Liu Yang, Yang Kun, Peng Zongqi, Zou Tianle, Su Danni, Sun Run, Ma Jingcong
Faculty of Geography, Yunnan Normal University, Kunming, 650500, China.
The Engineering Research Centre of GIS Technology in Western China, Ministry of Education of China, Yunnan Normal University, Kunming, China.
Sci Rep. 2025 Jul 1;15(1):22114. doi: 10.1038/s41598-025-04765-z.
As the world's second-largest rice exporter, Vietnam's monitoring of land cover changes and carbon stock estimation is crucial for achieving its carbon neutrality goals amidst deforestation and industrial upgrading. This study developed a new land cover classification method based on the phenological characteristics of rice, using the Google Earth Engine (GEE). The method significantly improves the identification accuracy of farmland by extracting rice phenological bands from Sentinel-1 radar data and Sentinel-2 multispectral data. Carbon stock data from 2015 to 2023 were generated using the InVEST model, and their spatial-temporal variations were analyzed. Additionally, the driving factors behind the changes in carbon stocks in forests, grasslands, and croplands were quantitatively explored using the geographic detector(Geo-Detector). The results show that: (1) The classification method for land cover created in this research exhibits greater accuracy than the European Space Agency (ESA) global land cover map and the Japan Aerospace Exploration Agency (JAXA) forest/non-forest maps from Japan, achieving an overall classification accuracy that surpasses 90%. This method also addresses the issue of low identification accuracy of croplands in traditional methods. (2) From 2015 to 2023, Vietnam's LULC changes were mainly characterized by decreases in forests and croplands, and increases in grasslands, construction land, bare land, and water bodies. (3) Overall, natural factors have a greater influence on LULC distribution in Vietnam than human activities, with slope being the most influential factor, followed by altitude, temperature, and population. (4) The main factors affecting the reduction of forest and cropland areas were slope, altitude, and population, while the main factors influencing the changes in construction land area were population and the economy. (5) Vietnam's average carbon stock from 2015 to 2023 was 2.312 billion tons, with an average annual change rate of - 0.63%. Accurate identification of land cover types is a prerequisite for precise carbon stock estimation, and accurate carbon stock estimates are crucial for advancing Vietnam's carbon neutrality goals.
作为世界第二大稻米出口国，在森林砍伐和产业升级的背景下，越南对土地覆盖变化的监测和碳储量估算对于实现其碳中和目标至关重要。本研究利用谷歌地球引擎（GEE），基于水稻的物候特征开发了一种新的土地覆盖分类方法。该方法通过从哨兵-1雷达数据和哨兵-2多光谱数据中提取水稻物候波段，显著提高了农田的识别精度。利用InVEST模型生成了2015年至2023年的碳储量数据，并分析了其时空变化。此外，利用地理探测器（Geo-Detector）定量探究了森林、草地和农田碳储量变化背后的驱动因素。结果表明：（1）本研究创建的土地覆盖分类方法比欧洲航天局（ESA）的全球土地覆盖地图和日本宇宙航空研究开发机构（JAXA）的日本森林/非森林地图具有更高的精度，总体分类精度超过90%。该方法还解决了传统方法中农田识别精度低的问题。（2）2015年至2023年，越南土地利用/土地覆盖变化的主要特征是森林和农田减少，草地、建设用地、裸地和水体增加。（3）总体而言，自然因素对越南土地利用/土地覆盖分布的影响大于人类活动，坡度是最具影响力的因素，其次是海拔、温度和人口。（4）影响森林和农田面积减少的主要因素是坡度、海拔和人口，而影响建设用地面积变化的主要因素是人口和经济。（5）2015年至2023年越南的平均碳储量为23.12亿吨，年均变化率为-0.63%。准确识别土地覆盖类型是精确碳储量估算的前提，而准确的碳储量估算对于推进越南的碳中和目标至关重要。
