Integrating remote sensing and plant physiology to assess the effects of mining pollution in a desert ecosystem
Bibliographic record
Abstract
Mining activities in arid environments pose significant risks to vegetation, biodiversity, and ecosystem stability. This study evaluates the long-term ecological effects of phosphate mining pollution on desert vegetation in Israel's Negev Desert by integrating a 36-year Landsat-based NDVI time series (1984–2020) with recent field physiological measurements and controlled greenhouse experiments. The NDVI record revealed contrasting vegetation trajectories between a polluted site and a non-polluted reference site, with a marked decline in NDVI beginning after 2013 at the polluted site. Field analyses of four native species ( Phoenix dactylifera (Palm), Phragmites australis (Common reed), Atriplex halimus (Saltbush), and Sueda vera forssk.ex JFGmel (Sueda vera) demonstrated significant physiological and spectral stress under polluted conditions, with palm and Sueda vera showing the greatest pigment loss. Groundwater from the polluted spring contained elevated concentrations of arsenic (304 μg/L; ∼3× above Israeli Ministry of Health limits), boron (2069 μg/L; ∼2× above Health Canada limits), and nickel (254 μg/L; ∼13× above EPA limits). Greenhouse experiments using maize ( Zea mays ) and basil ( Ocimum basilicum ) confirmed pollution-driven stress responses, including reduced chlorophyll content, decreased stomatal conductance, and increased visible-range reflectance. While the NDVI time series provides long-term evidence of vegetation decline, the field and greenhouse datasets offer mechanistic insight into current physiological stress. Together, these findings demonstrate the cumulative ecological impact of mining-related pollution and highlight the value of integrating Landsat NDVI time series monitoring, physiological measurements, and spectroscopy to support sustainable environmental management in vulnerable arid ecosystems. • NDVI trends show delayed collapse of vegetation in contaminated desert springs. • Groundwater at the polluted site contains high heavy metal and nutrient levels. • Native halophytes exhibit stress markers despite sustained vegetation growth. • Greenhouse tests confirm toxicity of polluted water on maize and basil plants. • Multi-scale approach detects early plant stress from industrial wastewater.
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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 | 0.001 |
| 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 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".