Predicting Pinus monophylla forest cover in the Baja California Desert by remote sensing

• Published in: PeerJ (San Francisco, CA) Vol. 6; p. e4603
• Main Authors: Escobar-Flores, Jonathan G, Lopez-Sanchez, Carlos A, Sandoval, Sarahi, Marquez-Linares, Marco A, Wehenkel, Christian
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 04-04-2018; PeerJ, Inc; PeerJ Inc
• Subjects: Analysis; Classification; Climate; Conifers; DEM; Distribution; Ecology; Environmental aspects; Environmental monitoring; Evapotranspiration; Forest; Landsat; Leaves; Methods; Microenvironments; Neural net; Neural networks; Pinus monophylla; Piñon pines; Plant Science; Precipitation; Quercus; Remote sensing; Ruggedness; Sentinel-2; Topography; Trees; Vegetation; Vegetation dynamics; United States; Mexico; Mojave Desert; California; Baja California Mexico; United States > US; Neural net; Kappa; Baja California; Classification; Sentinel-2; Forest; DEM; NDVI; Remote sensing; Ruggedness
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.4603

The Californian single-leaf pinyon (
var.
), 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 (i) to estimate the distribution of
var.
in Sierra La Asamblea by using Sentinel-2 images, and (ii) to test and describe the relationship between the distribution of
and five topographic and 18 climate variables. We hypothesized that (i) Sentinel-2 images can be used to predict the
distribution in the study site due to the finer resolution (×3) and greater number of bands (×2) 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 slope are particularly 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
and the five topographical and 18 climate variables. Using supervised classification of Sentinel-2 satellite images, we estimated that
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
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 °C. Ruggedness is known to create microclimates and provides shade that minimizes evapotranspiration from pines in desert environments. Identification of the
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.