
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Ноя. 11, 2024
Язык: Английский
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Ноя. 11, 2024
Язык: Английский
Ecological Informatics, Год журнала: 2024, Номер 82, С. 102732 - 102732
Опубликована: Июль 22, 2024
Accurately estimating aboveground biomass (AGB) in forest ecosystems facilitates efficient resource management, carbon accounting, and conservation efforts. This study examines the relationship between predictors from Landsat-9 remote sensing data several topographical features. While provides reliable crucial for long-term monitoring, it is part of a broader suite available technologies. We employ machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), alongside linear regression techniques like Multiple Linear (MLR). The primary objectives this encompass two key aspects. Firstly, research methodically selects optimal predictor combinations four distinct variable groups: (L1) data, fusion Vegetation-based indices (L2), integration with Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) (L3) combination best (L4) derived L1, L2, L3. Secondly, systematically assesses effectiveness different to identify most precise method establishing any potential field-measured AGB variables. Our revealed that (RF) model was utilizing OLI SRTM DEM predictors, achieving remarkable accuracy. conclusion reached by assessing its outstanding performance when compared an independent validation dataset. RF exhibited accuracy, presenting relative mean absolute error (RMAE), root square (RRMSE), R2 values 14.33%, 22.23%, 0.81, respectively. XGBoost subsequent choice RMAE, RRMSE, 15.54%, 23.85%, 0.77, further highlights significance specific spectral bands, notably B4 B5 Landsat 9 capturing spatial distribution patterns. Integration vegetation-based indices, including TNDVI, NDVI, RVI, GNDVI, refines mapping precision. Elevation, slope, Topographic Wetness Index (TWI) are proxies representing biophysical biological mechanisms impacting AGB. Through utilization openly accessible fine-resolution employing algorithm, demonstrated promising outcomes identification predictor-algorithm mapping. comprehensive approach offers valuable avenue informed decision-making assessment, ecological monitoring initiatives.
Язык: Английский
Процитировано
52Ecological Informatics, Год журнала: 2025, Номер unknown, С. 102997 - 102997
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
3Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103045 - 103045
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
2The Science of The Total Environment, Год журнала: 2025, Номер 966, С. 178763 - 178763
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
2Ecological Informatics, Год журнала: 2024, Номер 82, С. 102706 - 102706
Опубликована: Июль 4, 2024
Accurate canopy cover estimation is essential for mature and early-stage young forests, as it guides forest management silvicultural activities necessary their growth regeneration. However, obtaining precise measurements of in the field time-consuming challenging, especially at regional landscape levels. Remote sensing techniques offer a promising alternative to traditional field-based estimating cover. In this study, our objective estimate using vegetation indices derived from multispectral bands PlanetScope (Planet Lab, Inc., San Francisco, CA, USA). To best knowledge, first study utilise imagery data boreal forests. Based on analysis four (green, blue, red, near-infrared) imagery, 43 indices, including spectral 13 salinity were computed select predictors modelling. Six regression models employed model cover: linear, elastic net, support vector machine, random forest, extreme gradient boosting, light boosting machine. All demonstrated good performance both training dataset (R2 = 0.58–0.69) testing 0.59–0.64, RMSE 0.16–0.18, rRMSE 22%–23%, MAE 0.12–0.14). fit statistics datasets paired t-test, identified machine most suitable predicting For R2 value was 0.69 (training), data, 0.64, 0.16, 22%, 0.12. Therefore, we recommend that future researchers Planet higher spatial resolution. exploring additional learning algorithms explicitly methods when computing satellite remote strongly advised.
Язык: Английский
Процитировано
8Ecological Informatics, Год журнала: 2024, Номер 82, С. 102768 - 102768
Опубликована: Авг. 10, 2024
Fractional Vegetation Cover (FVC) serves as a crucial indicator in ecological sustainability and climate change monitoring. While machine learning is the primary method for FVC inversion, there are still certain shortcomings feature selection, hyperparameter tuning, underlying surface heterogeneity, explainability. Addressing these challenges, this study leveraged extensive field data from Qinghai-Tibet Plateau. Initially, selection algorithm combining genetic algorithms XGBoost was proposed. This integrated with Optuna tuning method, forming GA-OP combination to optimize learning. Furthermore, comparative analyses of various models inversion alpine grassland were conducted, followed by an investigation into impact heterogeneity on performance using NDVI Coefficient Variation (NDVI-CV). Lastly, SHAP (Shapley Additive exPlanations) employed both global local interpretations optimal model. The results indicated that: (1) exhibited favorable terms computational cost accuracy, demonstrating significant potential tuning. (2) Stacking model achieved among seven (R2 = 0.867, RMSE 0.12, RPD 2.552, BIAS −0.0005, VAR 0.014), ranking follows: > CatBoost LightGBM RFR KNN SVR. (3) NDVI-CV enhanced result reliability excluding highly heterogeneous regions that tended be either overestimated or underestimated. (4) revealed decision-making processes perspectives. allowed deeper exploration causality between features targets. developed high-precision scheme, successfully achieving accurate proposed approach provides valuable references other parameter inversions.
Язык: Английский
Процитировано
8Ecological Informatics, Год журнала: 2024, Номер 84, С. 102887 - 102887
Опубликована: Ноя. 9, 2024
Язык: Английский
Процитировано
8Earth Science Informatics, Год журнала: 2025, Номер 18(3)
Опубликована: Фев. 19, 2025
Язык: Английский
Процитировано
1Ecological Informatics, Год журнала: 2024, Номер 82, С. 102730 - 102730
Опубликована: Июль 20, 2024
Язык: Английский
Процитировано
6Trees, Год журнала: 2025, Номер 39(2)
Опубликована: Март 19, 2025
Язык: Английский
Процитировано
0