Urban forest assessments have been implemented in many cities worldwide to evaluate the urban forest structure and function. This study is the first step to institutionalize urban forest assessments in Thailand. Thus, the objective of this study was to conduct a pilot urban forest ecosystem assessment for Thailand, determine the urban forest value, and pilot study the appropriateness of adapting i-Tree Eco in Thailand. A stratified random sampling method was used to collect the field information. All data from 184 sampling plots were analyzed using i-Tree Eco for the urban forest structure, function, and value. The urban forest assessment in Bangkok showed a diverse mixture of 48 tree species. The three most common tree species which contributed 34.1% of total tree population were Polyalthia longifolia Sonn (15.7%), Mangifera indica L. (13.0%), and Pithecellobium dulce (Roxb.) Benth (5.4%). The majority of trees (approximately 70%) were < 23 cm in diameter. Nearly equally numbers of trees were in the ≤ 7.6 cm (24.4%), 7.7-15.2 cm (23.9%) and 15.3-22.9 cm (21.5%) diameter classes. An estimated 2,504,000 trees (S.E. = 408,646) exist in the Bangkok study area and these trees provide an 8.6% canopy cover. These trees store an approximate total of about 310,000 metric
A B S T R A C TEcosystem service estimation is a very popular topic. Many urban studies use the i-Tree Eco model developed by US Forest Service to estimate ecosystem services. Several ecosystem service estimation studies have been conducted acting upon the assumption that relationships developed elsewhere are applicable to sites that vary in species, site, climate, and environmental conditions. This study tested the accuracy of highly used existing leaf area and biomass models when used outside the region in which it was developed. To do this, we measured 74 urban trees from five species in Stevens Point, Wisconsin collecting data such as diameter at breast height (Dbh), tree height, height to the base of live crown, crown width, crown volume, leaf area, and leaf dry weight biomass. Using the data, we developed two models each to predict leaf area and biomass. Using ten independent samples, we compared our predictions with predictions from the existing models which are also used in i-Tree. Our results indicated that the local models developed in the current study predicted leaf area and biomass better than existing models which had higher prediction error. The difference in prediction will ultimately affect ecosystem services estimation when. using i-Tree, and future studies should acknowledge the difference.
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