{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T07:30:07Z","timestamp":1770276607645,"version":"3.49.0"},"reference-count":71,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CAS Strategic Priority Research Program","award":["XDA19030402"],"award-info":[{"award-number":["XDA19030402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Droughts are ranked among the most devastating agricultural disasters that occur naturally in the world. East Africa is the most vulnerable and drought-prone region worldwide. In this study, four drought indices were used as input variables for drought assessment from 1982 to 2015. This work applied the SMDM algorithm to the integrated approach of OLR and Hurst exponent. The Detrended Fluctuation Analysis (DFA) and Ordinary Least Square (OLR) were merged to compute the trend and persistence (Hurst exponent) of the drought indices. Result indicates that the OLR at time scale 1, 6, and 12 shows a similar distribution with positive (negative) trends scattered in the Northwest (Northeast and Southern) parts of the study area which differs with the OLR aggregated at a 3-month time scale. The percentage pixel distribution for OLR-1, OLR-3, OLR-6, and OLR-12 is 18.2 (81.8), 72.5 (27.5), 32.9 (67.1), and 36.9 (63.1) for increasing (decreasing) trends respectively. Additionally, results indicate that DFA-1 is highly persistent with few random pixels scattered around Ethiopia, South Sudan and Tanzania, with percentage pixels as 88.7, 11.3 and 0.1 representing h &gt; 0.5, h = 0.5, and h &lt; 0.5, respectively. DFA-6 shows high (low) pixels representing h &gt; 0.5 (h &gt; 1), respectively. Meanwhile, for DFA-3 and DFA-12, the distribution shows persistence and a random walk, respectively. Drought conditions may eventually persist, reverse or vary drastically in an unpredictable manner depending on the driving forces. Overall, the drought risk map at 1-, 3-, and 6-month aggregates has shown severe degradation in Southern Kenya and Tanzania while noticeable improvements are seen in western Ethiopia and South Sudan.<\/jats:p>","DOI":"10.3390\/rs13245067","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T22:06:10Z","timestamp":1639519570000},"page":"5067","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Spatial Multi-Criterion Decision Making (SMDM) Drought Assessment and Sustainability over East Africa from 1982 to 2015"],"prefix":"10.3390","volume":"13","author":[{"given":"Wilson","family":"Kalisa","sequence":"first","affiliation":[{"name":"Remote Sensing and Digital Earth Center, School of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing and Digital Earth Center, School of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Tertsea","family":"Igbawua","sequence":"additional","affiliation":[{"name":"Department of Physics, Federal University of Agriculture, Makurdi 970101, Nigeria"}]},{"given":"Alexis","family":"Kayiranga","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Fanan","family":"Ujoh","sequence":"additional","affiliation":[{"name":"Center for Sustainability and Resilient Infrastructure and Communities (SaRIC), School of the Built Environment and Architecture, London South Bank University, London SE1 0AA, UK"}]},{"given":"Igbalumun Solomon","family":"Aondoakaa","sequence":"additional","affiliation":[{"name":"Department of Physics, Federal University of Agriculture, Makurdi 970101, Nigeria"}]},{"given":"Pacifique","family":"Tuyishime","sequence":"additional","affiliation":[{"name":"Computer Science, University of Rwanda, Kigali 4285, Rwanda"}]},{"given":"Shuaishuai","family":"Li","sequence":"additional","affiliation":[{"name":"Remote Sensing and Digital Earth Center, School of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"given":"Claudien Habimana","family":"Simbi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Deborah","family":"Nibagwire","sequence":"additional","affiliation":[{"name":"Department of Mining Engineering, Xi\u2019an University of Science and Technology, No. 58, Yanta Zhong Road, Xi\u2019an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1016\/j.agwat.2010.12.008","article-title":"Economic analysis of drought risk: An application for irrigated agriculture in Spain","volume":"98","author":"Gil","year":"2011","journal-title":"Agric. 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