Classification of Grassland Types by MODIS Time ... - IEEE Xplore

404

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 3, NO. 3, SEPTEMBER 2010

Classification of Grassland Types by MODIS Time-Series Images in Tibet, China Qingke Wen, Zengxiang Zhang, Shuo Liu, Xiao Wang, and Chen Wang

Abstract—Tibet is one of the five largest pasturing regions of China. Grassland classification is significant for its utilization and protection, but few correlative studies have been done in Tibet due to its rugged natural conditions, which make it difficult and timeconsuming to conduct extensive field measurements. The remotesensing technique is helpful for grassland classification in such regions. In this study, high temporal resolution of a moderate resolution imaging spectroradiometer (MODIS) is used to construct temporal profiles of enhanced vegetation index (EVI) during the grass growth period in Tibet. By dividing the large study area into individual regions based on altitude and latitude, we classified the grasslands of Tibet into six types—meadow steppe, typical steppe, desert steppe, alpine meadow steppe, alpine typical steppe, and shrub herbosa. Based on the 1:500 000 scale maps of China’s grassland resources, the validation process indicates an overall accuracy of 68.02%, and a Kappa coefficient of 0.52. Land managers are provided with maps and area values of each grassland type in Tibet in 2005. In addition, regional average EVI reflect the relative biomass of each types of the grassland, which will provide evidences for coordinating plans for grassland developing. MODIS_EVI provides a simple and rapid method to classify the grassland in regions that are hard to reach, which offers an effective means of investigating biological resources on a large scale. Index Terms—Agriculture, geographic information systems, remote sensing.

I. INTRODUCTION

P

ERMANENT grasslands cover approximately 26% of the land surface of the earth and contribute towards a significant proportion of the diet for domestic and wild animals [1]. To promote stockbreeding, the grassland biomass needs to be estimated along with an evaluation of grassland degradation and ecological situation. Such studies need to be conducted on different types of rangelands, which play a significant role in stockbreeding. The rapid development of the Tibetan economy offers both an opportunity and challenge to stockbreeding in the region. To ensure the management of grassland resources, it is necessary to monitor grassland types precisely. However, the rugged natural conditions, as well as the high altitude, make Manuscript received February 05, 2010; accepted March 31, 2010. Date of publication June 01, 2010; date of current version August 25, 2010. This work was supported in part by the Basic Condition Platform Construction Project of the National Science and Technology-Sharing Network of Earth System Science and by the National Key Project of Scientific and Technical Supporting Programs under Contract 2006BAC08B0405. Q. Wen, Z. Zhang, X. Wang, and C. Wang are with the Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China (e-mail: Wen_qk@ irsa.ac.cn). S. Liu is with the Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTARS.2010.2049001

it difficult and time-consuming to conduct extensive field measurements. In Tibet, remote-sensing-based studies provide an alternative way to classify grassland types, which does not need to be on the site. Many surveys for the classification of grasslands have been conducted in China using remote sensing images [2]–[9], with most of them focusing on Inner Mongolia. However, little work was conducted in Tibet where grasslands account for about 21% of the total area of natural grasslands in China. One existing study interpreted the grassland in Tibet based on Landsat TM data and classified grasslands according to its vegetation fraction, but not types of grassland. In this study, the time series from moderate resolution imaging spectroradiometer Enhanced Vegetation Index (MODIS_EVI) data was used to classify grassland types in Tibet. MODIS is a moderate resolution sensor. The highest spatial resolution is 250 m for red and near-infrared (NIR) bands. Its application to grassland classification based on single phase MODIS images is not satisfactory [10]. Therefore, high temporal resolution of MODIS was utilized to construct temporal growth profiles of each type of grassland. To a certain extent, MODIS EVI uses integrated Atmosphere Resistant Vegetation Index (ARVI) and Soil Adjusted Vegetation Index (SAVI) [11]. It reduces the influence of soil background and atmospheric attenuation [11]. Based on the features of the temporal profiles of MODIS_EVI, grassland types of Tibet Autonomous Region are classified into six types. This paper proposes a more economical and faster approach to grassland classification and also provides an efficient approach of updating grassland maps. II. STUDY AREA The geographic location of Tibet is shown in Fig. 1. The Tibetan Plateau, dubbed the “Third Pole” of the earth, has an average elevation of more than 4000 m a.s.l., which is above almost half way up the troposphere [12], [13]. Tibet is in the Southwest of the Tibetan Plateau , with an area of 120.223 km , almost one-eighth of the area of China. The topography of this region slopes from the northwest to southeast. Therefore, the natural environment changes significantly from southeast to northwest. The climate in the northwest is cold, with mean C. On the contrary, the cliannual temperature around mate in the southeast is warm and humid, with mean annual temperature around 10 C. Mean annual precipitation, which ranges between 74.8–901.5 mm per year, occurs mainly between June and September [14]. The Southern Tibetan valley is dry and hot. The Tibetan Plateau is one of the major rangeland ecosystems and pasture areas on earth. The Tibetan Plateau in

