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On May 18, 2023 at 10:01:03 AM UTC, kennedysenagi:
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Added resource Clean data to Desert Locust (Schistocerca gregaria) Invasion Risk and Vegetation Damage in a Key Upsurge Area
f | 1 | { | f | 1 | { |
2 | "acknowledgement": "The authors gratefully acknowledge the financial | 2 | "acknowledgement": "The authors gratefully acknowledge the financial | ||
3 | support for this research by the following organizations and agencies: | 3 | support for this research by the following organizations and agencies: | ||
4 | the Swedish International Development Cooperation Agency (Sida); the | 4 | the Swedish International Development Cooperation Agency (Sida); the | ||
5 | Swiss Agency for Development and Cooperation (SDC); the Australian | 5 | Swiss Agency for Development and Cooperation (SDC); the Australian | ||
6 | Centre for International Agricultural Research (ACIAR); the Federal | 6 | Centre for International Agricultural Research (ACIAR); the Federal | ||
7 | Democratic Republic of Ethiopia; and the Government of the Republic of | 7 | Democratic Republic of Ethiopia; and the Government of the Republic of | ||
8 | Kenya. The views expressed herein do not necessarily reflect the | 8 | Kenya. The views expressed herein do not necessarily reflect the | ||
9 | official opinion of the donors.\u00a0\u00a0", | 9 | official opinion of the donors.\u00a0\u00a0", | ||
10 | "administrative_areas": "Turkana county", | 10 | "administrative_areas": "Turkana county", | ||
11 | "author": null, | 11 | "author": null, | ||
12 | "author_email": null, | 12 | "author_email": null, | ||
13 | "citation_narrative": "Mongare, R.; Abdel-Rahman, E.M.; Mudereri, | 13 | "citation_narrative": "Mongare, R.; Abdel-Rahman, E.M.; Mudereri, | ||
14 | B.T.; Kimathi, E.; Onywere, S.; Tonnang, H.E.Z. Desert Locust | 14 | B.T.; Kimathi, E.; Onywere, S.; Tonnang, H.E.Z. Desert Locust | ||
15 | (Schistocerca gregaria) Invasion Risk and Vegetation Damage in a Key | 15 | (Schistocerca gregaria) Invasion Risk and Vegetation Damage in a Key | ||
16 | Upsurge Area. Earth 2023, 4, x. https://doi.org/10.3390/xxxxx", | 16 | Upsurge Area. Earth 2023, 4, x. https://doi.org/10.3390/xxxxx", | ||
17 | "collaborators": "[{\"collaborator\": \"Raphael Mongare\"}, | 17 | "collaborators": "[{\"collaborator\": \"Raphael Mongare\"}, | ||
18 | {\"collaborator\": \" Elfatih Abdel-Rahman\"}, {\"collaborator\": | 18 | {\"collaborator\": \" Elfatih Abdel-Rahman\"}, {\"collaborator\": | ||
19 | \"Bester Tawona Mudereri\"}, {\"collaborator\": \"Emily Kimathi\"}, | 19 | \"Bester Tawona Mudereri\"}, {\"collaborator\": \"Emily Kimathi\"}, | ||
20 | {\"collaborator\": \"Simon Onywere\"}, {\"collaborator\": \"Henri E. | 20 | {\"collaborator\": \"Simon Onywere\"}, {\"collaborator\": \"Henri E. | ||
21 | Z. Tonnang\"}]", | 21 | Z. Tonnang\"}]", | ||
22 | "contact_person": "Elfatih Abdel-Rahman", | 22 | "contact_person": "Elfatih Abdel-Rahman", | ||
23 | "contact_person_email": "eabdel-rahman@icipe.org", | 23 | "contact_person_email": "eabdel-rahman@icipe.org", | ||
24 | "country": "[{\"country\": \"KE\"}]", | 24 | "country": "[{\"country\": \"KE\"}]", | ||
25 | "creator_user_id": "f09ec764-fe3c-4069-850b-f968ff0c20bb", | 25 | "creator_user_id": "f09ec764-fe3c-4069-850b-f968ff0c20bb", | ||
26 | "date_uploaded": "2023-05-18", | 26 | "date_uploaded": "2023-05-18", | ||
27 | "donor": "icipe core funds", | 27 | "donor": "icipe core funds", | ||
28 | "end_date": "2021-08-31", | 28 | "end_date": "2021-08-31", | ||
29 | "groups": [], | 29 | "groups": [], | ||
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32 | "license_id": "cc-nc", | 32 | "license_id": "cc-nc", | ||
33 | "license_title": "Creative Commons Non-Commercial (Any)", | 33 | "license_title": "Creative Commons Non-Commercial (Any)", | ||
