Jiangsu Province, China
Jiangsu Province is located in eastern coastal China between latitudes 30° 45′ N and 35° 20′ N and longitudes 116° 18′ E and 121° 57′ E (Fig. 1), This region has an eastern Asian monsoon climate. The mean annual temperature of the province is approximately 13.6–16.1 °C and the annual precipitation is about 1,000 mm. The main terrain of the province is plains, which account for 85% of the study area. Hilly land accounts for 15% of the area, with elevation ranging from 0 to 625 m. Due to natural conditions favorable for agriculture, Jiangsu Province has become one of China’s most important grain producing areas.
Jiangsu Province had a population of 73.27 million in 2000 and 79.76 million in 2015 (Statistics Bureau of Jiangsu Province, https://tj.jiangsu.gov.cn/), with an annual growth rate of 0.53%. Since the China’s policy of reform and opening to the world starting in 1978, Jiangsu Province has experienced a variety of social-economic changes. Since 2000, the total GDP of Jiangsu has always ranked in 2nd place of all provinces in China. The urbanization rate of Jiangsu has also continuously ranked above the national average, reaching 60.6% in 2010 and 66.5% in 2015. The degree of agricultural modernization is also one of the highest amongst the provinces of China.
Mapping Human footprint
We followed the concept of “human footprint”14 to map human pressure of Jiangsu Province for several years. Five variables measuring the direct and indirect human pressures on environment of Jiangsu Province were collected (Table 3). According to the data availability of these variables (especially the road data) (Table 3), human footprint in 2000, 2010 and 2015 were produced. The five variables were weighted to values of 0–10 (0 for lowest human pressure and 10 for highest human pressure) according to estimates of their relative levels of human pressure following Sanderson et al. (2002) and Oscar Venter et al. (2016). Most of the resolution for the original data is 1 km. We resampled the variables with resolution of not 1 km to be 1 km using ArcGIS 10.2. Finally, all the pressures were summed together to create the standardized human footprint index.
Population density
The number of people in an area is frequently cited as a primary underlying cause of human pressure28. Human population density used in this study was the Gridded Population of the World, Version 4 (GPWv4) data sets29. This data was released every 5 years since 2000. For representing the impact of human activities, pixels with the original human population density value of 0 was assigned to a score of 0. The areas with other population density values were grouped into 10 bins, and then values in each bin were coded from 1–10 successively. To make the population density of the 3 years comparable, the data of 2000 was grouped into 10 bins using the quantile classification method in ArcGIS 10.2, and the thresholds of the ten bins were used as standards to convert the population density data of 2010 and 2015. The same approach for assigning scores is taken for other continues variable, such as night-lights time and road impact.
Land use/cover
Human beings transform land for settlements, growing food, and producing other economic goods30,31. Different land uses differ in the extent to which they modify ecosystem processes32,33,34. The land use data was obtained from the China’s land use database developed by Environmental Sciences Chinese Academy of Sciences (RESDC). This data was released for every 5 years since 1990, and is reported as the most accurate land use remote sensing monitoring data product in China35. The original data was at a resolution of 1 km with 6 primary classes and 26 secondary classes. According to Sanderson et al. (2002), all the areas mapped as urban, rural settlement and industrial transportation construction land in the original dataset were given a highest score of 10. All areas mapped as cropland, garden land, farming ponds and reservoirs were assigned a score of 7, areas of all other land use types were assigned to 0.
Night-time lights
DMSP/OLS (Defense Meteorological Satellite Program-Operational Linescan System) sensors work at night to detect the gleam visible-near infrared (VNIR) radiance on the earth surface, and it can collect the night lights with intensity degree from the urban lights and even small-scale residential areas, traffic, etc. This data is a good complement to capturing a lot of flowing and unobtrusive human activities36. We downloaded the DMSP-OLS data with the original resolution of 30 arc-seconds on the RESDC for the year of 2000, 2010 and 2013. The digital numbers (DN) of night-time lights were assigned with scores by the method for the population density data.
Roads
As one of humanity’s most prolific linear infrastructures, beyond simply reducing the extent of suitable habitat, roads can act as population sinks for many traffic-induced activities and shrink the distance of human from nature37,38. It can be known that the closer distance from a location to a road, the greater impact of human activities on the environment of this location, furthermore, the difference of impact with distance varies among the road types. For example, the impact of a railway for a location would be larger than that of a country road when the distance is the same. Therefore, we calculated the road impact with considering both distance and road type.
