Abstract:Aim
We identify the main forest vegetation types in Taiwan, provide their formal definitions and describe their species composition, habitat affinities and distribution.
Location
Taiwan.
Methods
A data set of 9822 vegetation plots with environmental characteristics recorded in the field or derived from digital maps in GIS was compiled from historical literature and an extensive field survey. Using expert knowledge, 6574 of these plots were used to build a classification into broad vegetation types. The units o… Show more
“…Steep and fragile slopes are common, and landslides frequently occur following heavy rains, road construction, or earthquakes. Temperature mostly corresponds with altitude, with a mean altitudinal lapse rate of 0.55 • C per 100 m, whereas differences in precipitation are caused primarily by monsoon exposure, particularly the northeastern monsoon in winter [1]. In summer, the warm southwestern monsoons and typhoons lead to abundant precipitation across the entire island.…”
Section: Study Areamentioning
confidence: 99%
“…Temperature in January was calculated from the monthly mean temperatures computed from satellite images with a resolution of 1 km × 1 km. Temperature data derived from satellite images were more sensitive to topography than the temperature data calculated from the regression model of weather stations [1]. Winter precipitation (the ratio of the sum of precipitation of December, January and February divided by the annual precipitation) was taken as interpolated values from the WorldClim database [29].…”
Section: Classification Of Vegetation Typesmentioning
confidence: 99%
“…Its mountainous topography (highest peak: 3952 m a.s.l.) forms distinct altitudinal vegetation zones, with vegetation corresponding to several latitudinal zones from subarctic to tropical [1] and different geographic areas that vary by precipitation and temperature. Based on climatic data, Taiwan has been classified into seven ecoregions [2].…”
Section: Introductionmentioning
confidence: 99%
“…To describe and parameterize recognized vegetation types, formalization can be achieved using supervised classification methods that create unequivocal rules and criteria for determining vegetation types. An example is the cocktail determination key [18][19][20][21], which describes each vegetation type using a logical formula that combines statements concerning the presence or absence of typical or dominant species [1]. The cocktail determination key was developed by combining the presence or absence of specific ecologically meaningful species groups [18] that provide unequivocal rules for assigning relevés to vegetation types [20][21][22][23].…”
Abstract:In this study, we classified twenty associations, 179 families, 810 genera, 1897 species, and identified 291 rare species. The vegetation units were named following floristic-sociological approach. A cocktail determination key was used to classify the following vegetation units of association. More of the west side than the east side of the study area was at a higher altitude, and the vegetation experienced significant compression. Follow the result of detrended correspondence analysis (DCA), winter rainfall and average January temperature were the main environmental factors affecting variation in vegetation distribution by elevation gradient in this area. The particular association type on the eastern side of the study area is attributable to the northeast monsoon experienced year-round in this area, which causes relatively low temperature and humidity and wind rush, as well as the compression of the elevation range in each forest. The monsoon deciduous vegetation area exists only on the west of the low altitude mountains.
“…Steep and fragile slopes are common, and landslides frequently occur following heavy rains, road construction, or earthquakes. Temperature mostly corresponds with altitude, with a mean altitudinal lapse rate of 0.55 • C per 100 m, whereas differences in precipitation are caused primarily by monsoon exposure, particularly the northeastern monsoon in winter [1]. In summer, the warm southwestern monsoons and typhoons lead to abundant precipitation across the entire island.…”
Section: Study Areamentioning
confidence: 99%
“…Temperature in January was calculated from the monthly mean temperatures computed from satellite images with a resolution of 1 km × 1 km. Temperature data derived from satellite images were more sensitive to topography than the temperature data calculated from the regression model of weather stations [1]. Winter precipitation (the ratio of the sum of precipitation of December, January and February divided by the annual precipitation) was taken as interpolated values from the WorldClim database [29].…”
Section: Classification Of Vegetation Typesmentioning
confidence: 99%
“…Its mountainous topography (highest peak: 3952 m a.s.l.) forms distinct altitudinal vegetation zones, with vegetation corresponding to several latitudinal zones from subarctic to tropical [1] and different geographic areas that vary by precipitation and temperature. Based on climatic data, Taiwan has been classified into seven ecoregions [2].…”
Section: Introductionmentioning
confidence: 99%
“…To describe and parameterize recognized vegetation types, formalization can be achieved using supervised classification methods that create unequivocal rules and criteria for determining vegetation types. An example is the cocktail determination key [18][19][20][21], which describes each vegetation type using a logical formula that combines statements concerning the presence or absence of typical or dominant species [1]. The cocktail determination key was developed by combining the presence or absence of specific ecologically meaningful species groups [18] that provide unequivocal rules for assigning relevés to vegetation types [20][21][22][23].…”
Abstract:In this study, we classified twenty associations, 179 families, 810 genera, 1897 species, and identified 291 rare species. The vegetation units were named following floristic-sociological approach. A cocktail determination key was used to classify the following vegetation units of association. More of the west side than the east side of the study area was at a higher altitude, and the vegetation experienced significant compression. Follow the result of detrended correspondence analysis (DCA), winter rainfall and average January temperature were the main environmental factors affecting variation in vegetation distribution by elevation gradient in this area. The particular association type on the eastern side of the study area is attributable to the northeast monsoon experienced year-round in this area, which causes relatively low temperature and humidity and wind rush, as well as the compression of the elevation range in each forest. The monsoon deciduous vegetation area exists only on the west of the low altitude mountains.
