utilization efficiency models based on remote sensing data opened a new phase in .... vegetation models, which are Miami model [14], Montreal model [15], and ...
Estimating Net Primary Productivity of Terrestrial Vegetation Based on Remote Sensing: A Case Study in Inner Mongolia, China Wenquan Zhu1, Yaozhong Pan 1†, Haibo Hu 1, Jing Li 1, and Peng Gong 2 1
College of Resources Science and Technology, Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing, 100875, P. R. of China. †Corresponding author 2 Department of Environmental Science, Policy and Management, and Center for Assessment and Monitoring of Forest and Environmental Resources, University of California at Berkeley, CA 94720, USA estimation model of NPP, based on geographic information system (GIS) and remote sensing (RS) technology, was presented. Application and validation of this model in Inner Mongolia, China, was also conducted.
Abstract—Some vegetation primary production models have been developed in recent years as research issues related to food security and biotic response to climate warming have become more compelling. An estimation model of net primary productivity (NPP), based on geographic information system (GIS) and remote sensing (RS) technology, is presented. The model, driven with ground meteorological data and remote sensing data, moves beyond simple correlative models to a more mechanistic basis and avoids the need for a full suite of ecophysiological process algorithms that require explicit parameterization. Therefore, it is relatively easier to acquire data. Application and validation of this model in Inner Mongolia, China, was conducted. After the validation with observed data and the comparison with other NPP models, the results showed that the predicted NPP was in good agreement with field measurement, and the remote sensing method can more actually reflect the forest NPP than Chikugo model. These results illustrated the utility of the model for terrestrial primary production over regional scales.
II.
A. Study site Inner Mongolia (latitude 37°01'N to 52°57'N and longitude 98°02'E to 126°32'E) is located in the northern part of China (Fig. 1). The average elevation is about 1000 m above sea level. The total area is 1,183,000 km2. From the northeast to northwest, with the decreasing of precipitation and the increasing of temperature, boreal and northern temperate forests, meadows, grasslands, bare rocks and deserts are distributed. The Inner Mongolia grassland is a typical kind of semi-aridity temperate grassland ecosystem in the middle latitude. It is located in the Northeast China Transect (NECT) of the terrestrial transect for global change study in the International Geosphere-Biosphere Programme (IGBP). The climate of Inner Mongolia features a temperate continental monsoon climate with annual precipitation of 100–450 mm and annual mean temperature 0–8 oC.
Keywords—geographic information system; remote sensing; primary production; Inner Mongolia
I.
INTRODUCTION
Accurate estimate of Net primary productivity (NPP) is critical to understand the carbon dynamics within the atmosphere-vegetation-soil continuum and the response of terrestrial ecosystem to future climate warming [1]. Model simulation is commonly used to estimate regional and global NPP given the difficulties to directly measure NPP at such spatial scales [2]. These models range in complexity from statistical climate-correlation models to mechanistic ecophysiological models [3]. Typically, such models operate on point measurements that are extrapolated spatially. Spatial scaling of point measurements to the landscape or regional scale is problematic owing to great landscape heterogeneity relative to sampling density [4]. With the increasing availability of remote sensing measurements that provide the complete global coverage with a high revisit frequency, light utilization efficiency models based on remote sensing data opened a new phase in the study of NPP, and they made the global and regional NPP estimation possible. In this paper, an
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DATA AND METHODS
B. Model description For a given area, the amount of photosynthetically active radiation absorbed annually by green vegetation (APAR) multiplied by the efficiency by which that radiation is converted to plant biomass increment (ε) equals the NPP.
NPP = APAR × ε
(1)
1) APAR: APAR is calculated at each monthly time step. It is the product of PAR surface irradiance and the fraction of photosynthetically active radiation (FPAR). PAR surface irradiance is calculated as 1/2 the total solar surface irradiance (SSI). APAR is represented as:
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APAR = SSI × FPAR × 0.5
(2)
topographic map. The verification result showed that the registration error was less than 1 pixel. The residual error of NDVI was corrected with the assumption that the NDVI in deserts was zero [10].
where FPAR is a linear function of the simple ratio vegetation index (SRVI) [5]:
SRVI − SRVI min ,0.95 FPAR = min SRVI max − SRVI min
2) Meteorological data: Daily meteorological data, derived from 84 meteorological stations in the study area in the same period as MODIS images, included total daily precipitation, mean daily temperature, and total daily solar irradiance. All these data were validated with the missing and suspicious data eliminated. The data were then interpolated at the same scale with remote sensing data using Kringing method.
(3)
where SRVImin is a constant of 1.08 in the study site, SRVImax is somewhat different for different vegetation types, and its value varies between 4.14 and 6.17. SRVI is a function of normalized difference vegetation index (NDVI), and it can be defined as:
SRVI =
1 + NDVI 1 − NDVI
3) Vegetation map: The vegetation map of China with a scale of 1: 1 000 000 was provided by Institute of Botany, Chinese Academy of Sciences [11].
