Arid
DOI10.7717/peerj.4603
Predicting Pinus monophylla forest cover in the Baja California Desert by remote sensing
Escobar-Flores, Jonathan G.1; Lopez-Sanchez, Carlos A.2; Sandoval, Sarahi3; Marquez-Linares, Marco A.1; Wehenkel, Christian2
通讯作者Wehenkel, Christian
来源期刊PEERJ
ISSN2167-8359
出版年2018
卷号6
英文摘要

The Californian single-leaf pinyon (Pinus manophyna var. californiarum), a subspecies of the single-leaf pinyon (the world’s only one-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This tree is distributed as a relict subspecies, at elevations of between 1,010 and 1,631 m in the geographically isolated arid Sierra La Asamblea, an area characterized by mean annual precipitation levels of between 184 and 288 mm. The aim of this research was (1) to estimate the distribution of P. monophylla var. californiaruin in Sierra La Asamblea by using Sentinel-2 images, and (ii) to test arid de scribe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that (i) Sentinel-2, images can be used to predict the P. monophylla distribution in the study site due to the finer resolution (x3) and greater number of bands (x2) relative to Landsat-8 data,which is publically available free of charge and has been demonstrated to be useful for estimating forest cover, and (ii) the topographical variables aspect, ruggedness and lope are particularly important important because they represent important microhabitat factors that can determine the sites where conifers can become established and persist. An atmospherically corrected a 12-bit Sentinel-2A MSI image with 10 spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index (NDVI). Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multiple linear binominal logistical regression and Random Forest classification including cross validation were used to model the associations between presence/absence of monophylla and the five topographical and 18 climate variables. Using supervised classification of Sentinel-2 satellite images, we estimated that P. monophylla covers 6,653 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed most to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). Ruggedness was the most influential environmental predictor variable, indicating that the probability of occurrence of P. monophylla was greater than 50% when the degree of ruggedness terrain ruggedness index was greater than 17.5 m. The probability of occurrence of the species decreased when the mean temperature in the warmest month increased from 23.5 to 25.2 degrees C. Ruggedness is known to create microclimates and provides shade that minimizes evapotranspiration from pines in desert environments. Identification of the P. monophylla stands in Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.


英文关键词DEM Sentinel-2 Ruggedness Remote sensing Neural net Forest Baja California NDVI Kappa Classification
类型Article
语种英语
国家Mexico
收录类别SCI-E
WOS记录号WOS:000429846700005
WOS关键词ARTIFICIAL NEURAL-NETWORK ; SPECIES DISTRIBUTION MODELS ; CLASSIFICATION ; TREE ; ACCURACY ; DROUGHT ; CLIMATE ; GROWTH ; SOIL ; PRODUCTIVITY
WOS类目Multidisciplinary Sciences
WOS研究方向Science & Technology - Other Topics
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/212024
作者单位1.Inst Politecn Nacl, Ctr Interdisciplinario Invest Desarrollo Integral, Unidad Durango, Durango, Durango, Mexico;
2.Univ Juarez Estado Durango, Inst Silvicultura & Ind Madera, Durango, Mexico;
3.CONACYT Inst Politecn Nacl, CIIDIR Unidad Durango, Durango, Durango, Mexico
推荐引用方式
GB/T 7714
Escobar-Flores, Jonathan G.,Lopez-Sanchez, Carlos A.,Sandoval, Sarahi,et al. Predicting Pinus monophylla forest cover in the Baja California Desert by remote sensing[J],2018,6.
APA Escobar-Flores, Jonathan G.,Lopez-Sanchez, Carlos A.,Sandoval, Sarahi,Marquez-Linares, Marco A.,&Wehenkel, Christian.(2018).Predicting Pinus monophylla forest cover in the Baja California Desert by remote sensing.PEERJ,6.
MLA Escobar-Flores, Jonathan G.,et al."Predicting Pinus monophylla forest cover in the Baja California Desert by remote sensing".PEERJ 6(2018).
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