1939-1404/$26.00 © 2010 IEEE

WEN et al.: CLASSIFICATION OF GRASSLAND TYPES BY MODIS TIME-SERIES IMAGES IN TIBET, CHINA

405

TABLE I CLASSIFICATION SYSTEM OF THE GRASSLAND IN TIBET

Fig. 1. Tibetan autonomous region in southwest China shown by MODIS image on June 26th, 2005.

DEM grid images (100 m), 1:250 000, are used to classify the different regions. IV. METHODS

China is a home for 2 million Tibetan herdsmen, apart from 3 million agro-herdsmen supporting a livestock population of 10 million yaks and 30 million sheep and goats [15], [16]. The region can be divided into the northern Tibetan plateau, the southern Tibetan valley, the eastern Tibetan canyon, and the Himalayan Mountains. A colored composite MODIS image from June 2005 is used to depict land cover conditions for Tibet. The bands used to construct it are the NIR, R, and G bands. III. DATA SOURCE A. Remote-Sensing Data Source MODIS images were provided by Land Process Distributed Active Archive Center (LP DAAC). The data used included EVI images from MODIS collection 4, collected based on the quality of the data, with an interval of 16 days. The resolution is 250 m. The enhanced vegetation index is defined as , where represents reflection value after atmospheric correction (NIR, red, blue), is the soil-adjusted factor, and and equal 6.0 and 7.5, respectively. The normalized difference vegetation index (NDVI), which is more widely used, is chlorophyll-sensitive, while the EVI is more sensitive to canopy structural variations, including leaf area index (LAI), canopy type, plant physiognomy, and canopy architecture [17]. In this study, 23 EVI images during the entire year of 2005 were processed for classification. B. Ancillary Data The land use map in 2005 is obtained by visual interpretation on Landsat-TM (30-m resolution) and field observations. The grassland region in this map forms the base map of grassland classification. The atlas of grassland resources of China, 1:500 000, updated in the beginning of this century, is used to validate the accuracy of the classification method. The notebook of the atlas of grassland resources in China, 1:1 000 000 [18] in scale, provided the basic knowledge of the grasslands. DEM

A. Classification System The classification system used in this paper is based on the concept of land cover in [19]. The characteristics of each grassland type are shown in Table I. B. Pre-Processing MODIS EVI land data products are in Hierarchical Data Format—Earth Observing System (HDF-EOS). These products are referenced to a global tiling system in which each tile is approximately 10 latitude 10 longitude and nonoverlapping. MODIS Reprojection Tool (MRT) is used to convert MODIS products into GeoTIFF. At the same time, a complete mosaic of the Tibet portions of the product in the Albers projection, which is the standard projection format of the whole study, was constructed. The parameters used are as follows: smajor 6378245, sminor 6356863.0188, stdpr1 25, stdpr2 47, and center 105. The average J-M distance was calculated for the 23 EVI images of the entire year so as to select the optimal and most effective images. The test result showed that the average J-M distance of 11 images in the growth period reaches up to 1.5119 for the classification of grassland types, and its increase is relatively small when adding other images. This is because there is little difference in grassland types in the winter images. To reduce information redundancy, the study used 11 images during the growth period from April to October 2005 to construct the EVI time-series images. Quality assurance (QA) flags accompanying the MODIS_EVI were ignored, since only the relative values of each type were compared in the analysis. This will decrease the quality in an extensive district. Eleven MODIS_EVI images during the growth period from April to October 2005 were stacked to prepare time-series data for time-series analysis and classification. The 1:250 000 scale maps of DEM was sampled to 250 m in resolution, which is the same as MODIS_EVI data. The classification used in Atlas of grassland resource of China was transformed to the classification system of this study. Grassland types in Tibet were selected under the environment of ArcGIS9.0.

