卫星数据显示内战期间提格雷农民在作物种植方面的恢复力

IF 5.7 Q1 ENVIRONMENTAL SCIENCES Science of Remote Sensing Pub Date : 2024-06-01 DOI:10.1016/j.srs.2024.100140
Hannah R. Kerner , Catherine Nakalembe , Benjamin Yeh , Ivan Zvonkov , Sergii Skakun , Inbal Becker-Reshef , Amy McNally
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引用次数: 0

摘要

提格雷战争是 2020 年 11 月 3 日至 2022 年 11 月 2 日期间主要发生在埃塞俄比亚北部提格雷地区的一场武装冲突。鉴于提格雷地区的农业对生计和粮食安全的重要性,确定战争对耕地面积的影响至关重要。然而,由于在该地区内和进入该地区的行动受到限制,以及冲突导致的不安全和阻塞,很难量化这种影响。利用卫星图像和统计面积估算技术,我们评估了战争前和战争期间提格雷地区作物种植面积的变化。我们的研究结果表明,尽管战争造成了广泛影响,但 2020 年至 2021 年期间的耕地面积基本保持稳定。我们估计战前 2020 年的种植面积为 1,132,000 ± 133,000 公顷,而战时 2021 年的种植面积为 1,217,000 ± 132,000 公顷。比较冲突事件周围 5 公里缓冲区内外的变化,我们发现缓冲区内耕地损失的置信上限(0-3%)略高于缓冲区外(0-1%)。我们的研究结果支持了其他报告的观点,即尽管战争造成了广泛的破坏,但提格雷农民在很大程度上仍能维持耕作。我们的研究表明,遥感技术与机器学习和统计技术相结合,能够提供及时、透明的面积估算,用于监测因冲突而无法进入的地区的粮食安全状况。
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Satellite data shows resilience of Tigrayan farmers in crop cultivation during civil war

The Tigray War was an armed conflict that took place primarily in the Tigray region of northern Ethiopia from November 3, 2020 to November 2, 2022. Given the importance of agriculture in Tigray to livelihoods and food security, determining the impact of the war on cultivated area is critical. However, quantifying this impact was difficult due to restricted movement within and into the region and conflict-driven insecurity and blockages. Using satellite imagery and statistical area estimation techniques, we assessed changes in crop cultivation area in Tigray before and during the war. Our findings show that cultivated area was largely stable between 2020 and 2021 despite the widespread impacts of the war. We estimated 1, 132, 000 ± 133, 000 ha of cultivation in pre-war 2020 compared to 1, 217, 000 ± 132, 000 ha in wartime 2021. Comparing changes inside and outside of a 5 km buffer around conflict events, we found a slightly higher upper confidence limit of cropland loss within the buffer (0–3%) compared to outside the buffer (0–1%). Our results support other reports that despite widespread war-related disruptions, Tigrayan farmers were largely able to sustain cultivation. Our study demonstrates the capability of remote sensing combined with machine learning and statistical techniques to provide timely, transparent area estimates for monitoring food security in regions inaccessible due to conflict.

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