ALBERT

Description: The establishment and application of a spectral library is a critical step in the standardization and automation of remote sensing interpretation and mapping. Currently, most spectral libraries are designed to support the classification of land cover types, whereas few are dedicated to agricultural remote sensing monitoring. Here, we gathered spectral observation data on plants in multiple experimental scenarios into a spectral database to investigate methods for crop classification (16 crop species) and status monitoring (tea plant and rice growth). We proposed a set of screening methods for spectral features related to plant classification and status monitoring (band reflectance, vegetation index, spectral differentiation, spectral continuum characteristics) that are based on ISODATA and JM distance. Next, we investigated the performance of different machine learning classifiers in the spectral library application, including K-nearest neighbor (KNN), Random Forest (RF), and a genetic algorithm coupled with a support vector machine (GA-SVM). The optimal combination of spectral features and the classifier with the highest classification accuracy were selected for crop classification and status monitoring scenarios. The GA-SVM classifier performed the best, which produced an accuracy of OAA = 0.94, Kappa = 0.93 for crop classification in a complex scenario (crops mixed with 71 non-crop plant species), and promising accuracies for tea plant growth monitoring (OAA = 0.98, Kappa = 0.97) and rice growth stage monitoring (OAA = 0.92, Kappa = 0.90). Therefore, the establishment of a plant spectral library combined with relevant feature extraction and a classification algorithm effectively supports agricultural monitoring by remote sensing.

Description: In this study, an initial water-rights allocation (IWRA) model is proposed for adjusting the traditional initial water-rights empowerment model based on previous water intake permits, with the aim of improving the productivity of water resources under population growth and economic development. A stochastic scenario with Laplace criterion mixed fuzzy programming (SSLF) is developed into an IWRA model to deal with multiple uncertainties and complexities, which includes dynamic water demand, changing water policy, adjusted tradable water rights, the precise risk attitude of policymakers, development of the economy, and their interactions. SSLF not only deals with fuzziness in probability distributions with high satisfaction degrees, but also reflects the risk attitudes of policymakers with the Laplace criterion, which can handle the probability of scenario occurrence under the supposition of no data available. The developed IWRA model with the SSLF method is applied to a practical case in an alpine region of China. The results of adjusted initial water rights, optimal water-right allocation, changed industrial structure, and system benefits under various scenarios associated with risk attitudes and water productivity improvement were obtained and analyzed. It was found that the current initial water-rights allocation scheme based on previous intake water permits is not efficient, and this can be modified by the IWRA model. Based on the strategies of drinking safety and ecological security, the main tradeoff between agricultural and industrial water rights can facilitate optimization of the current initial water-rights allocation. This can assist policymakers in producing an effective plan to promote water productivity and water resource management in a robust and reliable manner.

Description: The hybrid energy system (HES) has attracted more and more attention since it can not only achieve multi-energy supply but realize cascade utilization of energy resources. However, the performances of the HES in relation to economic, environmental, social, and technological aspects are rarely studied. Therefore, this paper tries to fill this research gap to evaluate the sustainability performance of an HES. First, an evaluation criteria system is established based on a literature review. After that, the group analytic hierarchy process (GAHP) technique is used to obtain the importance weights of these criteria. Later, the sustainability performance of the HES is calculated through an improved fuzzy synthetic evaluation (FSE) approach based on a cloud model. The applicability of this approach is demonstrated by a real case study in Zhejiang province, China. Finally, the sensitivity analysis results reveal that the overall consequence is that the performance of an HES is robust when the criteria weight is floating within a certain range (−30–30%), and the comparative analysis with the traditional FSE also reveals that the proposed approach is superior.

Description: This study investigates the spatial dependence of house prices in the Yangtze Delta Urban Agglomeration since the year 2000. According to Moran’s I index and the LISA scatter plot derived from a cross-section data set, the spatial dependence of house prices can be traced across the 25 cities in the agglomeration and became more evident after 2005. This study develops a spatial panel model with geographical distance and economic distance weight matrices. Spatial effects significantly influenced house prices in both cases but the intensity of the former was weaker than for the latter. Income, proportion of the tertiary industry, and amenity exhibited significant indirect effects on house prices in other cities in the inner region of the agglomeration, while competition of population between cities with economic proximity exerted negative indirect effects. Furthermore, urban industrial structure, innovation capability, and urbanization degree revealed differences in terms of spatial dependence among various city groups.

Description: Since the energy crisis in the 1960s, crucial research and activities were spurred to improve energy efficiency and decrease environmental pollution. To deal with the various problems the construction industry are facing, the concept of green buildings (GBs) has been gradually shaped and put forward all over the world, and green building rating systems (GBRSs) have been developed. The concept of GBs covers a wide range of elements, and its definition is constantly updated as the construction industry develops. This paper compares the development of backgrounds and statuses of green building development in various countries. It also presents an overview of the green building development situation within these countries, summarizing two influences for GB development: one external and the other internal. External factors include GB development policy support, economic benefits, and certification schemes. Internal factors are the development and application of GB technology, the level of building management, and how users interact with the GB technology. Currently, 49 worldwide green building standards and application have been sorted out, including 18 standard expert appraisal systems. Moreover, it discusses the research results and lessons learned from green building projects in different countries and summarizes their achievements and challenges. To correctly understand and use green building technology, it is essential to improve the policy and incentive system, improve the professional quality and technical ability of employees and accredited consultants, constantly develop and update the evaluation system, strengthen technological innovation, and integrate design and management. This paper aims to draw a clear roadmap for national standard development, policy formulation, and construction design companies, provide solutions to remove the obstacles, and suggest research direction for future studies.

Description: With the accelerated urbanization process, cities are suffering from extremely heavy rain and urban storm water logging disasters in recent years. To provide reliable and effective information for urban management and emergency decision-making, the accuracy of basic data must be guaranteed in any urban rainwater model. This paper presents a novel MKFCM-MRF (Multiple Kernel Fuzzy C Means-Markov Random Field) model to segment high-resolution Unmanned Aerial Vehicle (UAV) images. The core ideas of MKFCM-MRF model are as follows. Firstly, in order to increase the correlation information between pixels, multiple-kernel functions are introduced into Fuzzy C Means (FCM) clustering algorithm, which automatically filters out the optimal weight combination among kernel functions according to the distribution characteristics of pixels in feature space. Secondly, in order to better segment the texture and edge of the image, the clustering results of multiple-kernel FCM clustering algorithm are introduced into Markov Random Field (MRF) model, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw clustering results are regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of MKFCM-MRF is performed by high-resolution UAV images data. The experimental results indicate MKFCM-MRF can refine the classification map in homogeneous areas, while reducing most of the edge blurring artifact, and improving the classification accuracy compared with FCM clustering algorithm. In addition, the validation of the urban storm flood model shows that the trend of the two clustering results is the same, but the runoff producing time and the peak time of FCM clustering results are advanced, the peak flow and the total runoff are larger; the simulation results corresponding to MKFCM-MRF clustering results are more realistic.