Can Landsat imagery detect tree line dynamics?

Abstract

Prior research on tree line dynamics has primarily been based on field inventory data, and little is known about the potential for using remotely sensed imagery to detect change. The present study developed a new methodology by combining remote sensing and field survey data to examine dynamics of the pristine forest in the tree line area on Changbai Mountain in Northeast China over the past two decades. The new method normalized remote sensing data by using the adjacent old-growth coniferous forest (close to climax) below the tree line as the reference (assuming steady state) to eliminate various potential errors associated with different satellite sensors, atmospheric conditions and seasonal vegetation changes. Specifically, we used a ratio of normalised difference vegetation index (NDVI) between the tree line forest (birch) and the referenced old-growth coniferous forest, as well as a ratio between the interface forest against the referenced coniferous forest, to investigate growth dynamics of the tree line forest from 1977 to 1999. The interface forest is distributed between the tree line forest and non-forest zone. Compared with traditional methods using remote sensing data, the new method has higher sensitivity to tree line dynamics. We found that the tree line delineated from satellite imagery had shown no shift in the past decades. However, the NDVI ratio of tree line forest against the reference forest increased from 0.9 to 1.2 from 1977 to 1999, and the ratio of interface forest against the reference forest increased from 0.83 to 0.98, indicating that growth of the tree line forest apparently exceeded that of the reference coniferous forest and had grown denser. Field surveys also supported the conclusion from remote sensing results that the tree line forest on Changbai Mountain has grown denser in the past decades. From 1980 to 2002, basal area of the tree line forest increased 35% (from 18.8 to 25.3 m 2 ha 21 ); at the same time the NDVI ratio increased about 33%. This study suggests that it is possible to monitor growth of the tree line forest based on multi-temporal remote sensing images. We speculate that global warming may have contributed to the observed rapid growth of the tree line forest because temperature is a major limiting factor among climatic variables in the tree line forest and effect from other factors, such as fire, harvesting and snow and wind damage, were not evident in this protected area in the past decades.

Modeling the Boundaries of Plant Ecotones of Mountain Ecosystems

“…Ecotone edges were identified by various hierarchical methods based on wavelet analysis and by bivariate techniques of edge detection, using filters and kernels [28,29]. Both field measurements and remote sensing data are used to detect and describe ecotone properties and analyze changes of their boundaries [30][31][32][33][34][35].…”

The role of spatial data and geomatic approaches in treeline mapping: a review of methods and limitations

“…Pixel-based [Tattoni et al, 2010] and object-oriented [Sitko et al, 2008] classification approaches, both supervised [Bader et al, 2008;Kral, 2009] and unsupervised [Klasner et al, 2002;, of multispectral aerial and satellite imagery, are also used. Some studies base mapping on screen image-interpretation [Zhang et al, 2009]. Ground surveys, mainly performed by GNSS (Global Navigation Satellite Systems) [Groen et al, 2012;Ropars et al, 2012] are widely used to collect ground truth samples that are in turn used to train classifiers and validate classification results.…”

“…For instance, optical remote sensing techniques, in particular, are recognized as an essential tool for treeline ecotone observation, with specific focus on its position and dynamics. This is generally achieved by using bi-dimensional primary datasets such as satellite data [Driese, 1997;Milah et al, 2007;Bader, 2008;Zhang et al, 2009;Panigraphy et al, 2010], digital aerial orthoimages [Stueve et al, 2009;Ropas et al, 2012], and current and historical maps [Mast et al, 1997;Bryn, 2008], all managed by GIS (Geographic Information Systems) [Paulsen and Korner, 2001;Stueve et al, 2009]. Additionally, some basic terrain features (e.g.…”

Effects of wind and thermal conditions on timberline formation in central Japan: a lattice model

“…Other studies also showed that tree density and biomass production increased at timberlines from the past to the present, although the altitudinal location of timberlines and treelines did not change (Camarero and Gutierrez 2004;Zhang et al 2009;Liang et al 2011). For example, increase in biomass production was recognized for 26.7 % of tree species examined by using meta-analysis (Lin et al 2010).…”