Environmental changes in biodiversity hotspot ecosystems of
South Ecuador: RESPonse and feedback effECTs (FOR2730)

Abstract:

Forest site classification adapted to the respective site conditions is one prerequisite for sustainable silviculture. This work aims to initiate the forest site classification for pine plantations in the southern Andean region of Ecuador. Forest productivity, estimated by the dominant height of 20-year-old trees (DH20), was related to data from climate, topography, and soil using 23 plots installed in pine plantations in the province of Loja. Forest site productivity was classified as: low (class C: 13.4 m), middle (class B: 16.6 m), and high (Class A: 22.3 m). Strong determinants to differentiate the forest site classes were: the short to medium term available Ca and K stocks (organic layer + mineral soil standardized to a depth of 60 cm), soil acidity, the C:N ratio, clay and sand content, forest floor thickness, altitude, and slope. The lowest forest productivity (Class C) is mainly associated with the lowest short to medium term available K and Ca stocks. Whereas, in site classes with the highest forest productivity, pines could benefit from a more active microbial community releasing N and P, since the soil pH was about 1 unit less acidic. This is supported by the lowest forest floor thickness and the narrowest C:N ratio.

Abstract:

Geographic Information Systems and Remote Sensing are important contributors to Sustainable Forestry Management Plans. Remote sensing techniques for image interpretation provides the means to extract valuable information that could be expensive and time-consuming to obtain through field observations (Franklin et al. 2001). Spatial Products derived from the interpretation of airborne and satellite borne images feed Geographic Information Systems to develop strategies and methodologies for resource management, harvest planning, fire management, map production, and model predictions. (Yusmah et al. 2015) This study has three important objectives: to test the feasibility of template matching for the identification of single pine tree crowns, to conduct a delineation of pine plantations using relational features and to evaluate how single tree crown size affects the accuracy of the proposed method. Templates of single trees were produced in the software eCognition Developer. The sampling process comprised the random selection of 3000 single pine trees in 7 different test sites (test sites were grouped in 3 categories according to the single tree sizes). A first rule set to detect single tree crowns was developed in eCognition Developer, using three different template groups (4, 8 and 16 templates). Through an analysis of variance, the number of single tree crowns detected was compared for the different template groups. Using a second rule set in eCognition, the template matching algorithm combined with relational, spectral and contextual information were applied to delineate pine plantation areas. An accuracy assessment was performed in the test sites for all thematic classes identified. Finally, an Analysis of Variance evaluated the influence of single tree crown size on the overall accuracy. Potential applications and improvements to the proposed methodology for single tree crown detection and plantation delineation are proposed at the end of the document.