近实时监测显示,加纳保护区最近出现了广泛的森林扰动

IF 5.2 Q1 ENVIRONMENTAL SCIENCES
Luofan Dong , Xiaojing Tang , Foster Mensah , Bashara Ahmed Abubakari , Kelsee H. Bratley , Pontus Olofsson , Curtis E. Woodcock
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引用次数: 0

摘要

加纳的保护区在保护西非绿化带丰富的生物多样性方面发挥着关键作用。但是,最近政策和法规的变化促进了伐木和采矿活动,从而加速了对森林的干扰。虽然人们一致认为保护区正在经历破坏性的变化,但森林干扰的程度、速度和地点却没有记录。在本研究中,我们采用融合近实时(FNRT)算法,利用Landsat、Sentinel-1和Sentinel-2数据和采样对加纳保护区的森林进行监测。结果表明,2023年和2024年,保护区森林面积减少704.74(±177.24)km2,占保护区森林面积的6%(±1.5%)。此外,估计2024年的森林干扰率比2023年观察到的率高91%(95%置信区间:34% - 172%)。库马西周边保护区存在大面积的森林干扰区,如塔诺奥芬保护区(15.4%±3.9%)、廷特别坡保护区(42%±10.5%)和Oda河保护区(18.5%±4.7%)。如果不采取有效的保护措施,这些保护区的原始森林面临进一步退化或消失的高风险。我们将FNRT结果与其他警报系统(包括RADD和GLAD-L)进行了比较。对比结果表明,多传感器融合能够更及时、准确地检测研究区森林扰动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Near real-time monitoring reveals extensive recent forest disturbance in Ghana's protected areas
The Protected Areas (PAs) in Ghana play a critical role in preserving the abundant biodiversity of the West Africa Green Belt. But recent changes in policies and regulations have facilitated logging and mining activities, which have accelerated forest disturbances. While there is a consensus that PAs are undergoing destructive change, the extent, rate, and locations of forest disturbances are undocumented. In this study, we applied the fusion near real-time (FNRT) algorithm that utilizes Landsat, Sentinel-1, and Sentinel-2 data and sampling to monitor forests in the PAs of Ghana. The results reveal that 704.74 (±177.24) km2 of forest in the PAs were lost in 2023 and 2024, representing 6 % (±1.5 %) of their forest area. Additionally, the forest disturbance rate in 2024 was estimated to be 91 % higher than the rate observed in 2023 (95 % CI: 34 %–172 %). Extensive forest disturbance areas were found in the PAs around Kumasi, such as the Tano Ofin (15.4 % ±3.9 %), Tinte Bepo (42 % ± 10.5 %), and Oda River (18.5 % ± 4.7 %) PAs. Native forests in these PAs are at high risk of further degradation or disappearance in the absence of effective conservation measures. We compared the FNRT results with other alert systems, including RADD and GLAD-L. The comparison demonstrates that multi-sensor fusion provides more timely and accurate detection of forest disturbances in the study area.
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CiteScore
12.20
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