Substantial rehabilitation of mangrove forests along the Indus Delta coastline of Pakistan: A 33-year review
Bibliographic record
Abstract
Mangrove forests are essential to coastal ecosystems and support biodiversity, carbon sequestration, and storm protection. This study utilizes 33 years of Landsat-5 and Landsat-8 time-series data (1990–2023) along with recent advancements in machine learning, like Object-Based Image Analysis (OBIA) and random forest classification, to investigate the spatial and temporal dynamics of mangrove forests in Pakistan’s Indus Delta. The results reveal that mangrove forests increased considerably from 50,973 ha in 1990 to 99,407 ha in 2015, and then to 101,446 ha in 2023. Spatial metrics indices were used to describe the mangrove patches’ main properties, and Frag-stats were used to calculate the landscape metrics analysis. Additionally, the results suggested that the best set of key metrics to quantify sustainable development index (SDI) comprises the following ten metrics: total area (TA), number of patches (NP), patch density (PD), landscape shape index (LSI), aggregation index (AI), landscape pattern index (LPI), mean Euclidean nearest neighbor distance (ENN_MN), cohesion index (COHESION), mean patch size (AREA_MN), and Divisions. This proves that fragmentation TA increased, PD, NP, LPI, LSI, and ENN_MN were reduced, patch size and density decreased significantly, and connectivity was higher. It also indicates improved ecological stability and the presence of enhanced mangrove forests in the Indus Delta. The COHESION, Divisions, and AI were also constant, with only minor change. We hope this study inspires further climate change adaptation planning and supports the prioritization and implementation of conservation measures for the mangrove ecosystems of the Indus Delta. These results strongly support the need for long-term mangrove monitoring and show the efficacy of conservation and restoration actions at multidecadal timescales.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".