( 4)
III.
2) Light utilization efficiency (ε): ε for each grid is the product of the maximum light utilization efficiency (εmax), estimated by the BIOME-BGC model with different vegetation types [6], and scales representing the availability of water (W) and the suitability of temperature (T1, T2) [5]:
ε = T1 × T2 × W × ε max
A. Regional application With the data sets and functions described above, the NPP and its distribution in Inner Mongolia was estimated (Fig. 2). The gradient distribution of NPP in Inner Mongolia was very distinct because of the different constraints from water and temperature. It showed an increasing trend from the southwest to northeast of Inner Mongolia, which was consistent with the zonal distribution of vegetation [12], [13]. The higher NPP value was found in the northeastern Inner Mongolia, which was mainly covered by boreal needle-leaf forests. The annual NPP was above 1000 gC·m-2·yr-1. Much of the semi-aridity temperate grassland was distributed in the central part of Inner Mongolia. With the decreasing precipitation, the NPP deceased to about 500 gC·m-2·yr-1. The lowest value was found in the deserts of western Inner Mongolia. The vegetation coverage was very low and the NPP was below 100 gC·m-2·yr-1.
(5)
The two temperature scalars in this model attempt to capture two aspects of the regulation of plant growth by temperature [5], [7]. The specific functions of T1 and T2 are expressed in [7]. The water scalar is calculated on a monthly time step as a function of the ratio of estimated evapotranspiration (EET) to potential evapotranspiration (PET) [7]:
W = 0.5 + 0.5 × EET/PET
RESULTS AND ANALYSIS
(6)
where PET is a function of temperature and latitude [8]. EET is a function of precipitation (P) and net solar irradiance (Rn), which is converted to the equivalent evaporation through water specific heat [9]. When EET exceeds PET, NPP is no longer restricted by water, and W equals 1. EET can be expressed as:
Inner Mongolia Autonomous Region
2
EET = P × Rn ×
p2 + Rn + P × Rn
(P + Rn ) × (p2 + Rn 2 )
(7)
C. Data acquisition and processing
Study area
1) TERRA/MODIS data: The MODIS NDVI images were provided by the Global Land Cover Facility (GLCF), University of Maryland. The spatial resolution is 500 m × 500 m. The 32-day composite data were taken from Dec. 2001 to Dec. 2002. These images were rectified to the reference
Meteorological Stations
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0
250
500
1000 km
China
Figure 1. Study site and the distribution of Meteorological stations
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Table 1 lists the modeling results of three climatevegetation models, which are Miami model [14], Montreal model [15], and Chikugo model [16]. Miami model and Montreal model just use a simple relationship between vegetation and climate to estimate NPP and are lack of the vegetation eco-physiological basis. Chikugo model integrates some eco-physiological features and statistic methods to estimate NPP, and the relative error between the estimated results and the field observed data is lower, especially in arid and semi-arid areas. The NPP distribution trend among different vegetation types was consistent between the remote sensing method and the Chikugo model (Table 1). For forests, the estimated NPP values from the remote sensing method were higher than that from Chikugo model. For other vegetation types, such as shrubs, grasslands and farmlands, the estimated NPP values were lower. To some extend, the remote sensing method is better than Chikugo model to estimate NPP, and can more actually reflect the NPP spatial distribution of forests.
B. Validation Global and regional validation of estimated NPP is very difficult due to the heterogeneity among different ecosystems. There are two common methods to validate the estimated NPP. One is to compare the measured value with the field observation data, and the other is to compare with other models. Field observation data from 30 stations were acquired from Institute of Botany, Chinese Academy of Sciences. They were the calculated mean value for a biome based on extensively measured plot data. The estimated results and the observed data showed a very similar distribution (Fig. 3). Somewhat differences between them still existed due to the variations of environment, such as the temperature, precipitation and human effects. It should be noted that most of the estimated values still existed in the fluctuation range of the measured data. This can be reflected from the trend line of the estimated NPP and the standard deviation of the observed data (Fig. 3).
TABLE I.
COMPARISON OF ANNUAL NPP ESTIMATED BY DIFFERENT MODELS Estimated NPP (gC·m-2·yr-1)
Vegetation types Needle-leaf deciduous forest Needle-leaf evergreen forest Broad-leaf deciduous forest Shrub Grassland/meadow Desert 0
250
Water/swamp Farmland
500 km
349–445 445–534 534–630 630–726
726–843 843–1042 1042–1213 1213–1330
Observed NPP (tC·ha-1·yr-1)
15
0 20
-1
Estimated NPP (tC·ha ·yr )
Figure 3. Spatial distribution of NPP in Inner Mongolia
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1035.4
726.2
476.8
900.8
840.4
757.0
513.4
1085.6
1092.0 958.6 1316.7 681.7 1027.7
602.8 687.4 293.9 742.6 715.4
372.2 442.9 94.1 496.1 465.1
235.6 259.9 19.4 369.0 376.8
CONCLUSIONS
•
The spatio-temporal distribution of NPP in Inner Mongolia was analyzed. It was in good agreement with other research results, which illustrated the utility of the model for terrestrial primary production over regional scales.