First, we calculated the shortest distance of each pixel to every type of road using Path Distance tools of ArcGIS 10.2. The maximum distance in terms of the impact of roads was set to 15-km according to the study of Sanderson. This means when roads all beyond 15 km away from one pixel, this pixel would be assigned a score of 0. To measure the total road impact pressure of one pixel, we calculated the weighted sum of all the minimum distance of each road type following the formula is:
$$RTD = mathop sum limits_{i = 1}^{6} D_{i} *M_{i}$$
where RTD represent the total impact distance of roads for one pixel, the smaller the value, the greater the impact of roads. MI is the adjusted coefficient for representing the impact of different road type with distance (Expressway: 0.2, Railway: 0.37, National road: 0.53, Provincial road: 0.8, Country road: 0.87, Other road: 1), which was determined according to the study of Li et al.20. Di is the shortest path distance of the pixel to each road type calculated using ArcGIS 10.2.
Driving analyze
Driver factors
We selected five natural and four anthropogenic factors impacting the spatial and temporal human footprint index in Jiangsu Province. The five natural variables are elevation, slope, mean annual temperature, annual precipitation and distance from water. Four anthropogenic drivers, industrialization level, industrial structure optimization39,40, rural per capita disposable income and urban per capita disposable income were selected for representing the social development level. The elevation and slope are extracted from the digital elevation model which is downloaded from Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (https://www.gscloud.cn). Mean annual temperature and annual precipitation for 2000 and 2015 were downloaded from the Data Center for Resources and Environmental Sciences Chinese Academy of Sciences (RESDC) (https://www.resdc.cn), the distance from water of a pixel was calculated by the Path Distance tool in ArcGIS 10.2. We collected the rural and urban per capita disposable income and value- added of primary, secondary and tertiary industries of the 55 counties of Jiangsu Province in 2000, 2010 and 2015 from the Statistical Yearbook of Jiangsu Province. The industrialization level is calculated as the value-added of secondary industry divided by the value-added of primary industry. The industrial structure optimization is calculated as the value-added of tertiary industry divided by the value-added of secondary industry. These anthropogenic indices are independent of the human footprint calculated above for that these human pressure data were absolutely not used these four statistical data.
Generalized additive model (GAM)
GAM41 is an extension of generalized linear model (GLM) in which the linear predictor is given by a sum of smooth functions of independent variables42,43. The linear predictor of a GAM has a structure as follows,
$${text{g}}left( {mu_{i} } right) = theta + mathop sum limits_{i = 1}^{n} f_{{ileft( {x_{i} } right)}}$$
where the response variables yi has an expectation of mui and g is a known monotonic ‘link’ function, θ is a constant, fi is a smoothing function that describes the relationship between g(mui) and the independent variables xi, and n is the number of variables.
GAM is flexible relative to strictly parametric linear or non-linear models for discerning effects of multiple factors44. GAM has been widely applied in detecting drivers and predicting spatial distributions of geographic elements/phenomena45,46. We thus chose GAM to detect and compare the driving effects of natural and anthropogenic driving factors for human footprint and its change in Jiangsu Province.
Three pools of drivers were developed, including natural drivers, anthropogenic drivers, natural and anthropogenic drivers. We first constructed GAM models for human footprint in 2000, 2015 and change of human footprint using all variables in each pool to compare the explanation effects of different drivers. The construction of a GAM model was mainly guided by generalized cross-validation (GCV), i.e. lower GCV values indicate better-fitted models. Besides, adjustment R square (R-sq. (adj)) was generated to evaluated the fitted models. The deviance explained (DE) was generated to examine the explanation ability of the sum effects of driving variables, where a higher DE value represents a better explanatory ability. Then, we constructed adjusted GAM models only using those significant variables within each pool with a backward stepwise method. We employed the p-scores to evaluate the significance of each driver and selected the significant drivers with a p < 0.1. We also plotted smooth functions of each significant drivers to examine their effect for explaining human footprint in each year and the change of human footprint from 2000 to 2015. The vertical axis in the plots is a relative scale indicating the effect of that explanatory variable on the dependent variable.
The mean value of every natural drivers and human footprint in the 55 counties of Jiangsu Province were calculated as the covariates xi and response variables yi, respectively. We constructed GAMs with the mgcv package in the R software. The univariate penalized cubic regression spline smooth function and an identity link function were used for fi and g in this study.
Source: Ecology - nature.com
