then k neighborhood is constructed from the partition results. All the points in k neighborhood are approximated by quadratic parametric surface based on scattered point cloud parameterization. The curvatures of fitting surface are further calculated. The judgment of requiring reduction is decided by the novel minimal surface distance of curvature features. Some typical cases with various surface features, such as surf, stone, pottery figurine, tooth, are chosen to verify the new method. The results indicate that the new algorithm is of significance in theory and practice for reduction of point cloud, and enables to reduce data directly and efficiently while maintaining the geometry of the original model. The reliability and accuracy of the algorithm are also proved by experimentation.

Methods for LiDAR-based estimation of extensive grassland biomass

The land use of wholesales, which handle large quantities of goods and require large space for storage and distribution, is less intensive than those of retails, finances, etc. Rising land values, severe competition for space and traffic difficulty have induced many wholesale establishments to relocate outward from Zones I and II to Zones III and IV (Fig. 4).The purpose of this article is to examine land use conversions on sites vacated by the relocation of wholesale establishments to the Wholesale Estate on the east of Sendai. Such relocations have two types: complete one, in which the previous site is completely vacated, and partial one, in which only selected functions remain.(I) Intensification of land use by complete relocationIn Zone I, open spaces created by the complete relocation of wholesale establishmants have mostly been converted to more intensive land uses (i. e. retails, services, restaurants, rental office buildings, hotels, hospitals, etc.), and rarely to less intensive land uses (i. e. residences, printings, constructions, parkings and other wholesales). The occupying ratio of intensive land uses to the whole declines precipitously from Zone I to Zone IV (0.77, 0.39, 0.23, 0.15). The intensity of land use by zone varies even within a particular intensive land use category. For example, retail establishments located in Zone I sell “shopping goods”, but those in Zones II, III and IV sell “consumer's goods”.(II) Functional differentiation by partial relocationThe partial relocation of the wholesale establishments has brought about spatial differentiation of functions. Some of the wholesale establishements remained subsidiary functions, such as processing, constructing, or retailing. Functions with extensive land use have been relocated to the Wholesale Estate, and those with intensive land use such as retail facilities have remained in place. Such changes resulted in the upgrading and specialization of land uses in the site, not only in Zones I and II but also in Zone III.In conclusion, most of sites formerly occupied by wholesale establishments have been converted to more intensive land uses, in the central zone (Zones I and II). This intensification has been especially stronger in Zone I, where banks, rental office buildings, hotels, etc. have been newly constructed on the vacated sites. But, the relocation of wholesale establishments has not effected greater upon the refining of functional structure of inner urban areas.

Three-dimensional (3D) mapping deals with the problem of building a map of the unknown environments explored by a mobile robot. In contrast to 2D maps, 3D maps contain richer information of the visited places. Besides enabling robot navigation in 3D, a 3D map of the robot surroundings could be of great importance for higher-level robotic tasks, like scene interpretation and object interaction or manipulation, as well as for visualization purposes in general, which are required in surveillance, urban search and rescue, surveying, and others. Hence, the goal of this thesis is to develop a system which is capable of reconstructing the surrounding environment of a mobile robot as a three-dimensional map. Microsoft Kinect camera is a novel sensing sensor that captures dense depth images along with RGB images at high frame rate. Recently, it has dominated the stage of 3D robotic sensing, as it is low-cost, low-power. For this work, it is used as the exteroceptive sensor and obtains 3D point clouds of the surrounding environment. Meanwhile, the wheel odometry of the robot is used to initialize the search for correspondences between different observations. As a single 3D point cloud generated by the Microsoft Kinect sensor is composed of many tens of thousands of data points, it is necessary to compress the raw data to process them efficiently. The method chosen in this work is to use a feature-based representation which simplifies the 3D mapping procedure. The chosen features are planar surfaces and orthogonal corners, which is based on the fact that indoor environments are designed such that walls, ground floors, pillars, and other major parts of the building structures can be modeled as planar surface patches, which are parallel or perpendicular to each other. While orthogonal corners are presented as higher features which are more distinguishable in indoor environment. In this thesis, the main idea is to obtain spatial constraints between pairwise frames by building correspondences between the extracted vertical plane features and corner features. A plane matching algorithm is presented that maximizes the similarity metric between a pair of planes within a search space to determine correspondences between planes. The corner matching result is based on the plane matching results. The estimated spatial constraints form the edges of a pose graph, referred to as graph-based SLAM front-end. In order to build a map, however, a robot must be able to recognize places that it has previously visited. Limitations in sensor processing problem, coupled with environmental ambiguity, make this difficult. In this thesis, we describe a loop closure detection algorithm by compressing point clouds into viewpoint feature histograms, inspired by their strong recognition ability. The estimated roto-translation between detected loop frames is added to the graph representing this newly discovered constraint. Due to the estimation errors, the estimated edges form a non-globally consistent trajectory. With the aid of a linear pose graph optimizing algorithm, the most likely configuration of the robot poses can be estimated given the edges of the graph, referred to as SLAM back-end. Finally, the 3D map is retrieved by attaching each acquired point cloud to the corresponding pose estimate. The approach is validated through different experiments with a mobile robot in an indoor environment

