Since the late 1978s, China has experienced one of the highest tourism growth rates in the world, which in turn has driven extensive land-use and land-cover change. The aim of this research is to develop a sensor nodes positioning strategy for detecting land use related dynamics of vegetation carbon stocks of Wulong world natural heritage. Based on the assessment of road networks’ influences on biomass carbon stocks, roadside biomass carbon stocks risk index was proposed as a sensor deployment strategy to identify the optimal positions of the sensors to detect the changes in vegetation carbon stocks. Forest and cropland around the lower levels of roads should be the most important region of sensor nodes deployment strategy. The results generated from this study have the ability to achieve optimal solution of spatial positioning problem with minimum number of sensors in biomass carbon monitoring sensor networks. This analysis appears to have great potential for a wide range of practical applications in tourism industry in China.
There is now a wide recognition of the urgent need for international governments and industries to reduce and mitigate carbon emissions [
Road networks are recognized as the most pervasive vectors to landscape change [
Efficiently monitoring land use and cover change is a fundamental part of accurately quantifying the fluxes of carbon to the atmosphere. Sensor networks offer a promise as a tool for remotely gathering real-time data on important carbon dynamics parameters. In all cases, the deployment of sensor nodes affects the effectiveness of vegetation carbon stocks monitoring networks, communication cost, and resource management [
In this paper, we present roadside biomass carbon stocks risk index as a sensor deployment strategy to identify the optimal positions of the sensors to detect the changes in vegetation carbon stocks. We also present describing how a tourism destination’s biomass carbon stocks can be influenced by the expansion of road network. This work has the ability to achieve optimal solution of spatial positioning problem with minimum number of sensors in biomass carbon monitoring sensor networks. This analysis appears to have great potential for a wide range of practical applications in tourism industry in China.
The study was conducted in Wulong County, Chongqing Municipality, China, one of the most famous tourist destinations of China. The study area is located between latitude 29°02′ N and 29°40′ N and longitude 117°13′ E and 108°05′ E with an area of 2,901 km2 and with a population of 400,000 (Figure
The spatial distribution of study area and roads.
Giant dolines (sinkholes), three Natural Bridges, and Furong Cave of Wulong County represent three of the world’s most spectacular examples of humid tropical to subtropical Karst landscapes. It is a part of the South China Karst, a UNESCO World Heritage Site. The unique geological formation and rich cultural heritage of the county prove huge draw for tourists. According to the Wulong Bureau of Statistics, the total tourist arrivals in 2012 were 13 million. The development of tourism industry may result in extensive land-use and land-cover change.
To retrieve the land-cover types, Landsat TM images were chosen and radiantly corrected. The images were false-color composed of five, four, and three bands using the red-green-blue (RGB) method of artificial visual interpretation. There were six aggregated classes of land use: cropland, forest, grassland, water bodies, built-up land, and bare land. These classes were further divided into 25 land-use classes. The built-up land contains urban land, rural residential land, and industrial and mining sites. The average interpretation accuracies were 92.9% for land use and 97.6% for the detection of changes in land cover. For cropland, the accuracy was 94.9%. The built-up area had the highest accuracy of 96.3%. For forest and grassland, the accuracies were 90.1% and 88.1%, respectively [
In addition, road dataset from the road map of 1 : 250000 (Figure
The characteristic of different levels of roads in Wulong County.
The levels of roads | Function | Average daily traffic volume |
---|---|---|
Road-1 | The arterial highway connecting the important political centers and economic centers | 10000~25000 |
Road-2 | The arterial highway connecting the political centers and economic centers | 2000~10000 |
Road-3 | The feeder road connecting counties | 200~2000 |
Road-4 | The feeder road connecting counties or villages | 100~200 |
To calculate the fractal dimension value (
After taking logarithm on both sides of the equation, the above formula could be transformed into the simple formula calculating the fractal dimension value of land-use type. Consider
The formula indicates the relationship between
Therefore, the stability index (
Road constructions can bring severe disturbances to land-use patches, further affecting biomass carbon stocks [
The converted area of land-use types between 1980 and 2005 were calculated by GIS overlay analysis in the paper. Biomass carbon densities of different land-use types were obtained by literature statistics (Table
The biomass carbon density.
Land use | Carbon density (kg·m−2) | References |
---|---|---|
Cropland | 0.81 | Fang et al., 2007 [ |
Forest | 5.41 | Wang et al., 1999 [ |
Grass | 0.42 | Cheng et al., 2012 [ |
Roads may affect the stability of land-use patches; thereby biomass carbon stocks were also disturbed. Therefore, it is necessary to construct roadside biomass carbon stocks risk monitoring networks from the following two perspectives: the structure of biomass carbon stocks risk and the process of biomass carbon stocks risk, respectively.
