The Development and Application of a GIS-Based Tool to Assess Forest Landscape Restoration Effects on Water Conservation Capacity
Bibliographic record
Abstract
In forest landscape restoration, one of the key objectives is to improve the water conservation capacity of the deforested land. A rapid, accurate assessment of the effects of the restoration measures on the water conservation capacity of targeted forests can help forest managers to identify the best practices for forest restoration. However, the traditional assessment tools of forest water conservation function lack a description of forest growth, and are featured by complex computation, which fails to evaluate the effects of forest restoration on the regional forest water conservation capacity in an efficient way. To address this issue, through combining the forest restoration evaluation model (equivalent recovery area, ERA), classic forest water storage capacity estimation (total water storage capacity), this study has taken advantage of ENVI/IDL, ArcGIS Engine/C#.Net to develop the Forest and Water Assessment Tool (FWAT) for assessing the changes of the regional forest landscape and the associated forest water conservation capacity in various forest restoration scenarios. This tool has been successfully applied in the Upper Zagunao watershed, a large forested watershed in the Upper Yangtze River basin. According to the assessment, the forest water conservation capacity of the study watershed consistently increased from about 1580.76 t/hm2 in 2010 to a projected 2014.34 t/hm2 by natural restoration, and 2124.18 t/hm2 by artificial restoration by 2030. The artificial restoration measures yield a better effect on forest water conservation function than natural restoration. By 2030, the forest water conservation capacity of artificial restoration scenario is expected to be about 7% higher than that of natural restoration scenario. The FWAT as an efficient tool to assess the effects of forest restoration measures on regional forest water conservation capacity can provide scientific support for the design of forest restoration and management strategies worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".