A comprehensive understanding of emerging city land utilization and the evaluation of intensive land use in the Emerging City will provide the comprehensive and reliable technical basis for the planning and management. It is an important node. According to the Han cheng from 2008 to 2016 years of land use, based on PSR-Entropy model of land use evaluation system, using entropy method to determine the index weight, the introduction of comprehensive index method to evaluate the degree of land use. The results show that the intensive land use comprehensive evaluation index of Han cheng increased from 2008 to 2015, but the land intensive use can not achieve the standards. The potential of further enhancing space is relatively large.

A modelling approach for the assessment of the effects of Common Agricultural Policy measures on farmland biodiversity in the EU27

Construction land expansion often occurs on cultivated land in developing countries during rapid urbanization and industrialization. Understanding its characteristics and driving mechanisms is of great significance for land-use policy and sustainable development. This paper depicted the spatio-temporal patterns of China’s urban–rural expansion and its occupation of cultivated land based on national land survey data from 1996 to 2006. It further explored the influencing mechanism of cultivated land occupation for urban–rural construction land. The results showed that the establishment and expansion of various economic development zones contribute to more than half of the occupation of cultivated land while the expansion of cities and towns is relatively slower, and their sources of construction land are more diverse. The empirical results showed that (1) economic growth and investment play key roles in shaping the spatio-temporal patterns of the occupation of cultivated land for urban–rural construction land, and (2) the mechanisms of cultivated land occupation in different stages of regional development are different. In particular, the establishment of national economic development zones is conducive to the intensive use of construction land and the protection of cultivated land in inland regions, whereas provincial economic development zones have led to a waste of land resources in coastal regions. Based on the results, this study suggested that the policies to the intensive use of land resources and cultivated land protection required regional disparities.

On the basis of IV theory and computing tools discussed in previous chapters, this chapter studies intensive land use evaluation on subordinate districts/counties. China's capital, Beijing, is selected as the target of this case study. The basic functional model of IVs is used in accordance with the dynamic changes of resource conditions, while indicators are selected including GDP, land area, resource condition suitability index, total fixed asset investment, population, technological advance, and land Gini coefficient. Calculation results show Beijing has already been in the intensive land use stage, but is in an excessive use stage spatially. The IV model is proved to be applicable on intensive urban land use evaluation, and IVs can accurately reflect the status of urban land use.

The impact of land use- and climate change on the managed eco-geomorphic balance in the Alps

This paper establishes an evaluation system based on the low-carbon intensive land use in Jinan city from 2010 to 2017 and uses a multi-attribute approach named grey fuzzy integral to build the evaluation model. In this model, based on the Mobius transformation coefficient of subjective and objective weights of index factors and the interaction degree between index factors, 2-additive fuzzy measures can be obtained; therefore, evaluation of low-carbon and intensive land use in Jinan city is processed by combining the grey correlation degree and Choquet fuzzy integral. The results show that in the study period, land input intensity, land use degree, land output benefit and land sustainability in Jinan city all show a good upward trend, but the low-carbon land use level of has been in a declining state. Although there is a good development trend of low-carbon and intensive land use in Jinan, the state is not stable. A Low-carbon and intensive land use pattern will not be achieved completely overnight, and it is bound to be a dynamic game process.

Deterioration of groundwater quality along an increasing intensive land use pattern in a small catchment