34 | "license_url": "http://creativecommons.org/licenses/by-nc/2.0/", | 34 | "license_url": "http://creativecommons.org/licenses/by-nc/2.0/", | ||
35 | "maintainer": "Kennedy Senagi", | 35 | "maintainer": "Kennedy Senagi", | ||
36 | "maintainer_email": "ksenagi@icipe.org", | 36 | "maintainer_email": "ksenagi@icipe.org", | ||
37 | "metadata_created": "2023-05-18T07:51:22.064105", | 37 | "metadata_created": "2023-05-18T07:51:22.064105", | ||
n | 38 | "metadata_modified": "2023-05-18T10:00:34.971568", | n | 38 | "metadata_modified": "2023-05-18T10:01:03.125221", |
39 | "name": | 39 | "name": | ||
40 | d-future-prediction-of-land-use-land-cover-dynamics-using-ca-and-ann", | 40 | d-future-prediction-of-land-use-land-cover-dynamics-using-ca-and-ann", | ||
41 | "notes": "In the recent past, the Horn of Africa witnessed an | 41 | "notes": "In the recent past, the Horn of Africa witnessed an | ||
42 | upsurge in the desert locust (Schistocerca gregaria) invasion. This | 42 | upsurge in the desert locust (Schistocerca gregaria) invasion. This | ||
43 | has raised major concerns over the massive food insecurity, | 43 | has raised major concerns over the massive food insecurity, | ||
44 | socioeconomic impacts, and livelihood losses caused by these recurring | 44 | socioeconomic impacts, and livelihood losses caused by these recurring | ||
45 | invasions. This study determined the potential vegetation damage due | 45 | invasions. This study determined the potential vegetation damage due | ||
46 | to desert locusts (DLs) and predicted the suitable habitat at high | 46 | to desert locusts (DLs) and predicted the suitable habitat at high | ||
47 | risk of invasion by the DLs using current and future climate change | 47 | risk of invasion by the DLs using current and future climate change | ||
48 | scenarios in Kenya. The normalized difference vegetation index (NDVI) | 48 | scenarios in Kenya. The normalized difference vegetation index (NDVI) | ||
49 | for the period 2018\u20132020 was computed using multi-date Sentinel-2 | 49 | for the period 2018\u20132020 was computed using multi-date Sentinel-2 | ||
50 | imagery in the Google Earth Engine platform. This was performed to | 50 | imagery in the Google Earth Engine platform. This was performed to | ||
51 | assess the vegetation changes that occurred between May and July of | 51 | assess the vegetation changes that occurred between May and July of | ||
52 | the year 2020 when northern Kenya was the hotspot of the DL upsurge. | 52 | the year 2020 when northern Kenya was the hotspot of the DL upsurge. | ||
53 | The maximum entropy (MaxEnt) algorithm was used together with 646 DL | 53 | The maximum entropy (MaxEnt) algorithm was used together with 646 DL | ||
54 | occurrence records and six bioclimatic variables to predict DL habitat | 54 | occurrence records and six bioclimatic variables to predict DL habitat | ||
55 | suitability. The current (2020) and two future climatic scenarios for | 55 | suitability. The current (2020) and two future climatic scenarios for | ||
56 | the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5 from the model | 56 | the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5 from the model | ||
57 | for interdisciplinary research on climate (MIROC6) were utilized to | 57 | for interdisciplinary research on climate (MIROC6) were utilized to | ||
58 | predict the future potential distribution of DLs for the year 2030 | 58 | predict the future potential distribution of DLs for the year 2030 | ||
59 | (average for 2021\u20132040). Using Turkana County as a case, the NDVI | 59 | (average for 2021\u20132040). Using Turkana County as a case, the NDVI | ||
60 | analysis indicated the highest vegetation damage between May and July | 60 | analysis indicated the highest vegetation damage between May and July | ||
61 | 2020. The MaxEnt model produced an area under the curve (AUC) value of | 61 | 2020. The MaxEnt model produced an area under the curve (AUC) value of | ||
