Impact of urban dynamics and climate change on forest areas the Maamora forest in the city of Kenitra, Morocco
Bibliographic record
Abstract
Studies have focused on the issue of drought on one hand, and urban dynamics on the other, as prominent topics in physical geography for the former, which specializes in climate change, and human geography for the latter, which concerns field sciences. This research is part of a series of studies and specifically relates to a wetland area in the western plain, specifically Maamora Forest in the city of Kenitra. This research addresses three main axes: the first axis relates to human factors contributing to the reduction and deterioration of Maamora Forest over the past three decades and analyzes their impact on the forest. This is done by determining the development and dynamics of cork oak through remote sensing data, manifested in the analysis of aerial images from three different periods (1975, 1995, and 2022), complemented by field research throughout the period between 2022 and 2023. The second axis focuses on studying climatic data for the studied area, extending from 1987 to 2019. It highlights the manifestations of climate change, such as a decrease in annual precipitation and an increase in temperatures, and their impacts on the overall forest and specifically on cork oak trees. This is done using the LANG equation. The results indicate that the region has experienced four dry periods, accounting for 87.5% of the total 28 years, which range from 1987/1988 to 1995/1994, 1997/1998 to 2010/2011, 2012/2013 to 2014/2015, and 2016/2017 to 2018/2019. In contrast, the percentage of semi-humid and extremely dry years only accounted for 6.25% each, with an average duration of two years. The third axis relates to monitoring the effects of climate change on the forestry sector, specifically the Maamora Forest, through the use of modern techniques such as remote sensing and spectral plant and water indicators. It aims to understand the role of these technologies in spatial monitoring of factors and phenomena that negatively impact forest areas in general, and the Maamora Forest in particular.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".