Landscape pattern and its change are comprehensive reflections of the regional ecological environment system generated by nature and human behavior. Fractal dimension (
To quantitatively calculate the relationship between the construction of roads and the biomass carbon stocks, carbon density index was applied in the paper. The carbon density index (
Landscape pattern can represent the process of ecosystem change. To ecological risk, the process of ecosystem change shows the responses of different landscape types to outside disturbances. Different landscape types play different poles in maintaining and improving overall ecological structure and function and promoting natural evolution of ecological system. Meanwhile, they have different abilities to resist outside disturbances. Their subsequent ecological effects are obviously different because of different degrees and patterns of damage in different landscape regions. Ecological vulnerability index was used to reflect the impact of the construction of road on the process of biomass carbon stocks risk.
Based on the impact assessment of roads to different land-use types, the vulnerability values were given as urban land, cropland, grass, forest, water body, and rural residential area to 6, 5, 4, 3, 2, and 1. We finally calculated the ecological vulnerability index (
Finally, we calculated the roadside biomass carbon stocks risk index (
The size of fishnet was
The flow chart of sensor nodes deployment strategy for monitoring roadside biomass carbon stocks.
The impact of roads on land use stability was quantitatively calculated through overlay analysis and buffer analysis based on ArcGIS platform. Within the 1 km buffers of the four levels of roads, the stability indices were 0.04, 0.12, 0.17, and 0.17, respectively. Obviously the higher level of roads had the more serious impact on land use stability.
There were distinct differences for the impacts of different levels of roads on the various land-use types (Tables
The impact of Road-1 on land use stability.
Land use | The regression formula between area and perimeter |
|
|
|
---|---|---|---|---|
Cropland |
|
0.94 | 1.47 | 0.03 |
Forest |
|
0.94 | 1.44 | 0.06 |
Grass |
|
0.92 | 1.71 | 0.21 |
Water body | ||||
Urban land |
|
0.94 | 1.23 | 0.27 |
Rural residential area |
|
0.99 | 0.82 | 0.68 |
All land-use patches |
|
0.93 | 1.46 | 0.04 |
The impact of Road-2 on land-use stability.
Land use | The regression formula between area and perimeter |
|
|
|
---|---|---|---|---|
Cropland |
|
0.93 | 1.40 | 0.10 |
Forest |
|
0.96 | 1.43 | 0.07 |
Grass |
|
0.92 | 1.18 | 0.32 |
Water body | ||||
Urban land | ||||
Rural residential area | ||||
All land-use patches |
|
0.94 | 1.38 | 0.12 |
The impact of Road-3 on land-use stability.
Land use | The regression formula between area and perimeter |
|
|
|
---|---|---|---|---|
Cropland |
|
0.92 | 1.34 | 0.16 |
Forest |
|
0.94 | 1.28 | 0.22 |
Grass |
|
0.94 | 1.38 | 0.12 |
Water body |
|
0.97 | 1.16 | 0.34 |
Urban land |
|
0.79 | 1.50 | 0.00 |
Rural residential area |
|
0.99 | 0.89 | 0.61 |
All land-use patches |
|
0.93 | 1.33 | 0.17 |
The impact of Road-4 on land-use stability.
