The Natural Resource Management Planning Portal: Perspectives for NRM Planning and Reporting

Natural Resource Management (NRM) is often conducted as a partnership between government and citizens. In Australia, government agencies formulate policy and fund implementation that may be delivered on-ground by community groups (such as Landcare). Since the late 1980s, over AUS$8b of Commonwealth investment has been made in NRM. However, quantitative evidence of environmental improvements is lacking. The NRM Planning Portal has been developed to (1) provide an online spatial information system for sharing Landcare and agency data; and (2) to facilitate NRM priority setting at local and regional planning scales. While the project successfully federates Landcare NRM activity data, challenges included (1) unstructured, non-standardized data, meaning that quantitative reporting against strategic objectives is not currently possible, and (2) a lack of common understanding about the value proposition for adopting the portal approach. Demonstrating the benefit of technology adoption is a key lesson for digital NRM planning.     

Water quality data for Fall Creek,New York,USA: 1972–1995

The 33 086 ha mixed land use Fall Creek watershed in upstate New York is part of the Great Lakes drainage system. Results from more than 3500 water samples are available in a data set that compiles flow data and measurements of various water quality analytes collected between 1972 and 1995 in all seasons and under all flow regimes in Fall Creek and its tributaries. Data is freely accessible at https://ecommons.cornell.edu/handle/1813/8148 and includes measurements of suspended solids, pH, alkalinity, calcium, magnesium, potassium, sodium, chloride, nitrate nitrogen (NO3-N), sulphate sulphur (SO4-S), phosphorus (P) fractions molybdate reactive P (MRP) and total dissolved P (TDP), percent P in sediment, and ammonium nitrogen (NH4-N). Methods, sub-watershed areas, and coordinates for sampling sites are also included. The work represented in this data set has made important scientific contributions to understanding of hydrological and biogeochemical processes that influence loading in mixed use watersheds and that have an impact on algal productivity in receiving water bodies. In addition, the work has been foundational for important regulatory and management decisions in the region.     

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.     

Wetland hydroperiod classification in the western prairies using multitemporal synthetic aperture radar

Wetlands represent one of the world's most biodiverse and threatened ecosystem types and were diminished globally by about two‐thirds in the 20th century. There is continuing decline in wetland quantity and function due to infilling and other human activities. In addition, with climate change, warmer temperatures and changes in precipitation and evapotranspiration are reducing wetland surface and groundwater supplies, further altering wetland hydrology and vegetation. There is a need to automate inventory and monitoring of wetlands, and as a study system, we investigated the Shepard Slough wetlands complex, which includes numerous wetlands in urban, suburban, and agricultural zones in the prairie pothole region of southern Alberta, Canada. Here, wetlands are generally confined to depressions in the undulating terrain, challenging wetlands inventory and monitoring. This study applied threshold and frequency analysis routines for high‐resolution, single‐polarization (HH) RADARSAT‐2, synthetic aperture radar mapping. This enabled a growing season surface water extent hyroperiod‐based wetland classification, which can support water and wetland resource monitoring. This 3‐year study demonstrated synthetic aperture radar‐derived multitemporal open‐water masks provided an effective index of wetland permanence class, with overall accuracies of 89% to 95% compared with optical validation data, and RMSE between 0.2 and 0.7 m between model and field validation data. This allowed for characterizing the distribution and dynamics of 4 marsh wetlands hydroperiod classes, temporary, seasonal, semipermanent, and permanent, and mapping of the sequential vegetation bands that included emergent, obligate wetland, facultative wetland, and upland plant communities. Hydroperiod variation and surface water extent were found to be influenced by short‐term rainfall events in both wet and dry years. Seasonal hydroperiods in wetlands were particularly variable if there was a decrease in the temporary or semipermanent hydroperiod classes. In years with extreme rain events, the temporary wetlands especially increased relative to longer lasting wetlands (84% in 2015 with significant rainfall events, compared with 42% otherwise).     

基于灯光数据的京津冀城市多标度异速分析

城镇体系的时空演化是一个复杂的动力学过程,具有无尺度性,其特征可以采用异速标度指数定量描述。论文以京津冀为例,基于1992—2013年夜间灯光数据的校正结果,提取城镇夜间灯光信息,采用多标度异速分析方法,分析京津冀城镇体系的相对发展特征及其空间分异格局。研究发现：①京津冀大城市的增长空间局限日益显著。虽然大城市如京津唐的绝对发展水平高,但接近环境承载量的极限,22 a间增长快速的是大城市交接地带的小城市(如三河、迁安、廊坊等)。②相对增长速度快的城市从东北到西南呈现带状分布——政府选择发展的“雄安新区”就在这个轴带上。由此可以得出两个结论：其一,京津冀城市演化的趋势相对均衡化,空间结构异质性逐步减弱,整个城镇体系的发展趋于相对平衡状态;其二,大、小城市“两极”贯通或许是增强城镇体系功能的有效途径。京津冀的空间格局优化不能仅仅考虑主要的、规模大的中心城市,而应该基于等级结构协调整个城镇网络体系。     

自然资源一体化政务服务系统及数据融合建设——以南京为例

自然资源部制定的信息化建设总体方案中，要求构建“互联网+自然资源政务服务”应用体系，实现“服务事项标准统一、整体联动、业务协同，自然资源政务服务和共享开放能力全面提高”。一体化政务服务系统作为政务服务应用体系的重点建设内容，也是落实自然资源管理“两统一”职责的重要支撑，如何整合各类数据资源、发挥系统作用，成为亟待研究的问题。本文在分析自然资源业务管理需求后，提出了一体化政务服务系统及数据融合的建设思路。     

Lidar-aided analysis of boreal forest backscatter at Ku band

This paper analyzes the backscatter of the microwave signal in a boreal forest environment based on a Ku -band airborne Frequency-Modulated Continuous Waveform (FMCW) profiling radar—Tomoradar. We selected a half-managed boreal forest in the southern part of Finland for a field test. By decomposing the waveform collected by the Tomoradar, the vertical canopy structure was achieved. Based on the amplitude of the waveform, the Backscattered Energy Ratio of Canopy-to-Total (BERCT) was calculated. Meanwhile, the canopy fraction was derived from the corresponding point cloud recorded by a Velodyne VLP-16 LiDAR mounted on the same platform. Lidar-derived canopy fraction was obtained by counting the number of the first/ the strongest returns versus the total amount of returns. Qualitative and quantitative analysis of radar-derived BERCT on lidar-derived canopy fraction and canopy height are investigated. A fitted model is derived to describe the Ku-band microwave backscatter in the boreal forest to numerically analyze the proportion contributed by four factors: lidar-derived canopy fraction, radar-derived canopy height, the radar-derived distance between trees and radar sensor and other factors, from co-polarization Tomoradar measurements. The Root Mean Squared Error (RMSE) of the proposed model was 0.0958, and the coefficient of determination R2 was 0.912. The fitted model reveals that the correlation coefficient between radar-derived BERCT and lidar-derived canopy fraction is 0.84, which illustrates that lidar surface reflection explains the majority of the profiling /waveform radar response. Thus, vertical canopy structure derived from lidar can be used for the benefit of radar analysis.     

基于云存储技术的海岛信息管理和 展示平台设计与实现

随着海岛遥感影像数据的不断积累,传统数据存储管理方法越来越难以满足需求。针对上述问题,利用云存储技术对海岛影像数据进行分块组织,建立分布式海岛影像数据集,并在优化数据存储的同时,设计了一套海岛信息多形式展示平台。实现了信息检索、数据传输与审核、分析评价、成果展示和共享等功能。     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.