Assessment of Spatial-Temporal Changes of Landscape Ecological Risk in Xishuangbanna, China from 1990 to 2019

Human activities are constantly affecting ecological space, and the construction of ecological security patterns to ensure ecological security has become an issue that must be considered for sustainable development. At present, little attention has been paid to the ecological security of cities with a high number of nature reserves. In this study, we took Garze Tibetan Autonomous Prefecture in China, which has many nature reserves, as the research object to construct an ecological security pattern with nature reserves as ecological sources. Firstly, Fragstats 4.2 and ArcGIS 10.4 were used to obtain the ecological risk evaluation results of the study area landscape. Secondly, a “nature–society–landscape” resistance factor system and an ecological resistance surface were constructed using a minimum resistance model. Finally, the ecological safety zone of the nature reserve was divided, and the ecological safety pattern of the nature reserve was established. (1) The ecological risk of the study area shows a spatial distribution pattern of “low in the northwest and high in the southeast”, with low and moderate-low ecological risk dominating; (2) The study area has formed an ecological security pattern consisting of 9 ecological sources, 35 ecological nodes, 8 ecological corridors with a total length of 702.96 km and 4 ecological safety zones; (3) The ecological security pattern of nature reserves in the study area was divided into four categories: low, medium, high and moderate-high ecological safety zones, accounting for 20.62%, 27.34%, 24.48% and 27.55%, respectively. This study provides a new framework for the construction of urban ecological safety patterns and offers scientific guidance for the conservation and management of nature reserves and urban ecology.

Section: Discussionmentioning

confidence: 99%

Construction of Nature Reserves’ Ecological Security Pattern Based on Landscape Ecological Risk Assessment: A Case Study of Garze Tibetan Autonomous Prefecture, China

Liu

Yang

et al. 2023

“…In this study, on the USGS platform (https://earthexplorer.usgs.gov/, accessed on 20 May 2023), six Landsat images with a low cloud cover and lush vegetation were selected in the study area from June to August. This study was conducted using Landsat-TM/ETM satellite imagery from 1995 to 2010 and Landsat 8 OLI satellite imagery from 2015 and 2020 [44]. To calculate the corresponding quantitative remote sensing index based on the reflectance of the relevant bands of the satellite image, it was necessary to use ENVI5.6 software to perform data preprocessing such as radiometric calibration, atmospheric cor-4 of 20 rection, geometric correction, and image enhancement on the Landsat image to reduce the influence of the atmosphere on surface reflectance [45].…”

Section: Data Sourcesmentioning

confidence: 99%

Landscape Analysis and Ecological Risk Assessment during 1995–2020 Based on Land Utilization/Land Coverage (LULC) and Random Forest: A Case Study of the Fushun Open-Pit Coal Area in Liaoning, China

Xu,

Cheng

2024

China’s Fushun open-pit mine is the largest century-old coal mine in Asia. Large-scale mining and the use of coal has caused dramatic changes in the regional urban landscape pattern, seriously affecting the ecological function and ecosystem stability of its surrounding landscape. Evaluating the ecological risks of the landscape in the urban areas of open-pit mines contributes to the risk management of regional ecosystems and the sustainable development of society. This study selected six-phase Landsat ETM/OLI remote sensing images from 1995 to 2020 and combined them with the random forest model to carry out an LULC classification of the open-pit mine and its surrounding areas and, on this basis, discusses the evolution of its landscape pattern and evaluates the ecological risks. It fills the gap in the research on the evolution of regional landscape patterns and ecological risks in the study area and improves the automatic classification efficiency of LULC for use in open-pit mines. The results show that the classification accuracy of LULC regarding open-pit mines based on image pixels and the random forest model can reach 30 m, and the rate of accuracy can reach 92–97%. From 1995 to 2020, the coverage area of forest land and building land in the study area has increased and is mainly composed of grassland and undeveloped land. The use of land was transferred to the mining area, the water body area maintained a relative dynamic balance, and the overall vegetation coverage of the mining area was greatly improved; the forest land began to expand from the surrounding area to the mining area in 2010, and the construction land began to move from the areas surrounding the mining area to the surrounding valleys in 2015. The landforms have extended radially, and the landscape sprawl index has increased, indicating the optimization of the ecological environment; the high- and medium-risk areas decreased by 75.51 km2, the low-risk areas expanded by 461.48 km2, and the overall ecological risk index decreased. From this, it is possible to conclude that the landscape restoration project adopted in the study area has achieved great results, and the improvement of the ecological environment also directly affects the increase of construction land. These research results can provide scientific guidance for the rational utilization and sustainable development of land resources in urban areas of open-pit mines.

“…This growth is the fruit of population demand for new infrastructures (housing, factories, roads, etc.) and this issue is replicated worldwide [2][3][4][5]. Essentially, this reflects the increase in needed resources.…”

Section: Introduction 1frameworkmentioning

confidence: 96%

Measuring the Performance of a Strategic Asset Management Plan through a Balanced Scorecard

de-Almeida-e-Pais,

Raposo,

Farinha

et al. 2023

The purpose of this paper is to propose a tool to measure the performance of a Strategic Asset Management Plan (SAMP) based on a Balanced Scorecard (BSC). The SAMP converts organizational objectives into asset management objectives, as well as specifies the role of the asset management system, providing support to achieve asset management objectives. The SAMP becomes the heart of the organization and integrates the long-term, medium-term, and short-term plans. In the SAMP, the balance among performance, costs, and risks are taken into consideration in order to achieve the organization’s objectives. On the other hand, the SAMP is a guide to set the asset management objectives while describing the role of the Asset Management System (AMS) in meeting these objectives. Since the SAMP is the central figure of AMS, it is important to measure its performance and should be built and improved through an iterative process. This indicates that it is not just a document, it is “the document” that should be treated as a “living being”, which needs to adapt to internal and external changes quickly. The BSC is an excellent tool where, through the appropriate Key Performance Indicators (KPIs), the progress can be measured, and is supported by four perspectives: Financial, Customer, Internal Business Process, and Learning and Growth.