Academy of Forestry Inventory and Planning

Beijing, China

Academy of Forestry Inventory and Planning

Beijing, China
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Wang J.,Beijing Forestry University | Sun T.,Academy of Forestry Inventory and Planning | Fu A.,Academy of Forestry Inventory and Planning | Xu H.,Ningxia University | Wang X.,Beijing Forestry University
Theoretical and Applied Climatology | Year: 2017

Degradation in drylands is a critically important global issue that threatens ecosystem and environmental in many ways. Researchers have tried to use remote sensing data and meteorological data to perform residual trend analysis and identify human-induced vegetation changes. However, complex interactions between vegetation and climate, soil units and topography have not yet been considered. Data used in the study included annual accumulated Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m normalized difference vegetation index (NDVI) from 2002 to 2013, accumulated rainfall from September to August, digital elevation model (DEM) and soil units. This paper presents linear mixed-effect (LME) modeling methods for the NDVI-rainfall relationship. We developed linear mixed-effects models that considered the random effects of sample points nested in soil units for nested two-level modeling and single-level modeling of soil units and sample points, respectively. Additionally, three functions, including the exponential function (exp), the power function (power), and the constant plus power function (CPP), were tested to remove heterogeneity, and an additional three correlation structures, including the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)] and the compound symmetry structure (CS), were used to address the spatiotemporal correlations. It was concluded that the nested two-level model considering both heteroscedasticity with (CPP) and spatiotemporal correlation with [ARMA(1,1)] showed the best performance (AMR = 0.1881, RMSE = 0.2576, adj-R2 = 0.9593). Variations between soil units and sample points that may have an effect on the NDVI-rainfall relationship should be included in model structures, and linear mixed-effects modeling achieves this in an effective and accurate way. © 2017 Springer-Verlag Wien

Zhang Z.,Academy of Forestry Inventory and Planning | Tian X.,Chinese Academy of Forestry | Chen E.-X.,Chinese Academy of Forestry | He Q.-S.,Hohai University
Beijing Linye Daxue Xuebao/Journal of Beijing Forestry University | Year: 2011

According to the data source used, the major methods globally used to estimate forest above-ground biomass were introduced in this paper. The methods can be categorized into forest inventory data based method and remote sensing based method. Based on previous studies conducted in China and abroad, we summarized the characteristics and the deficiencies of these two kinds of methods and then preliminarily explored the synergetic method in estimating forest aboveground biomass using multi-source data. In accordance with the deficiencies of estimation methods, some discussions about the scale, parameterization and validation of the models were given and some work concerning the estimation of forest aboveground biomass in the future was stressed.

Jiang N.,Zhejiang Academy of Agricultural Sciences | Jiang N.,CAS Kunming Institute of Botany | Peng X.-M.,Academy of Forestry Inventory and Planning | Yu W.-B.,CAS Kunming Institute of Botany
Novon | Year: 2011

Asarum longirhizomatosum, originally described from Guanxi Province in China by C. F. Liang and C. S. Yang in 1975, was invalidly published in the original publication, because two collections were cited as type. The name is validated here by designating the collection Chao-Liang Zhang 002 (IBK 00190377) as the holotype.

Peng X.-M.,Academy of Forestry Inventory and Planning | He Z.,Academy of Forestry Inventory and Planning | Yu W.-B.,CAS Kunming Institute of Botany
Bangladesh Journal of Plant Taxonomy | Year: 2011

In Flora Reipublicae Popularis Sinicae and Flora of China, the reference citations of Myristica yunnanensis and Cyclobalanopsis yonganensis are incorrect. The publication date of Myristica yunnanensis is 1977, not 1976, and that of Cyclobalanopsis yonganensis is 1999, not 1993. Additionally, the authorship of the combination C. yonganensis belongs to C. C. Huang, Y. T. Zhang and B. Bartholomew, but not to Y. C. Hsu and H. W. Jen. To formalize the usage of the two names, they are revised here. © 2011 Bangladesh Association of Plant Taxonomists.