Land use | The regression formula between area and perimeter |
|
|
|
---|---|---|---|---|
Cropland |
|
0.92 | 1.37 | 0.13 |
Forest |
|
0.94 | 1.29 | 0.21 |
Grass |
|
0.94 | 1.25 | 0.25 |
Water body |
|
0.98 | 1.12 | 0.38 |
Urban land |
|
0.65 | 1.59 | 0.09 |
Rural residential area |
|
0.95 | 0.98 | 0.52 |
All land-use patches |
|
0.93 | 1.33 | 0.17 |
The total area of land-use change was 121.67 km2 between 1980 and 2005 (Table
Roadside biomass carbon stocks changes (area: km2; carbon stocks changes:
Land-use change | Road-1 | Road-2 | Road-3 | Road-4 | All roads | The study area | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Area | Carbon stocks changes | Area | Carbon stocks changes | Area | Carbon stocks changes | Area | Carbon stocks changes | Area | Carbon stocks changes | Area | Carbon stocks changes | |
Cropland→ forest | 1.51 | 6.94 | 0.00 | 0.00 | 7.53 | 34.62 | 14.28 | 65.67 | 18.66 | 85.82 | 27.20 | 125.10 |
Cropland→ grass | 0.13 | −0.05 | 0.00 | 0.00 | 0.45 | −0.18 | 2.16 | −0.84 | 2.47 | −0.96 | 3.07 | −1.20 |
Cropland→ water body | 0.00 | 0.00 | 0.00 | 0.00 | 1.44 | −1.16 | 2.31 | −1.87 | 2.53 | −2.05 | 2.76 | −2.23 |
Cropland→ urban land | 2.79 | −2.26 | 0.00 | 0.00 | 2.38 | −1.93 | 0.72 | −0.59 | 2.97 | −2.41 | 2.97 | −2.41 |
Cropland→ rural residential area | 0.00 | 0.00 | 0.00 | 0.00 | 0.16 | −0.13 | 0.07 | −0.06 | 0.16 | −0.13 | 0.16 | −0.13 |
Forest→ cropland | 0.00 | 0.00 | 0.00 | 0.00 | 0.08 | −0.35 | 0.30 | −1.39 | 0.33 | −1.51 | 1.51 | −6.97 |
Forest→ grass | 0.00 | 0.00 | 0.00 | 0.00 | 0.63 | −3.16 | 1.96 | −9.78 | 2.01 | −10.01 | 2.58 | −12.86 |
Forest→ water body | 0.00 | 0.00 | 0.00 | 0.00 | 1.30 | −7.06 | 3.81 | −20.62 | 4.36 | −23.59 | 5.99 | −32.42 |
Forest→ urban land | 0.46 | −2.46 | 0.00 | 0.00 | 0.13 | −0.72 | 0.44 | −2.37 | 0.58 | −3.16 | 0.58 | −3.16 |
Grass→ cropland | 0.45 | 0.18 | 0.73 | 0.29 | 1.56 | 0.61 | 2.05 | 0.80 | 3.04 | 1.18 | 3.94 | 1.54 |
Grass→ forest | 2.52 | 12.56 | 0.00 | 0.00 | 22.32 | 111.38 | 36.97 | 184.46 | 48.31 | 241.07 | 69.72 | 347.93 |
Grass→ urban land | 0.25 | −0.10 | 0.00 | 0.00 | 0.41 | −0.17 | 0.75 | −0.31 | 1.16 | −0.49 | 1.16 | −0.49 |
Grass→ rural residential area | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | −0.01 | 0.00 | 0.00 | 0.01 | −0.01 | 0.01 | −0.01 |
|
||||||||||||
Total | 8.10 | 14.80 | 0.73 | 0.29 | 38.42 | 131.73 | 65.81 | 213.08 | 86.60 | 283.75 | 121.67 | 412.69 |
The total biomass carbon stocks were
The roads had strong influences on land-use change. The converted area was 86.6 km2 in the 1 km buffer of roads, accounting for 71.18% of the total converted area during the period of 1980~2005. Consequently, biomass carbon stocks increased by
There were different changes of biomass carbon stocks in the 1 km buffers of different levels of roads. Biomass carbon stocks increased by
Forest was the most obvious land-use type for biomass carbon stocks change. The losses of biomass carbon stocks resulting from the area converting from forest to other land-use types were
Roadside biomass carbon stocks risk was divided into five levels, slight, light, medium, heavy, and extreme (Figure
The spatial pattern of roadside biomass carbon stocks risk regions.
The areas of different risk regions in the influence domain of the four levels of roads were calculated (Figure
Roadside biomass carbon stocks risk for different levels of roads.
The areas of different land-use types in all risk regions were calculated (Figure
Roadside biomass carbon stocks risk for different land use types.
This paper developed a sensor nodes positioning strategy for detecting land use related dynamics of biomass carbon stocks of a tourism destination. Roadside biomass carbon stocks risk index was proposed based on the assessment of impact of road networks on land-use stability and biomass carbon stocks changes. Roadside biomass carbon stocks risk monitoring networks could be established through the assessment of biomass carbon stocks risk taking into account the structure of biomass carbon stocks risk and the process of biomass carbon stocks risk.
The higher level of roads had the more serious impact on land use stability. There was more vigorous influence on biomass carbon stocks resulting from urban land change in the buffer of higher level of road. Meanwhile, biomass carbon stocks changed more distinctly under the influences of the lower level of roads. The lower level of roads was the high risk areas threatening biomass carbon stocks. Forest and cropland mainly were the most important components of the high risk areas. In conclusion, forest and cropland around the lower levels of roads should be the important region of sensor nodes deployment strategy. Roadside biomass carbon stocks risk monitoring networks may thus be an effective approach to protect the biomass carbon stocks in tourism industry in China.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This study was supported by the National Natural Science Foundation of China (NSFC, no. 41101115), China Postdoctoral Science Foundation (no. 2011M500376), and Chongqing City Board of Education Science and Technology Project (no. KJ100603).