ArticleInternational journal of environmental research and public health2022
Intensity Characteristics and Multi-Scenario Projection of Land Use and Land Cover Change in Hengyang, China.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Land use change and ecological sensitivity in the Qingdao West Coast new area: A 30-year analysis and future scenario simulation.PloS one · 2026Article
- Evaluation of the synergistic change in cultivated land and wetland in northeast China from 1990 to 2035.Scientific reports · 2025Article
- Spatiotemporal changes and driving factors of alpine land cover in Tianshan world natural heritage sites.Scientific reports · 2024Article
- Application of a Multi-Layer Perceptron and Markov Chain Analysis-Based Hybrid Approach for Predicting and Monitoring LULCC Patterns Using Random Forest Classification in Jhelum District, Punjab, Pakistan.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intensity Analysis has generally been applied as a top-bottom hierarchical accounting method to understand regional dynamic characteristics of land use and land cover (LULC) change. Given the inconvenience of transition level in the detailed and overall presentation of various category transitions at multiple intervals, a novel transition pattern is proposed to represent the transition's size and intensity and to intuitively identify the stationary mode of transition, which helps the transition level to connect to the mode with the process. Intensity Analysis was conducted to communicate the transition between LULC categories in Hengyang from 1980 to 2015. The patch-generating land use simulation (PLUS) model was employed for multi-scenario projection from 2015 to 2045. From 1980 to 2015, 2005 was a significant turning point in the speed of LULC change in Hengyang, and the change rate after this time point was three times that before the time point. The gain of built-up and bare, and the loss of cultivated was always active. The reason for the large loss of forest is that forest comprises the largest proportion of Hengyang. The loss of cultivated and the loss of forest contributing to the built-up's gain is much larger, but the mechanism behind the transition differed. A stationary targeting transition mode from cultivated to built-up in Hengyang was detected. The PLUS model confirmed that the area of forest, cultivated and grass will reduce, and the rate of decrease will slow down in the future, while water areas will slightly increase. Our work enriches the methodology of Intensity Analysis and provides a scientific reference for the sustainable development and management of land resources in Hengyang.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.