Digital soil mapping to enhance climate change mitigation and adaptation in the Lower Fraser Valley using remote sensing
Bibliographic record
Abstract
Globally, the agriculture sector is constantly being challenged by multiple climate change-induced stresses while agricultural activities are responsible for a large portion of global greenhouse gas emissions. At the same time, agroecosystems have a sizable potential to mitigate climate change through the sequestration of atmospheric carbon-dioxide as soil organic carbon (SOC); a key soil quality parameter that can also enhance climate change adaptation. Although the dual benefits of SOC are well established, intensive agricultural production and associated land use/land cover (LULC) changes continue to drive large declines in SOC. Alternatively, sustainable LULC practices can potentially reverse this trend and improve SOC stocks. Digital soil mapping (DSM) using remote sensing can help elucidate SOC dynamics associated with LULC change and agricultural management practices by producing spatially explicit information on SOC at the field- and landscape-scales. In this research, I developed and applied innovative DSM techniques to study the spatiotemporal changes in SOC and related soil properties in the Lower Fraser Valley (LFV), one of the most intensive agriculture regions of British Columbia, Canada. At the field-scale, I evaluated various sampling strategies for DSM using unmanned aerial vehicle imagery, mid-infrared spectroscopy and geostatistical models to identify the most cost-effective approach. At the landscape-scale, using Landsat satellite imagery and machine learning tools, I produced maps of soil workability thresholds (WT) for the agricultural lands in Delta and then, assessed the SOC dynamics across the entire LFV since 1984. My analysis identified that 40% of Delta’s agricultural lands had a WT of <30%, making them extremely vulnerable to the shifting precipitation patterns expected for the region. In addition, 61% of LFV lost SOC, 12% of the region gained SOC, while 27% remained unchanged between 1984 and 2018. Areas that lost the most SOC were those that had experienced changes in LULC; however, I concluded the majority of SOC loss occurred due to agricultural practices. The dissertation contributes to devising cost-effective approaches to quantify and monitor changes in SOC at the field- and landscape-scales that can help in the development of effective agricultural climate change mitigation and adaptation strategies.
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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 | 0.000 | 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".