干旱区生态环境知识资源中心(Arid): Application of normalized difference vegetation index (NDVI) to forecast rodent population abundance in smallholder agro-ecosystems in semi-arid areas in Tanzania

Arid

DOI	10.1515/mammalia-2018-0175
	Application of normalized difference vegetation index (NDVI) to forecast rodent population abundance in smallholder agro-ecosystems in semi-arid areas in Tanzania
	Chidodo, Davis J.2; Kimaro, Didas N.2; Hieronimo, Proches 2; Makundi, Rhodes H.1; Isabirye, Moses 3; Leirs, Herwig 4; Massawe, Apia W.1; Mdangi, Mashaka E.5; Kifumba, David 3; Mulungu, Loth S.1
通讯作者	Mulungu, Loth S.
来源期刊	MAMMALIA
ISSN	0025-1461
EISSN	1864-1547
出版年	2020
卷号	84 期号:2 页码:136-143
英文摘要	This study aimed to evaluate the potential use of normalized difference vegetation index (NDVI) from satellite-derived remote sensing data for monitoring rodent abundance in semi-arid areas of Tanzania. We hypothesized that NDVI could potentially complement rainfall in predicting rodent abundance spatially and temporally. NDVI were determined across habitats with different vegetation types in Isimani landscape, Iringa Region, in the southern highlands of Tanzania. Normalized differences in reflectance between the red (R) (0.636-0.673 mm) and near-infrared (NIR) (0.851-0.879 mm) channels of the electromagnetic spectrum from the Landsat 8 [Operational Land Imager (OLI)] sensor were obtained. Rodents were trapped in a total of 144 randomly selected grids each measuring 100 x 100 m(2), for which the corresponding values of NDVI were recorded during the corresponding rodent trapping period. Raster analysis was performed by transformation to establish NDVI in study grids over the entire study area. The relationship between NDVI, rodent distribution and abundance both spatially and temporally during the start, mid and end of the dry and wet seasons was established. Linear regression model was used to evaluate the relationships between NDVI and rodent abundance across seasons. The Pearson correlation coefficient (r) at p <= 0.05 was carried out to describe the degree of association between actual and NDVI-predicted rodent abundances. The results demonstrated a strong linear relationship between NDVI and actual rodent abundance within grids (R 2 =0.71). NDVI-predicted rodent abundance showed a strong positive correlation (r = 0.99) with estimated rodent abundance. These results support the hypothesis that NDVI has the potential for predicting rodent population abundance under smallholder farming agro-ecosystems. Hence, NDVI could be used to forecast rodent abundance within a reasonable short period of time when compared with sparse and not widely available rainfall data.
英文关键词	NDVI rainfall rodents satellite remote sensing Tanzania
类型	Article
语种	英语
国家	Tanzania ; Uganda ; Belgium
收录类别	SCI-E
WOS记录号	WOS:000518585400003
WOS类目	Zoology
WOS研究方向	Zoology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315151
作者单位	1.Sokoine Univ Agr, Pest Management Ctr, POB 3110, Morogoro, Tanzania; 2.Sokoine Univ Agr, Dept Engn Sci & Technol, POB 3003, Morogoro, Tanzania; 3.Busitema Univ, Fac Nat Resources & Environm, POB 236, Tororo, Uganda; 4.Univ Antwerp, Evolutionary Ecol Grp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium; 5.Minist Agr, Dept Training Extens Serv & Res, POB 2182, Dodoma, Tanzania
推荐引用方式 GB/T 7714	Chidodo, Davis J.,Kimaro, Didas N.,Hieronimo, Proches,et al. Application of normalized difference vegetation index (NDVI) to forecast rodent population abundance in smallholder agro-ecosystems in semi-arid areas in Tanzania[J],2020,84(2):136-143.
APA	Chidodo, Davis J..,Kimaro, Didas N..,Hieronimo, Proches.,Makundi, Rhodes H..,Isabirye, Moses.,...&Mulungu, Loth S..(2020).Application of normalized difference vegetation index (NDVI) to forecast rodent population abundance in smallholder agro-ecosystems in semi-arid areas in Tanzania.MAMMALIA,84(2),136-143.
MLA	Chidodo, Davis J.,et al."Application of normalized difference vegetation index (NDVI) to forecast rodent population abundance in smallholder agro-ecosystems in semi-arid areas in Tanzania".MAMMALIA 84.2(2020):136-143.