Liu Y.C.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Liu Y.C.,Academy of Forestry Inventory and Planning | Yu G.R.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Wang Q.F.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | And 2 more authors.
Science China Life Sciences | Year: 2014

Forests play an important role in acting as a carbon sink of terrestrial ecosystem. Although global forests have huge carbon carrying capacity (CCC) and carbon sequestration potential (CSP), there were few quantification reports on Chinese forests. We collected and compiled a forest biomass dataset of China, a total of 5841 sites, based on forest inventory and literature search results. From the dataset we extracted 338 sites with forests aged over 80 years, a threshold for defining mature forest, to establish the mature forest biomass dataset. After analyzing the spatial pattern of the carbon density of Chinese mature forests and its controlling factors, we used carbon density of mature forests as the reference level, and conservatively estimated the CCC of the forests in China by interpolation methods of Regression Kriging, Inverse Distance Weighted and Partial Thin Plate Smoothing Spline. Combining with the sixth National Forest Resources Inventory, we also estimated the forest CSP. The results revealed positive relationships between carbon density of mature forests and temperature, precipitation and stand age, and the horizontal and elevational patterns of carbon density of mature forests can be well predicted by temperature and precipitation. The total CCC and CSP of the existing forests are 19.87 and 13.86 Pg C, respectively. Subtropical forests would have more CCC and CSP than other biomes. Consequently, relying on forests to uptake carbon by decreasing disturbance on forests would be an alternative approach for mitigating greenhouse gas concentration effects besides afforestation and reforestation. © 2014, The Author(s).

Gong M.H.,Chinese Academy of Forestry | Liu G.,Chinese Academy of Forestry | Guan T.P.,Mianyang Normal University | Li H.X.,Chinese Academy of Forestry | And 2 more authors.
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2016

Population dispersal is an important life history trait that is influenced by environmental change, and it can alter the distribution, structure, and abundance of a population. In addition, population dispersal allows a species to actively adapt and ensure long-term survival. Patterns of population dispersal can provide key information about the rules and mechanisms of how populations disperse, and they are an important basis for conservation management. Methods for studying population dispersal in large animals are lacking, which therefore restricts the development and application of dispersal ecology. Two crucial issues that need to be taken into account when considering dispersal patterns are population distribution and abundance. Based on the factors of dispersal pattern and the giant panda characteristics of population and home range, this study intends to (1) reveal the dispersal patterns of giant pandas in the Qinling Mountains by comparing the change in their population distribution and aggregation from 2000 to 2012, and (2) explore methods for studying large animal population dispersal. Based on signs of giant pandas obtained from the third and fourth national surveys conducted by the Chinese Forestry Administration (completed in 2000 and 2012, respectively), a circular extension region with a giant panda sign as the center was produced using the buffer function in ArcCISlO.O. The average diameter of the home range of giant pandas was defined as 3 km. Subsequently, using the dissolve function in ArcCIS, we created polygons based on these circles, and established the primary population distribution area around the outer boundary of the polygons. We identified the population dispersal area based on the change in distribution range. Additionally, we mapped population aggregation densities in 2OOO and 2O12, and divided the population distribution range into areas with different aggregation densities by employing the kernel density analysis function of ArcCIS. We also revealed the population abundance and direction of population dispersal based on the variation in population aggregation. We found that the population distribution area of giant pandas increased by 153O7.8 hm2 in the Qinling Mountains since 2OOO, with an obvious expanding trend in the northwestern and southwestern regions of the study area. However, the population distribution decreased in the eastern and southern regions. Furthermore, the degree of population aggregation increased, especially for areas with medium aggregation density, and two patches of high-density aggregation became one. In addition, the integrity of the population aggregation pattern also greatly improved. In this way, the population pattern showed a detectable trend of expansion and an increase in population aggregation. This study revealed the area and direction of giant pandas dispersal in the Qinling Mountains since 2OOO, which has important implications for current population safety and conservation. Our study also showed that the population dispersal patterns of giant pandas could be effectively determined with the spatial variation in population distribution and abundance that is based on biological characteristics and long-term monitoring. The methodology developed in this study, combined with ongoing monitoring programs in Chinese nature reserves, can facilitate the study of population dispersal in large animals. © 2016, Ecological Society of China. All rights reserved.

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