406

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 3, NO. 3, SEPTEMBER 2010

Fig. 2. EVI temporal profiles during the growth period of each grassland type in Tibet before and after HANTS processing.

C. MODIS-EVI Denoise by Harmonic Analysis of Time Series Based on the quality of the data with the interval of 16 days, while cloud noise was removed to a certain extent, partial noise still remained. Harmonic analysis of the time-series (HANTS) algorithm [20] was used to reduce noise further. The HANTS algorithm performs two tasks: 1) screening and removal of cloud affected observations and 2) temporal interpolation of the remaining observations to reconstruct gapless images at a prescribed time. After the harmonic analysis, the time-series images showed natural phenomena of the plants more clearly [21]. Fig. 2 depicts the EVI temporal profiles before and after harmonic analysis. The curves indicate different types of grasslands before and after harmonic analysis. Through harmonic analysis, grassland temporal profiles of each type became smoother. Steep data caused by noise such as cloud noise was corrected. The curve shape was closer to the natural rule. At the same time, the feature points of the original images can be maintained, such as the date of maximum EVI, the date of minimum EVI, EVI fast rising data, and EVI fast declining data. This makes the selection of classification features more efficient [22]. D. Classification The land use map of 2005 which was obtained by visual interpretation of Landsat-TM (30 m resolution) is the basic grassland region for grassland secondary-type classification. First, EVI time-series data were classified using the IsoData method. Taking into consideration that the classification of the region involves many other surface features besides grasslands, cluster types were set to 50, which is eight times larger than the target types 6, to ensure that six types of grassland are sufficiently classified. The area of Tibet is large, and its natural features vary significantly over the region. Three main natural factors effecting the distribution of grassland are elevation, temperature, and precipitation. In this region, alpine grass is mainly present at altitudes over 4300 m. At altitudes higher than 5000 m, alpine meadow steppe is present more often than alpine typical steppe. Meadow steppe and typical steppe are concentrated between 3000–4300 m. Desert steppe includes alpine desert steppe and warm desert steppe, which are lumped together because they

Fig. 3. Five regions divided by DEM in Tibet autonomous region.

are hardly used. Shrub herbosa is only present at altitudes lower than 3700 m and when the latitude is less than 30 N. At altitudes lower than 3000 m, less grass is present, though they have more forests [18]. On the basis of above geographical knowledge, the study divides the region into ten regions. The five DEM regions (Fig. 3) are 1–3000 m, 3000–3700 m, 3700–4300 m, 4300–5000 m, over 5000 m and two latitude regions—one south of 30 N and one north of 30 N. The method of combining the 50 clusters uses the temporal curves after HANTS. We randomly selected 60 samples in each of the two latitude regions divided by 30 N and visually interpreted them into the secondary types of grassland based on expert knowledge. Average temporal curves were separately calculated for the secondary types in each of the two latitude regions, and were considered as the typical temporal curves of each secondary type in the two latitude regions. Fig. 2 shows the curves of the latitude region north of 30 N. Average time-series curves of the 50 clusters were drawn and compared with the shape of the typical temporal curves. Five features are considered to be significant: the maximum EVI, the minimum EVI, the date of reaching maximum EVI, the date of reaching minimum EVI, and the date of reaching the minimum region (0.05 less than the minimum EVI). The similarity was compared for different regions according to latitude. The whole comparison process was carried out under the environment of ENVI4.3, using the decision-tree classification method.

WEN et al.: CLASSIFICATION OF GRASSLAND TYPES BY MODIS TIME-SERIES IMAGES IN TIBET, CHINA

407

B. Average EVI Comparision Among Each Types Fig. 5 shows the regional average EVI of each grassland types. Shrub herbosa has the largest average EVI. However, since its distribution area is limited, the shrub herbosa can’t play important parts in the stockbreeding. Average EVI of meadow steppe is 0.2548, which is the second largest grassland type, followed by alpine meadow steppe, typical steppe, and alpine typical steppe in that order. These four types are potentially available grasslands for stockbreeding. Desert steppe has the lowest average EVI. The utilization of desert steppe is difficult and due care should be taken for the protection of this type of grassland. Fig. 4. Distribution of grassland types in Tibet Autonomous Region classified by MOSIS-EVI.

