Identifying Ecological Security Patterns Based on Ecosystem Service Supply and Demand Using Remote Sensing Products (Case Study: The Fujian Delta Urban Agglomeration, China)

Abstract

As the global population increases and cities expand, increasing social needs and ecosystem degradation generally coexist, especially in China’s urban agglomerations. Identifying ecological security patterns (ESPs) for urban agglomerations serves as an effective way to sustain regional ecological security and promote harmonious ecological conservation and economic development. Focusing on the Fujian Delta Urban Agglomeration (FDUA) as an example, this study aims to present a framework for linking the supply and demand of ecosystem services (ESs) to identify ESPs in 2020. First, the ecological sources are delimited by coupling the supply and demand of four critical ESs (carbon storage, water provision, grain production, and outdoor recreation). Afterward, the resistance coefficient is modified using nighttime light intensity data and the ecological risk index, the second of which combines the effects of the soil erosion sensitivity index, the geological disaster risk index, and the land desertification risk index. Then, ecological corridors are determined by employing the minimum cumulative resistance method. With the integration of ecological sources and corridors, the ESPs of the FDUA can be identified. The results show a distinct supply–demand mismatch for ESs, with supply exhibiting an upward gradient from coastal cities to inland mountain cities and demand showing the opposite trend. The ESPs consist of 8359 km2 of ecological sources that are predominantly forests, 171 ecological corridors with a total length of 789.04 km, 34 pinch points, 26 barriers, and 48 break points. This paper presents a realizable approach for constructing ESPs for urban agglomerations, which will help decision makers optimize ecological sources and ecological protection policies.

Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China

“…Dead organic carbon is the amount of carbon stored in dead trees. The carbon density data of each land use type were obtained by referring to existing research results [58][59][60][61][62], and the final carbon density database in the study area was constructed (Table 1).…”

“…Dead organic carbon is the amoun carbon stored in dead trees. The carbon density data of each land use type were obtain by referring to existing research results [58][59][60][61][62], and the final carbon density database the study area was constructed (Table 1). Socioeconomic, public administration, climatic, and geographic-environmental factors were taken into account as the drivers of land use change (Table 2), and datasets were quality-controlled through manual review.…”

Spatio-Temporal Evolution of Key Areas of Territorial Ecological Restoration in Resource-Exhausted Cities: A Case Study of Jiawang District, China

“…In the current research, nature reserves, parks, waters, and other elements with high ecological service value are directly selected as ecological source areas, but this only focuses on considering the attributes of the ecological patches themselves [33,34]. Another method is to select high-value ecological areas as ecological source areas by evaluating ecological indicators based on the evaluation of ecological service value and ecological sensitivity [35][36][37][38][39], but this method does not fully consider the integrality and connectivity of patches in the landscape space. Therefore, this study combined the importance of ecosystem services, ecological sensitivity, and landscape stability to conduct a comprehensive evaluation of ecological security [40][41][42].…”

Propuesta para el monitoreo del cultivo de maíz basado en sensores remotos

“…Esta situación generará una demanda adicional de alimentos que solo se puede satisfacer mediante un mayor rendimiento de los cultivos. Sin embargo, esto conlleva a problemas ambientales como la degradación de los suelos, contaminación de aguas superficiales por el uso de fertilizantes, plaguicidas, entre otros (Niu et al 2023). Por otro lado, el maíz es uno de los cultivos alimentarios más valiosos de México y es un componente fundamental de las economías locales, regionales y nacionales.…”

“…La adopción de técnicas de agricultura de precisión mejora la salud del suelo, mantiene la calidad del agua y garantiza la seguridad ambiental, alimentaria y nutricional, así como el desarrollo económico local. De esta forma, la agricultura de precisión aborda varios Objetivos de Desarrollo Sostenible (ODS) (Nhamo et al 2020, Meivel y Maheswari 2021, Rakhymberdina et al 2022, Singh 2022, Upadhyaya et al 2022, Gokool et al 2023, Niu et al 2023.…”