PredictingPinus monophyllaforest cover in the Baja California Desert by remote sensing

Abstract

The Californian single-leaf pinyon (Pinus monophylla 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 (i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea 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 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 P. 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 °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.

Identifying Vegetation in Arid Regions Using Object-Based Image Analysis with RGB-Only Aerial Imagery

“…Much research has focused on time series analyses of desertification or forest decline (Peters et al [66], Joshi et al [67], and more recently Dorman et al [68], Bajocco et al [69], Fensholt et al [70], and Zhang et al [71]). Remote sensing data has aided research in tracking the health and distribution of certain species of vegetation (Escobar-Flores et al [72], Pham et al [4], Paz-Kagan et al [5]). Yet, again, all of the above employed additional spectral bands.…”

Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data

“…Another big advantage of this mission is the wide swath of 290 km (more than 100 km wider than the Landsat satellite), which makes it an ideal sensor for forest analyses over a large area at the regional and national scales. A few studies have focused on the use of the Sentinel-2 data for the classification of tree type and tree species [26,[29][30][31][32][33]. Wessel et al [30] analysed the separation of beech from oak trees in a managed forest in Bavaria.…”

Effects of afforestation on water resource variations in the Inner Mongolian Plateau

“…In recent years, a large number of people in Shanxi Province, Inner Mongolia, and parts of Beijing and Tianjin cities and Hebei Province in North China has suffered from the destruction of the ecological environment and the scarcity of water resources resulting from conditions of almost continuous drought. Given that the Inner Mongolian Plateau is the primary ecological barrier of Inner Mongolia and the Beijing-Tianjin-Hebei region, for those who rely on this barrier, the methods used by the Government of China to protect the ecological environment and water resources on the plateau will have a significant impact on the effectiveness of the ecological barrier of the region (Miao et al, 2015; Escobar-Flores et al, 2018). Many studies have reported that China’s large-scale water resource protection plans and water transport projects will significantly affect the ecological environment and available water resources in the Inner Mongolian Plateau and the Beijing-Tianjin-Hebei region.…”