E. Average EVI of Each Types Vegetation indexes have been proven to be broadly correlated with vegetation biophysical characteristics [23]. Previous studies have reported significant correlations between grass biomass and vegetation indexes [24], [25]. Many researchers estimate grass biomass or NPP in small regions, using a combination of remote-sensing data and ground measurements [26], [27]. This is a large-scale study, and there is no basic data available to construct grass biomass models. However, a correlation must exist between EVI and grass biomass. The average EVI per pixel of each type during the whole growth period is calculated to reflect the grass biomass ability for the different types. V. RESULTS AND VALIDATION A. Map for Grassland Types in the Tibet Autonomous Region The grassland types in Tibet Autonomous Region were mapped using the decision-tree classification method (Fig. 4). The total area of grassland covers 0.84 million square kilometers and accounts for 69.93% of the total land area of Tibet. Alpine typical steppe and alpine meadow steppe are the two types that have the largest area, and account for 44.67% and 32.45%, respectively, of the total grassland area. Alpine typical steppe is mainly present in the middle of Tibetan Autonomous Region. Alpine meadow steppe is found mainly in the high mountains in the east and on the plateau in the Naqu region. Desert steppe is the third type, accounting for 18.28% of the total area. It is mainly present in the north of Tibet, the Hoh Xil Basin and the southern side of the Kunlun Mountains. C , meager The region has a low maximum temperature mm , and a strong breeze implying annual precipitation that most of the grassland is of desert steppe type. The other three types, including meadow steppe, typical steppe, and shrub herbosa, account for 4.6% of the total area. Meadow steppe is mainly present on the both sides of the Jinsha River, the Lancang River, and the Nu River, where the altitude is around 3000 m. Typical steppe is mainly present along the middle reaches of the Brahmaputra, where the altitude is less than 3000 m. Shrub herbosa is only present in the southeast of Tibet, where the temperature is higher and precipitation is abundant.

C. Validation The atlas of grassland resources in China, 1:500 000 was mapped at the beginning of this century. It is less than five years since the MODIS_EVI data used by this study was obtained (2005). Dynamic changes of the grassland types may not be too significant during these five years. Thus, this data can be used as true value to validate the results classified by MODIS_EVI. However, certain dynamic changes take place and these changes always occur at the borders of each type. To overcome this uncertainty factor, the validation points were selected in the middle of each type, far away from the dynamic edge. 1) Comparing Proportion of Each Type: The proportion of each grassland type from the classification results based on MODIS_EVI temporal data is close to those in the atlas of grassland resources, 1:500 000 (Table II). The difference is less than 0.015. Considering the existence of dynamic changes and an acceptable method error, the difference of 0.015 is reasonable. Analyzing from the aspect of total area of each type, classification results using MODIS_EVI data are feasible. 2) Comparing by Confusion Matrix: As validation samples, 3980 points were selected randomly for each type, and the confusion matrix and kappa coefficient was calculated to examine the MODIS_EVI classification results (Table III). To avoid the influence of the unequal areas, the sample size was selected proportionate to the area. In the alpine typical steppe and the alpine meadow steppe, 1600 points were selected and 80 edge points were eliminated for each type. In the desert steppe, 600 points were selected and 40 edge points were eliminated. In the meadow steppe and typical steppe, 200 points were selected and 30 edge points were eliminated for each type. In the shrub herbosa, 50 points were selected and 10 edge points eliminated. The validation reveals that the overall accuracy of the classification result of grassland types in Tibet based on MODIS_EVI time series images is 68.02%, and the kappa coefficient is 0.52. Considering the fact that the study area is the largest plateau and that there exist some uncertainties in the true value based on the atlas of grassland resources in China due to possible difficulties during local measurements, this result is acceptable. Most of the alpine meadow steppe, alpine typical steppe, desert steppe, and shrub herbosa are classified correctly, with the correct classification percent greater than 60%. The correct classification percent of meadow steppe is 53%. Nearly 25% of meadow steppe is classified as alpine meadow steppe. This is because the method

408

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 3, NO. 3, SEPTEMBER 2010

Fig. 5. Average EVI of each grassland type from April to October in Tibet.

