Imperfection of Geographic Information: Concepts and Terminologies

Devillers, Rodolphe; Desjardin, Éric; Runz, Cyril de

doi:10.1002/9781119507284.ch2

Cited by 4 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a crucial quantitative indicator of forest ecosystem structure and function, forest biomass is an important parameter in the construction of carbon stock estimation models, as there is a ratio of 0.5 between forest biomass and carbon concentration [9]. In the modeling of carbon stock estimation models, various factors contribute to the inaccuracy of the Forests 2023, 14, 953 2 of 18 model compared to the measured data, also referred to as uncertainty [10]. In addition, the estimation of biomass is one of the significant sources of uncertainty in carbon stock estimation models [11].…”

Section: Introductionmentioning

confidence: 99%

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

Xue

Wang

2023

Forests

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%