Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.ecolind.2018.01.032 |
Remote sensing data to assess compositional and structural indicators in dry woodland | |
Campos, Valeria E.1; Gatica, Gabriel M.2; Cappa, Flavio M.1; Giannoni, Stella M.1,2; Campos, Claudia M.3 | |
通讯作者 | Campos, Valeria E. |
来源期刊 | ECOLOGICAL INDICATORS
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ISSN | 1470-160X |
EISSN | 1872-7034 |
出版年 | 2018 |
卷号 | 88页码:63-70 |
英文摘要 | Integrating field-based and remotely sensed data has proven valuable for assessing on-the-ground diversity of plants across a range of spatial scales. Here we assessed whether remotely sensed data is a good indicator of vegetation composition and structure in dry, Prosopis flexuosa-dominated woodlands. Our objectives were (1) to quantify on-the-ground vegetation composition and structure using (A) field-based methods and (B) remotely sensed images and analysis techniques, and (2) to evaluate how well the data extracted from remotely sensed data estimate field-based measures of vegetation composition and structure. We selected 40 individuals of P. flexuosa in Ischigualasto Provincial Park (San Juan, Argentina) and its influence zone. Each individual was the center of a plot (1500-m(2)) where we recorded richness (compositional indicator) and abundance (structural indicator) of trees, shrubs and other plants (i.e. cacti, grasses and forbs). To assess woodland structure, we evaluated canopy area of each P. fiexuosa and the proportion of adult P. flexuosa trees in a plot. In addition, we used Landsat 8 OLI to calculate SATVI (Soil Adjusted Total Vegetation Index) values from the pixel that corresponds with the center of each sample plot, and then estimated first- and second-order texture measures (in 3 x 3 and 5 x 5 moving window sizes). We fitted generalized linear models with different error distributions. Vegetation richness was significantly and directly related to range and entropy (3 x 3 and 5 x 5 windows). Both trees and shrubs, were related to SATVI values and first- and second-order means (3 x 3 and 5 x 5 windows). Moreover, shrub abundance was inversely related to range and entropy (5 x 5 window); and the "other plants" group was inversely related to first- and second-order means in the same window. Variance of the canopy area was directly related to range (5 x 5 window); however, proportion of adults was not related to remote sensing data. Our findings suggest satellite imagery-derived image texture is a valuable tool for management and conservation, and can indicate areas of high plant species richness and abundance of trees and shrubs and help differentiate areas of different canopy sizes in dry P. fiexuosa-dominated woodlands of Argentina. |
英文关键词 | Argentina Desert ecosystem Prosopis fiezuosa Richness Texture measures Woodland structure |
类型 | Article |
语种 | 英语 |
国家 | Argentina |
收录类别 | SCI-E |
WOS记录号 | WOS:000430760800008 |
WOS关键词 | IMAGE TEXTURE ; HABITAT SUITABILITY ; PROSOPIS-FLEXUOSA ; VEGETATION ; DESERT ; BIODIVERSITY ; DIVERSITY ; HETEROGENEITY ; ECOSYSTEMS ; MANAGEMENT |
WOS类目 | Biodiversity Conservation ; Environmental Sciences |
WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/208722 |
作者单位 | 1.Univ Nacl San Juan, CIGEOBIO, UNSJ CONICET, CUIM, Av I de la Roza 590 O, J5402DCS Rivadavia, San Juan, Argentina; 2.Univ Nacl San Juan, Fac Ciencias Exactas Fis & Nat, Dept Biol, Av I de la Roza 590 O, J5402DCS Rivadavia, San Juan, Argentina; 3.Consejo Nacl Invest Cient & Tecn, Inst Argentino Invest Zonas Aridas IADIZA, CC507, RA-5500 Mendoza, Argentina |
推荐引用方式 GB/T 7714 | Campos, Valeria E.,Gatica, Gabriel M.,Cappa, Flavio M.,et al. Remote sensing data to assess compositional and structural indicators in dry woodland[J],2018,88:63-70. |
APA | Campos, Valeria E.,Gatica, Gabriel M.,Cappa, Flavio M.,Giannoni, Stella M.,&Campos, Claudia M..(2018).Remote sensing data to assess compositional and structural indicators in dry woodland.ECOLOGICAL INDICATORS,88,63-70. |
MLA | Campos, Valeria E.,et al."Remote sensing data to assess compositional and structural indicators in dry woodland".ECOLOGICAL INDICATORS 88(2018):63-70. |
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