TABLE II PROPORTION OF GRASSLAND TYPES IN TIBET

TABLE III CONFUSION MATRIX OF GRASSLAND TYPE CLASSIFICATION RESULT IN 2005 BASED ON MODIS_EVI TEMPORAL PROFILE

used in this paper primarily emphasized the influence of altitude on types of grassland. Some meadow steppe present above the classification altitude was considered as alpine meadow steppe. Typical steppe has the lowest correct classification percentage. Nearly a quarter of typical steppe is classified as meadow steppe. This uncertainty is caused by the similarity of the growth period between typical steppe and meadow steppe. The region where uncertainty exists is the main region to analyze the dynamics of grassland resources. VI. CONCLUSION The advantages of high temporal resolution of MODIS were utilized to construct time series EVI, and the shortcomings of MODIS’ low spatial resolution are reduced considerably. Hence, MODIS images can be used for classification of secondary types of grasslands. In addition, MODIS is available for free and is suitable for monitoring large regions. This method offers an integrated low-cost, high-resolution technique

for large-region monitoring for remote sensing of grassland resources. EVI temporal feature curves are constructed, using the images in the growth period of grass. Using the similarity of the shape of the curve is effective in classifying grassland type. Dividing the large study area into individual regions based on natural factors makes classification more accurate. In the case of Tibet, the main natural factor having a significant influence is altitude. Temperature is the secondary natural factor. Land managers were provided with a map of the grassland types in Tibet in 2005. This is a breakthrough from the common existing grassland classification, since it classifies the grasslands into high, middle, and low vegetation fractions qualitatively. Alpine typical steppe and alpine meadow steppe are the dominant type of grassland, accounting for 77.12% of the total. Desert steppe is the third, accounting for 18.28% of the total grassland. It is mainly present in the north of Tibet, the Hoh Xil Basin and the south side of the Kunlun Mountains. Meadow steppe, typical steppe, and shrub herbosa, account for 4.6% of

WEN et al.: CLASSIFICATION OF GRASSLAND TYPES BY MODIS TIME-SERIES IMAGES IN TIBET, CHINA

the total area. The map of rangeland types in Tibet will be the basis for advancing stockbreeding and keeping an ecological and sustainable development. The average EVI of each grassland type during growth period is assumed to reflect the relative grass biomass of each type. Meadow steppe, alpine meadow steppe, typical steppe, and alpine typical steppe are potentially available grasslands for stockbreeding. REFERENCES [1] “FAOSTAT Database: Agriculture,” FAO, 1996 [Online]. Available: http://apps.fao.org/cgi-bin/nph-db.pl [2] X. Peng, Z. Jinzhong, and A. Shazhou, “Discussion on grassland type interpretation based on remote sensing images,” Chinese J. Grassland, vol. 6, pp. 61–66, Nov. 1988. [3] X. Guanhua and X. Jiyan, Renewable Resources Study by Remote Sensing. Beijing, China: Science Press, 1988, pp. 227–233. [4] Wenhe, “Discussion on investigation of natural grassland using remote sensing technology,” Chinese J. Grassland, vol. 2, pp. 74–79, Apr. 1989. [5] H. Jingfeng and W. Xiuzhen, “A study on monitoring and predicting models of grass yield in natural grassland using remote sensing data and meteorological data,” J. Remote Sens., vol. 5, pp. 69–74, Jan. 2001. [6] W. Zhengxing, L. Chuang, and Z. Bingru, “Potentials and limitation of AVHRR for grassland classification in Xilingol, Inner Mongolia,” J. Natural Resources, vol. 18, pp. 704–711, Nov. 2003. [7] D. Tieying and X. Yuchun, “Dynamic analysis on the natural pasture in Longyang reservoir region,” Pratacult. Sci., vol. 20, pp. 13–17, Mar. 2003. [8] Z. Fengli, Y. Qiu, K. Dingbo, L. Fengxia, and Z. Bingrong, “Optimal temporal selection for grassland spectrum classification,” J. Remote Sens., vol. 10, pp. 482–488, Jul. 2006. [9] Z. Bingru and M. Long, “Multi-source data complex classification of grassland in Inner Mongolia based on MODIS_EVI,” J. Zhejiang Univ. (Agric. & Life Sci.)., vol. 33, pp. 342–347, May 2007. [10] S. Xiaoqing, A. Shazhou, W. Kun, F. Yanmin, and J. Guili, “Classification of rangeland resources types in tianshan mountains of xinjiang uygur autonomous region, based on multi-source remote sensing data,” Acta Agrestia Sinica, vol. 15, pp. 145–152, Mar. 2007. [11] W. Zheng-Xing, L. Chuang, and H. Alfredo, “From AVHRR-NDVI to MODIS-EVI: Advances in vegetation index research,” Acta Ecologica Sinica, vol. 23, pp. 979–987, May 2003. [12] L. Wenhua, “Forests of the Himalayan-Hengduan mountains of china and strategies for their sustainable development,” in Proc. Int. Centre Integr. Mountain Develop., Kathmandu, 1993. [13] Z. Du, “The system of physico-geographical regions of the QinghaiXizang (Tibet) Plateau,” Sci. China, Ser. D, vol. 39, pp. 410–417, Apr. 1996. [14] “Government of Tibet Autonomous Region,” 2006 [Online]. Available: http://www.xizang.gov.cn/getCommonContent.do? contentId=342222 [15] D. J. Miller, “Fields of grass: Pastoralists of the pastoral landscape and nomads of the Tibetan Plateau and Himalayas,” J. Range Manage., vol. 52, pp. 297–298, May 1999. [16] Y. Zhaoli and W. Ning, “Rangeland privatization and its impacts on the Zoige wetlands on the eastern Tibetan Plateau,” J. Mountain Sci., vol. 2, pp. 105–115, Mar. 2005. [17] A. Huete, C. Justice, and W. V. Leeuwen, “Algorithm Theoretical Basis Document,” Modis Vegetation Index (MOD13) Version 3, 1999 [Online]. Available: http://modis.gsfc.nasa,gov/data/atbd_mod13.pdf [18] “Commission for compilation of grassland resources of china,” in Atlas of Grassland Resources of China, 1:1000 000. Beijing, China: SinoMaps Press, 1993.