“…While cloud forest areas of Taiwan are already the subject of intensive research (cf., e.g., Mildenberger et al, 2009;Chu et al, 2012), Taiwan's cloud forest has never been completely mapped on a country-wide scale. The most comprehensive information about the extent of cloud forest in Taiwan available today is given by Li et al (2013), based on the National Vegetation Database of Taiwan. Since these data are based on field surveys, they are highly reliable but only cover the area of 9822 plots (each with an area of 400-2000 m 2 ) distributed over the whole country.…”
Abstract. The mountain cloud forest of Taiwan can be delimited from other forest types using a map of the ground fog frequency. In order to create such a frequency map from remotely sensed data, an algorithm able to detect ground fog is necessary. Common techniques for ground fog detection based on weather satellite data cannot be applied to fog occurrences in Taiwan as they rely on several assumptions regarding cloud properties. Therefore a new statistical method for the detection of ground fog in mountainous terrain from MODIS Collection 051 data is presented. Due to the sharpening of input data using MODIS bands 1 and 2, the method provides fog masks in a resolution of 250 m per pixel. The new technique is based on negative correlations between optical thickness and terrain height that can be observed if a cloud that is relatively plane-parallel is truncated by the terrain. A validation of the new technique using camera data has shown that the quality of fog detection is comparable to that of another modern fog detection scheme developed and validated for the temperate zones. The method is particularly applicable to optically thinner water clouds. Beyond a cloud optical thickness of ≈ 40, classification errors significantly increase.
Litterfall is important for returning nutrients and carbon to the forest floor, and microbes decompose the litterfall to release CO 2 into the atmosphere. Litterfall is a pivotal component in the forest biogeochemical cycle, which is sensitive to climate variability and plant physiology. In this study, we combined field litterfall estimates and time series (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) climate (the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and Tropical Rainfall Measuring Mission (TRMM) precipitations) and green vegetation (MODIS photosynthetically active vegetation cover (PV)) variables to estimate regional annual litterfall in tropical/subtropical forests in Taiwan. We found that time series MODIS LST-and PV-derived metrics, the annual accumulated MODIS LST, and coefficient of variation of PV, respectively, but not the TRMM precipitation variables were salient factors for the estimation (r 2 = 0.548 and p < 0.001). The mean (±standard deviation) annual litterfall was 5.1 ± 1.2 Mg ha À1 yr À1 during the observation period. The temporal dynamics of the litterfall revealed that typhoons and consecutive drought events might affect the litterfall temporal variation. Overall, the annual litterfall decreased along the elevation gradient, which may reflect a change in the vegetation type. The northeast and northwest facing slopes yielded the highest amount of annual litterfall (≥5.9 Mg ha À1 yr À1 ), which was in contrast with the southern aspect (5.1 Mg ha À1 yr À1 ). This variation may be associated with the dryness of the microclimate influenced by solar radiation. This study demonstrates the feasibility of utilizing time series MODIS LST and PV data to predict large-scale field litterfall, which may facilitate large-scale monitoring of biogeochemical cycles in forest ecosystems.
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