Analysis of Land Surface Temperature and NDVI Using Geo-Spatial Technique: A Case Study of Keti Bunder, Sindh, Pakistan
Bibliographic record
Abstract
Keti Bunder is a small coastal community situated at about 200 km south east of Karachi. It has four major creeks namely Chann, Hajamro, Kangri (Turchhan) and Khober with an arid subtropical climate and temperature remaining moderate throughout the year. This paper reports the application of an integration of Remote Sensing (RS) and Geographic Information Systems (GIS) for analysis and monitoring of the relationship of land surface temperature (LST) with Normalized Difference Vegetation Index (NDVI) in the area. LST is one of the critical elements in the natural phenomena of surface energy and water balance at local and global extent. [1-5]. Remote sensing in accord with tradition utilizes the NDVI to provide specific information on vegetation abundance to the LST–vegetation relationship. For mapping purposes, satellite images of Landsat-5 ETM+, Landsat-7 TM and Landsat-8 OLI / TIRS images, acquired on March 08, 2000, April 29, 2010 and April 08, 2014 respectively, were used. The results indicate that the maximum land surface temperature increased gradually from 39°C in 2000, to 42°C in 2010 and 45°C in 2014. Due to global warming and climatic changes. Keti Bunder of the Indus delta has experienced a serious condition over the past few years; the local communities have suffered badly from climate change impacts as heavy rainfalls, floods and cyclones have forced people to migrate to other places for their livelihood and shelter. However, mean NDVI value increased to -0.009 in 2014 as compared to 2010 (-0.165), due to several plantations of mangroves being established by the government. In the past, the mangrove forest was degraded due to lack of freshwater and seawater intrusion. The rate of degradation of mangrove forest in the delta was approximately 6 percent per year between 1980 and 1995 and only a small percentage of mangroves are now considered to be healthy [6-7].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 source (direct Gemma or distilled Codex), 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".