409

[19] Z. Zengxiang, W. Xiao, W. Changyao, Z. Lijun, W. Qingke, D. Tingting, Z. Xiaoli, L. Bin, and Y. Ling, “National land cover mapping by remote sensing under the control of interpreted data [J ],” J. Geo. Inf. Sci., vol. 11, no. 2, pp. 216–224, 2009. [20] W. Verhoef, “Application of harmonic analysis of NDVI time series (HANTS),” in Fourier Analysis of Temporal NDVI in the Southern African and American Continents, S. Azzali and M. Menenti, Eds. Wageningen, The Netherlands: DLO Winand Staring Centre, 1996, pp. 19–24. [21] G. J. Roerink, M. Menen, and W. Verhoef, “Reconstructing cloudfree NDVI composites using Fourier analysis of time series,” Int. J. Remote Sens., vol. 21, pp. 1911–1917, Sep. 2000. [22] Z. Xia, S. Rui, Z. Bing, and T. Qingxi, “Land cover classification of north china plain using MODIS-EVI temporal profiles,” Trans. CSAE, vol. 22, pp. 128–133, Dec. 2006. [23] E. A. Cook, L. R. Iverson, and R. L. Graham, “Remote Sensing of Environment,” Int. J. Remote Sens., vol. 28, pp. 131–141, Apr.–Jun. 1989. [24] W. J. Ripple, “Landsat thematic mapper bands for characterizing fescue grass vegetation,” Int. J. Remote Sens., vol. 8, pp. 1373–1384, Aug. 1985. [25] H. Ikeda, K. Okamoto, and M. Fukuhara, “Estimation of aboveground grassland phytomass with a growth model using Landsat TM and climate data,” Int. J. Remote Sens., vol. 20, pp. 2283–2294, Nov. 1999. [26] C. Duo, J. Qiunei, D. Yangzong, and P. Ci, “Estimating grassland biomass in north Tibetan Plateau using EOS/MODIS,” Acta Meteorolog. Sinica, vol. 65, pp. 612–621, Aug. 2007. [27] G. Schino, F. Borfecchia, L. De Cecco, C. Dibari, M. Iannetta, S. Martini, and F. Pedrotti, “Satellite estimate of grass biomass in a mountainous range in central Italy,” Agroforestry Syst., vol. 59, pp. 157–162, 2003.

Qingke Wen was born in Jilin, China, in 1982. She received the B.S. degree in geography from Beijing Normal University, Beijing, China, in 2000, and the M.S. and Ph.D. degrees in geographic information systems from the Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, in 2006 and 2009, respectively. She is currently an Assistant Fellow with the Institute of Remote Sensing Applications, Chinese Academy of Sciences. Her research interests focus on the study of grassland from remote sensing, study of urban expansion from remote sensing, and study of wetland from remote sensing.

Zengxiang Zhang , photograph and biography not available at the time of publication.

Shuo Liu , photograph and biography not available at the time of publication.

Xiao Wang , photograph and biography not available at the time of publication.

Chen Wang , photograph and biography not available at the time of publication.