Spatial modeling of the carbon stock of forest trees in Heilongjiang Province, China

Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.

Spatiotemporal variation and driving factors of water yield services on the Qingzang Plateau

Water resources are a basic need for social sustainable development and human existence.As an important national strategy for water resources security,spatial and temporal patterns and driving mechanisms of water yield ecosystem services on the Qingzang Plateau(QP)are critical for water resources management,optimal water allocation and the improvement of ecological water protection efficiency.However,only a few relevant studies are currently available.In this study,we simulated the water yield(WY)of the QP over 34 years,from 1982 to 2015,using the InVEST model and analyzed the spatiotemporal dynamic relationships between WY and climate change as well as between WY and vegetation change,using geographically weighted regression(GWR)models.The results showed that:1)from 1982 to 2015,the WY of the QP increased at an average rate of 3.8 mm/yr;2)WY presented a reduced spatial pattern from southeast to northwest;and 3)the WY driving factors have individual and spatial differences.In terms of the area percentage in promoting WY when analyzing each driving factor,precipitation(99.8%)and air pressure(53.3%)played the major roles in promoting WY,while temperature(71.9%),wind speed(57.2%),net primary productivity(87.2%),radiation(68.3%)and lake(87.7%)played negative roles.The areas where WY are dominated by temperature are the largest(41.1%),and followed by areas dominated by pressure(19.7%)and precipitation(18.5%).The results of this study provide scientific support for formulating regional water resources policy,social and economic development planning and other macro decisions for the QP.

Land use intensity dynamics in the Andhikhola watershed, middle hill of Nepal

Land use intensity is a valuable concept to understand integrated land use system, which is unlike the traditional approach of analysis that often examines one or a few aspects of land use disregarding multidimensionality of the intensification process in the complex land system. Land use intensity is based on an integrative conceptual framework focusing on both inputs to and outputs from the land. Geographers’ non-stationary data-analysis technique is very suitable for most of the spatial data analysis. Our study was carried out in the northeast part of the Andhikhola watershed lying in the Middle Hills of Nepal, where over the last two decades, heavy loss of labor due to outmigration of rural farmers and increasing urbanization in the relatively easy accessible lowland areas has caused agricultural land abandonment. Our intention in this study was to ascertain factors of spatial pattern of intensity dynamism between human and nature relationships in the integrated traditional agricultural system. High resolution aerial photo and multispectral satellite image were used to derive data on land use and land cover. In addition, field verification, information collected from the field and census report were other data sources. Explanatory variables were derived from those digital and analogue data. Ordinary Least Square(OLS) technique was used for filtering of the variables. Geographically Weighted Regression(GWR) model was used to identify major determining factors of land use intensity dynamics. Moran’s I technique was used for model validation. GWR model was executed to identify the strength of explanatory variables explaining change of land use intensity. Accordingly, 10 variables were identified having the greatest strength to explain land use intensity change in the study area, of which physical variables such as slope gradient, temperature and solar radiation revealed the highest strength followed by variables of accessibility and natural resource. Depopulation in recent decades has been a major driver of land use intensity change but spatial variability of land use intensity was highly controlled by physical suitability, accessibility and availability of natural resources.

Spatial heterogeneity and its influencing factors of carbon emissions in 17 cities of Shandong province

Using data of prefecture-level cities in Shandong province from 2004 to 2012 and the Stochastic Impacts by Regression on Population,Affluence,and Technology framework,this paper builds the geographically weighted regression(GWR)model of carbon emissions and its influencing factors.Unlike traditional econometric methods,such as ordinary least squares(OLS),the spatial econometrics models of spatial lag model(SLM)and spatial error model(SEM)are often estimate parameters constantly,namely these methods just estimate parameters in "average" or "globally" and can not reflect the parameters' nonstationary in different spaces.So in this paper,the influencing factors of carbon emissions are estimated by GWR,and the influencing factors of carbon emissions are estimated to be more realistic.The results indicates that the local GWR model is better than OLS,SLM and SEM,and there is spatial heterogeneity between the factors involved in economic growth,population status,industrial structure,energy price and carbon emissions across cities in Shandong province.

Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China

Global warming has been one of the major concerns behind the world＇s high-speed economic growth. How to implement the coordinated development of the carbon footprint and the economy will be the core issue of the world＇s economic and social development, as well as the heated debate of the research at home and abroad in recent years. Based on the energy consumption, integrated with the ＂Top-Down＂ life cycle approach and geographically weighted regression （GWR） model, this paper analyzed the spatial differences and multi-mechanism of carbon footprint in provincial China in 2010. Firstly, this study calculated the amount of carbon footprint of each province using ＂Top-Down＂ life cycle approach and found that there were significant differences of carbon footprint and per capita carbon footprint in provincial China. The provinces with higher carbon footprint, mainly located in northern China, have large economic scales; the provinces with higher per capita carbon footprint are mainly distributed in central cities such as Beijing, Shanghai and energy-rich regions and heavy chemical bases. Secondly, with the aid of GIS and spatial analysis model （GWR model）, this paper had unfolded that the expansion of economic scale is the main driver of the rapid growth of carbon footprint. The growth of population and urbanization also acted as promoting factors for the increase of the carbon footprint. Energy structure had no considerable promoting effect for the increase of the carbon footprint. Improving energy efficiency is the most important factor to inhibit the growing carbon footprint. Thirdly, developing low-carbon economies and low-carbon industries, as well as advocating low-carbon city construction and improving carbon efficiency would be the primary approaches to inhibit the rapid growth of carbon footprint. Moderately controlling the economic scale and population size would also be required to alleviate carbon footprint. Meanwhile, environmental protection and construction of low-carbon cities would evoke extensive attention in the process of urbanization.