Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China

Abstract

Accurately estimating the above-ground biomass (AGB) of spruce forests and analyzing their spatial patterns are critical for quantifying forest carbon stocks and assessing regional climate conditions in China’s drylands, with significant implications for the sustainable management and conservation of forest ecosystems in the Tianshan Mountains. The K-Means clustering algorithm was used to divide 144 measured AGB samples into four AGB classes, combined with remote sensing data from Landsat products, 19 bioclimatic variables, 3 topographical variables, and 3 soil variables to generate probability distributions of four AGB classes using the MaxEnt model. Finally, the spatial distribution of AGB was mapped using the mathematical formulae available in the GIS software. Results indicate that (1) the area under the receiver operating characteristic curve (AUC-ROC) of the AGB models for all classes exceeded 0.8, indicating satisfactory model accuracy; (2) the dominant factors affecting the distribution of different AGB classes varied. The primary dominant factors for the first–fourth AGB classes model were altitude (20.4%), precipitation of warmest quarter (Bio18, 15.7%), annual mean temperature (Bio1, 50.5%), and red band (Band4, 26.7%), respectively, and the response curves indicated that the third AGB model was more tolerant of elevation than the first and second AGB classes; (3) the AGB has a spatial distribution pattern of being higher in the west and low in the east, with a “single-peaked” pattern in terms of latitude, and the average AGB of pixels was 680.92 t·hm−2; (4) the correlation coefficient between measured and predicted AGB is 0.613 (p < 0.05), with the average uncertainty of AGB estimation at 39.32%. This study provides valuable insights into the spatial patterns and drivers of AGB in spruce forests in the Tianshan Mountains, which can inform effective forest management and conservation strategies.

Impacts of Climate Change on the Distribution of Suitable Habitats and Ecological Niche for Trollius Wildflowers in Ili River Valley, Tacheng, Altay Prefecture

“…The value of RM is generally taken in the range [0. 1,4]. In this study, the occurrence data for each species were divided into training and test sets using the kuenm_occsplit.R package, and the kuenm_calibration R package was employed to construct candidate model ensembles in conjunction with the environmental variables.…”

“…ITAP (Ili River Valley, Tacheng, Altay Prefecture) is situated in northwestern Xinjiang, with a total area of 325,117.01 km 2 and the highest biodiversity in Xinjiang [4], making it an ideal location for studying ecosystem heterogeneity in buffering or accelerating the impacts of GCC owing to its climatic sensitivity. ITAP is also the main producing area for Trollius wildflowers, and is comprised of three parts.…”

Global Warming Drives Expansion of Endangered Spruce Forest on the Tibetan Plateau

“…var. Complanata (P. complanata), and Picea aurantiaca (P. aurantiaca), as endangered spruce on the Tibetan Plateau, play an irreplaceable role in the construction of local subalpine coniferous forests and the improvement of the living environment for wildlife habitat [32][33][34]. They also play an indispensable role in maintaining the ecological safety of the Asian water tower and the upper of the Yangtze River [35].…”

Automatic Extraction of the Spatial Distribution of Picea schrenkiana in the Tianshan Mountains Based on Google Earth Engine and the Jeffries–Matusita Distance

“…At present, the research on Picea schrenkiana mainly includes nitrogen addition [31], individual genomics [32], stoichiometric characteristics [33], population dynamics [34], and biomass estimation [35,36]. However, the spatial distribution pattern of Picea schrenkiana in the TS is not clear, which has become the bottleneck for the scientific management and utilization of the plant resources.…”