查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting out of Guangzhou, People's Re public of China, by NewsRx editors, research stated, "The Heritiera littoralis, an important rare semi-mangrove species found in coastal protective forests, pla ys a crucial role in carbon storage and cycling within mangrove wetland ecosyste ms." Our news correspondents obtained a quote from the research from Guangdong Academ y of Forestry: "Despite its importance, previous studies have overlooked remote monitoring of its carbon storage. Taking the world's oldest and largest natural community of H. littoralis in Shenzhen, China, as the study area, this research pioneers the use of UAV hyperspectral imaging to identify the H. littoralis comm unity and estimate its above-ground, below-ground, and total carbon storage. The impacts of five geo-environmental factors (elevation, slope, aspect, vegetation communities, and inland distance from the coastline) on the spatial variability of carbon storage using SHapley Additive exPlanations (SHAP) and multiscale geo graphically weighted regression (MGWR) methods were also investigated. The resul ts demonstrate that the first derivative bands within the red edge and near-infr ared regions, the anthocyanin reflectance index 2 (ARI2) and newly-developed thr ee-band VIs, were sensitive features for community identification and carbon sto rage estimation within the H. littoralis community. Among the four machine learn ing models (eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vec tor Machine (SVM), and Logistic Regression (LR)), the best identification accura cy for the H. littoralis community was achieved by XGBoost. Among the four machi ne learning models (XGBoost, RF, SVM and kernel ridge regression (KRR)), RF mode l achieved the best performance in estimating above-ground (R2 = 0.749, RMSE=1.7 23 kg/m2, EV (explained variance) = 0.704), below-ground (R2 = 0.636, RMSE=0.6 k g/m2, EV=0.606) and total carbon storage (R2 = 0.613, RMSE=2.592 kg/m2, EV=0.597 )."
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