The objective of this study is to estimate the biomass and carbon of global-level mangroves as a special type of wetland. Mangrove ecosystems play an important role in regulating carbon cycling, thus having a significant impact on global environmental change. Extensive studies have been conducted for the estimation of mangrove biomass and carbon stock. However, this estimation at a global level has been insufficiently investigated because the spatial scale of interest is large and most existing studies are based on physically challenging fieldwork surveys that are limited to local scales. Over the past few decades, high-resolution geospatial data related to mangroves have been increasingly collected and processed using remote sensing and Geographic Information Systems (GIS) technologies. While these geospatial data create potential for the estimation of mangrove biomass and carbon, the processing and analysis of these data represent a big data-driven challenge. In this study, we present a spatially explicit approach that integrates GIS-based geospatial analysis and high-performance parallel computing for the estimation of mangrove biomass and carbon at the global level. This integrated approach provides support for enabling and accelerating the global-level estimation of mangrove biomass and carbon from existing high-resolution geospatial data. With this integrated approach, the total area, biomass (including above- and below-ground), and associated carbon stock of global mangroves are estimated as 130,420 km², 1.908 Pg, and 0.725 Pg C for the year of 2000. The averaged aboveground biomass density of global mangroves is estimated as 146.3 Mg ha⁻¹. Our analysis results demonstrate that this integrated geospatial analysis approach is efficacious for the computationally challenging estimation of global mangrove metrics based on high-resolution data. This global-level estimation and associated results are of great assistance for promoting our understanding of complex geospatial dynamics in mangrove forests.

Land use optimization (LUO) first considers which types of land use should exist in a certain area, and secondly, how to allocate these land use types to specific land grid units. As an intelligent global optimization search algorithm, the Genetic Algorithm (GA) has been widely used in this field. However, there are no comprehensive reviews concerning the development process for the application of the Genetic Algorithm in land use optimization (GA-LUO). This article used a bibliometric analysis method to explore current state and development trends for GA-LUO from 1154 relevant documents published over the past 25 years from Web of Science. We also displayed a visualization network from the aspects of core authors, research institutions, and highly cited literature. The results show the following: (1) The countries that published the most articles are the United States and China, and the Chinese Academy of Sciences is the research institution that publishes the most articles. (2) The top 10 cited articles focused on describing how to build GA models for multi-objective LUO. (3) According to the number of keywords that appear for the first time in each time period, we divided the process of GA-LUO into four stages: the presentation and improvement of methods stage (1995–2004), the optimization stage (2005–2008), the hybrid application of multiple models stage (2009–2016), and the introduction of the latest method stage (after 2017). Furthermore, future research trends are mainly manifested in integrating together algorithms with GA and deepening existing research results. This review could help researchers know this research domain well and provide effective solutions for land use problems to ensure the sustainable use of land resources.

Improvements in and the assessment of land use efficiency are crucial pillars for achieving the Sustainable Development Goals (SDGs). This study reviews 208 representative papers, oral reports, and project reports to provide a systematic and comprehensive understanding of the current status and future trends of research on land use efficiency assessment. The findings reveal that (1) the number of papers on land use efficiency assessment is rapidly increasing, with research primarily focused on environmental science and ecology (n = 157, 75.48%). (2) Quantitative models are gaining popularity for land use efficiency assessment, with more than 46.63% of the studies adopting the data envelopment analysis (DEA) model. (3) The definition and analysis perspectives of land use efficiency are diverse, but research on relative land use efficiency and comprehensive analysis perspectives accounts for a significant proportion. (4) Constructing a large and complex model that incorporates geospatial effects, big data, and computer technology is a hot topic for future research methods. On the other hand, conducting land use efficiency research on a global scale is more conducive to achieving the SDGs. (5) The core to improving land use efficiency lies in the joint implementation of multi-pronged measures.

Artificial neural networks (ANNs) have been extensively used for the spatially explicit modeling of complex geographic phenomena. However, because of the complexity of the computational process, there has been an inadequate investigation on the parameter configuration of neural networks. Most studies in the literature from GIScience rely on a trial-and-error approach to select the parameter setting for ANN-driven spatial models. Hyperparameter optimization provides support for selecting the optimal architectures of ANNs. Thus, in this study, we develop an automated hyperparameter selection approach to identify optimal neural networks for spatial modeling. Further, the use of hyperparameter optimization is challenging because hyperparameter space is often large and the associated computational demand is heavy. Therefore, we utilize high-performance computing to accelerate the model selection process. Furthermore, we involve spatial statistics approaches to improve the efficiency of hyperparameter optimization. The spatial model used in our case study is a land price evaluation model in Mecklenburg County, North Carolina, USA. Our results demonstrate that the automated selection approach improves the model-level performance compared with linear regression, and the high-performance computing and spatial statistics approaches are of great help for accelerating and enhancing the selection of optimal neural networks for spatial modeling.

Settlement models help to understand the social–ecological functioning of landscape and associated land use and land cover change. One of the issues of settlement modeling is that models are typically used to explore the relationship between settlement locations and associated influential factors (e.g., slope and aspect). However, few studies in settlement modeling adopted landscape visibility analysis. Landscape visibility provides useful information for understanding human decision-making associated with the establishment of settlements. In the past years, machine learning algorithms have demonstrated their capabilities in improving the performance of the settlement modeling and particularly capturing the nonlinear relationship between settlement locations and their drivers. However, simulation models using machine learning algorithms in settlement modeling are still not well studied. Moreover, overfitting issues and optimization of model parameters are major challenges for most machine learning algorithms. Therefore, in this study, we sought to pursue two research objectives. First, we aimed to evaluate the contribution of viewsheds and landscape visibility to the simulation modeling of - settlement locations. The second objective is to examine the performance of the machine learning algorithm-based simulation models for settlement location studies. Our study region is located in the metropolitan area of Oyo Empire, Nigeria, West Africa, ca. AD 1570–1830, and its pre-Imperial antecedents, ca. AD 1360–1570. We developed an event-driven spatial simulation model enabled by random forest algorithm to represent dynamics in settlement systems in our study region. Experimental results demonstrate that viewsheds and landscape visibility may offer more insights into unveiling the underlying mechanism that drives settlement locations. Random forest algorithm, as a machine learning algorithm, provide solid support for establishing the relationship between settlement occurrences and their drivers.