5
-1
1435.9
After the validation with observed data and the comparison with other NPP models, the results showed that the predicted NPP was consistent with observed values, and the remote sensing method can more actually reflect the forest NPP than Chikugo model.
10
15
515.4
•
2
10
754.8
NPP can be estimated just using ground meteorological data and remote sensing data. The remote sensing method integrates some plant eco-physiological process bases and makes some parameters simple to compute. It is relatively easier to acquire data and its application can be enhanced.
R = 0.84
5
Remote sensing
•
25
0
Chikugo model
An NPP estimation model was presented in this paper based on GIS and RS technology. After the application and validation in Inner Mongolia, China, some results were acquired as follows:
1330–1425 1425–1508 1508–1597 1597–1775
Figure 2. Comparison of estimated NPP and field observed NPP
20
Montreal model
593.9
IV.
Unit: gC·m-2·yr-1 6–68 68–157 157–253 253–349
Miami model
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ACKNOWLEDGMENT This study was funded by the Natural Science Foundation of China (40371001) and the National High Technology Research and Development Program of China (2002AA133060). REFERENCES [1]
[2]
[3]
[4]
[5]
[6]
[7]
[8] [9]
[10]
[11] [12]
[13]
[14] [15]
[16]
J. Liu, J. M. Chen, and W. Chen, “Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data,” J. geophys. Res., vol. 104, No. D22, pp. 27735–27754, 1999. G. A. Alexandrov, T. Oikawa, Y. Yamagata, “The scheme for globalization of a process-based model explaining gradations in terrestrial NPP and its application,” Ecol. Modell., vol. 148, pp. 293– 306, 2002. J. M. Melillo, A. D. McGuire, D. W. Kicklighter, B. Moore III, C. J. Vorosmatry, and A. J. Schloss, “Global climate change and terrestrial net primary production”, Nature, vol. 363, pp. 234–240, 1993. S. J. Goetz, S. D. Prince, S. N. Goward, M. M. Thawley, J. Small, “Satellite remote sensing of primary production: an improved production efficiency modeling approach,” Ecol. Modell., vol. 122, pp. 239–255, 1999. C. S. Potter, J. T. Randerson, C. B. Field, P. A .Matson, P. M. Vitousek, H. A. Mooney, S. A. Klooster, “Terrestrial ecosystem production: a process model based on global satellite and surface data,” Global Biogeochem. Cycles, vol. 7, pp. 811–841, 1993. S. W. Running, P. E. Thornton, R. Nemani and J. M. Glassy, “Global terrestrial gross and net primary productivity from the earth observing system,” in Methods in ecosystem science, O. Sala, R. Jackson and H. Mooney, Eds. New York: Springer Verlag, 2000, pp. 44–57. C. B. Field, J. T. Randerson, C. M. Malmström, “Global net primary production: combining ecology and remote sensing,” Remote Sens. of Environ., vol. 51, pp. 74–88, 1995. C. W. Thornthwaite, “An approach toward a rational classification of climate,” Geogr. Rev., vol. 38, pp. 55–74, 1948. Zhou Guangsheng, Zhang Xinshi, “Study on climate-vegetation classification for global change in China,” Acta Botanica Sinica, vol. 38, No. 1, pp. 8–17, 1996. (In Chinese) R. B. Myneni, C. J. Tucker, G. Asrar, and C. D. Keeling, “Interannual variations in satellite-sensed vegetation index data from 1981 to 1991”, J. geophys. Res., vol. 103, No. D6, pp. 6145–6160, 1998. Hou Xueyu, The vegetation atlas of China (1: 1 000 000), Beijing: Science Press, 2001. (In Chinese) Piao Shilong, Fang Jingyun, Guo Qinghua, “Application of CASA model to the estimation of Chinese terrestrial net primary productivity,” Acta Phytoecologica Sinica, vol. 25, No. 5, pp. 603–608, 2001. (In Chinese) Chen Lijun, Liu Gaohuan, Li Huiguo, “Estimating net primary productivity of terrestrial vegetation in China using remote sensing,” Journal of Remote Sensing, vol. 6, No. 2, pp. 129–135, 2002. (In Chinese) H. Lieth, “Modeling the productivity of the world,” Nature & Resources, vol. 8, No. 2, pp. 5–10, 1972. H. Lieth and E. Box, “Evapotranspiration and primary productivity,” in Memorial Model, Publications in Climatology, W. Thornthwaite, Eds. New Jersey: C. W. Thornthwaite Associates, 1972, pp. 37–46. Z. Uchijima, “Agroclimate evaluation of net primary productivity of natural vegetation (1) Chikugo model for evaluating net primary productivity,” J. Agr. Meteorol., vol. 40, pp. 343–352, 1985.
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