62 | 0.87 and a true skill statistic (TSS) of 0.61, while temperature | 62 | 0.87 and a true skill statistic (TSS) of 0.61, while temperature | ||
63 | seasonality (Bio4), mean diurnal range (Bio2), and precipitation of | 63 | seasonality (Bio4), mean diurnal range (Bio2), and precipitation of | ||
64 | the warmest quarter (Bio18) were the most important bioclimatic | 64 | the warmest quarter (Bio18) were the most important bioclimatic | ||
65 | variables in predicting the DL invasion suitability. Further analysis | 65 | variables in predicting the DL invasion suitability. Further analysis | ||
66 | demonstrated that currently 27% of the total area in Turkana County is | 66 | demonstrated that currently 27% of the total area in Turkana County is | ||
67 | highly suitable for DL invasion, and the habitat coverage is predicted | 67 | highly suitable for DL invasion, and the habitat coverage is predicted | ||
68 | to potentially decrease to 20% in the future using the worst-case | 68 | to potentially decrease to 20% in the future using the worst-case | ||
69 | climate change scenario (SSP5-8.5). These results have demonstrated | 69 | climate change scenario (SSP5-8.5). These results have demonstrated | ||
70 | the potential of remotely sensed data to pinpoint the magnitude and | 70 | the potential of remotely sensed data to pinpoint the magnitude and | ||
71 | location of vegetation damage caused by the DLs and the potential | 71 | location of vegetation damage caused by the DLs and the potential | ||
72 | future risk of invasion in the region due to the available favorable | 72 | future risk of invasion in the region due to the available favorable | ||
73 | vegetational and climatic conditions. This study provides a scalable | 73 | vegetational and climatic conditions. This study provides a scalable | ||
74 | approach as well as baseline information useful for surveillance, | 74 | approach as well as baseline information useful for surveillance, | ||
75 | development of control programs, and monitoring of DL invasions at | 75 | development of control programs, and monitoring of DL invasions at | ||
76 | local and regional scales.", | 76 | local and regional scales.", | ||
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152 | "start_date": "2021-01-01", | 178 | "start_date": "2021-01-01", | ||
153 | "state": "draft", | 179 | "state": "draft", | ||
154 | "tags": [ | 180 | "tags": [ | ||
155 | { | 181 | { | ||
156 | "display_name": "Food security", | 182 | "display_name": "Food security", | ||
157 | "id": "4c9ace73-7e9a-4220-bd8b-aa2a05e317fa", | 183 | "id": "4c9ace73-7e9a-4220-bd8b-aa2a05e317fa", | ||
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189 | }, | 215 | }, | ||
190 | { | 216 | { | ||
191 | "display_name": "species distribution model", | 217 | "display_name": "species distribution model", | ||
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193 | "name": "species distribution model", | 219 | "name": "species distribution model", | ||
194 | "state": "active", | 220 | "state": "active", | ||
195 | "vocabulary_id": null | 221 | "vocabulary_id": null | ||
196 | }, | 222 | }, | ||
197 | { | 223 | { | ||
198 | "display_name": "vegetation index", | 224 | "display_name": "vegetation index", | ||
199 | "id": "d1d49155-af99-46af-8d2d-534c477aa513", | 225 | "id": "d1d49155-af99-46af-8d2d-534c477aa513", | ||
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203 | } | 229 | } | ||
204 | ], | 230 | ], | ||
205 | "third_party": "yes", | 231 | "third_party": "yes", | ||
206 | "title": "Desert Locust (Schistocerca gregaria) Invasion Risk and | 232 | "title": "Desert Locust (Schistocerca gregaria) Invasion Risk and | ||
207 | Vegetation Damage in a Key Upsurge Area", | 233 | Vegetation Damage in a Key Upsurge Area", | ||
208 | "type": "dataset", | 234 | "type": "dataset", | ||
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211 | } | 237 | } |