LiDAR-based Ecosystem Classification for Calvert Island
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this work was to define and map a set of repeating ecohydrological classes on Calvert and Hecate Islands using remote sensing data and an unsupervised classification technique. The resulting map provides a new tool for characterizing the extent and internal properties of different ecosystem classes, for stratifying future study designs, and for evaluating the influence of terrestrial landscape characteristics on watershed processes. "Traditionally, forest inventory and ecosystem mapping at local to regional scales rely on manual interpretation of aerial photographs, based on standardized, expert-driven classification schemes. These current approaches provide the information needed for forest ecosystem management but constrain the thematic and spatial resolution of mapping and are infrequently repeated. The goal of this research was to demonstrate the utility of an unsupervised, quantitative technique based on Light Detection And Ranging (LiDAR) data and multi-spectral satellite imagery for mapping local-scale ecosystems over a heterogeneous landscape of forested and non-forested ecosystems. We derived a range of metrics characterizing local terrain and vegetation from LiDAR and RapidEye imagery for Calvert and Hecate Islands, British Columbia. These metrics were used in a cluster analysis to classify and quantitatively characterize ecological units across the island. A total of 18 clusters were derived. The clusters were attributed with quantitative summary statistics from the remotely sensed data inputs and contextualized through comparison to ecological units delineated in a traditional expert-driven mapping method using aerial photographs. The 18 clusters describe ecosystems ranging from open shrublands to dense, productive forest and include a riparian zone and many wetter and wetland ecosystems. The clusters provide detailed, spatially-explicit information for characterizing the landscape as a mosaic of units defined by topography and vegetation structure. This study demonstrates that using various types of remotely sensed data in a quantitative classification can provide scientists and managers with multi- variate information unique from that which results from traditional, expert-based ecosystem mapping methods." - Abstract from Thompson et al. 2016. A complete explanation of methods is available in Thompson et al. 2016. Data-driven regionalization of forested and non-forested ecosystem in coastal British Columbia with LiDAR and RapidEye imagery. The manuscript is available here: <a href="https://www.researchgate.net/publication/296623199_Data-driven_regionalization_of_forested_and_non-forested_ecosystems_in_coastal_British_Columbia_with_LiDAR_and_RapidEye_imagery">Thompson et al. 2016</a> A small number of data voids in the 2012 LiDAR coverage were present and were excluded from the analysis. Although the voids have since been filled with new LiDAR data acquired in 2014, the new data were not included in the analysis of Thompson et al. Other “gaps” in the spatial coverage of the final map are a result of the exclusion of non-vegetated areas (as guided by the Normalized Difference Vegetation Index (NDVI) and the provincial Freshwater Atlas (FWA): http://geobc.gov.bc.ca/base-mapping/atlas/fwa/index.html). In addition to small waterbodies, these non-vegetated areas include a few small areas at high elevation that were snow-covered at the time of the RapidEye image acquisition. DOI: http://dx.doi.org/10.21966/1.135248
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it