Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing
- Published
- Accepted
- Subject Areas
- Ecology, Plant Science
- Keywords
- DEM, Sentinel-2., ruggedness, remote sensing, neural net, forest, Baja California, NDVI, Kappa, Classification
- Copyright
- © 2018 Escobar-Flores et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing. PeerJ Preprints 6:e3439v5 https://doi.org/10.7287/peerj.preprints.3439v5
Abstract
Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe 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 higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.
Author Comment
This is a preprint submision to Peer J
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