Observation and simulation of net primary productivity in Qilian Mountain, western China

We modeled net primary productivity (NPP) at high spatial resolution using an advanced spaceborne thermal emission and reflection radiometer (ASTER) image of a Qilian Mountain study area using the boreal ecosystem productivity simulator (BEPS). Two key driving variables of the model, leaf area index (LAI) and land cover type, were derived from ASTER and moderate resolution imaging spectroradiometer (MODIS) data. Other spatially explicit inputs included daily meteorological data (radiation, precipitation, temperature, humidity), available soil water holding capacity (AWC), and forest biomass. NPP was estimated for coniferous forests and other land cover types in the study area. The result showed that NPP of coniferous forests in the study area was about 4.4 tCha(-1)y(-1). The correlation coefficient between the modeled NPP and ground measurements was 0.84, with a mean relative error of about 13.9%.     

LAI inversion algorithm based on directional reflectance kernels

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.     

Combining remote sensing imagery and forest age inventory for biomass mapping

Aboveground biomass (AGB) of forests is an important component of the global carbon cycle. In this study, Landsat ETM(+) images and field forest inventory data were used to estimate AGB of forests in Liping County, Guizhou Province, China. Three different vegetation indices, including simple ratio (SR), reduced simple ratio (RSR), and normalized difference vegetation index (NDVI), were calculated from atmospherically corrected ETM(+) reflectance images. A leaf area index (LAI) map was produced from the RSR map using a regression model based on measured LAI and RSR. The LAI map was then used to develop an initial AGB map, from which forest stand age was deduced. Vegetation indices, LAI, and forest stand age were together used to develop AGB estimation models for different forest types through a stepwise regression analysis. Significant predictors of AGB changed with forest types. LAI and NDVI were significant predictors of AGB for Chinese fir (R(2)=0.93). The model using LAI and stand age as predictors explained 94% of the AGB variance for coniferous forests. Stand age captured 79% of the AGB variance for broadleaved forests (R(2)=0.792). AGB of mixed forests was predicted well by LAI and SR (R(2)=0.931). Without differentiating among forest types, the model with SR and LAI as predictors was able to explain 90% of AGB variances of all forests. In Liping County, AGB shows a strong gradient that increases from northeast to southwest. About 64% of the forests have AGB in the range from 90 to 180 t ha(-1).     

Measurements and simulation of forest leaf area index and net primary productivity in Northern China

Large scale process-based modeling is a useful approach to estimate distributions of global net primary productivity (NPP). In this paper, in order to validate an existing NPP model with observed data at site level, field experiments were conducted at three sites in northern China. One site is located in Qilian Mountain in Gansu Province, and the other two sites are in Changbaishan Natural Reserve and Dunhua County in Jilin Province. Detailed field experiments are discussed and field data are used to validate the simulated NPP. Remotely sensed images including Landsat Enhanced Thematic Mapper plus (ETM+, 30 m spatial resolution in visible and near infrared bands) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, 15m spatial resolution in visible and near infrared bands) are used to derive maps of land cover, leaf area index, and biomass. Based on these maps, field measured data, soil texture and daily meteorological data, NPP of these sites are simulated for year 2001 with the boreal ecosystem productivity simulator (BEPS). The NPP in these sites ranges from 80 to 800 gCm(-2)a(-1). The observed NPP agrees well with the modeled NPP. This study suggests that BEPS can be used to estimate NPP in northern China if remotely sensed images of high spatial resolution are available.     

Selecting Sites for Converting Farmlands to Wetlands in the Sanjiang Plain, Northeast China, Based on Remote Sensing and GIS

Wetlands in the Sanjiang Plain are rich in biodiversity and natural resources in the northeast of China. However, this wetland area has decreased in size and deteriorated in quality owing to expanded agricultural activities since the 1950s. Converting farmlands to wetlands is necessary to improve these conditions. Using Remote Sensing (RS) and Geographic Information Systems (GIS) technologies, we derived farmland productivity data and hydrology data for the Sanjiang Plain. The farmland productivity data were derived from land use and net primary productivity (NPP) data of the MODIS products. We obtained three productivity farmland classes (low, medium, and high) through the NPP anomaly percentage method. We were only concerned with the low-productivity farmland. Hydrology data were modeled with a wetness index, which was derived from Digital Elevation Model (DEM) data. Based on these two data layers, we identified and prioritized sites for the conversion of farmlands to wetlands. The areas with low farmland productivity and medium or high wetness values have potential to support the conversion of farmlands to wetlands. Potential sites were prioritized in terms of patch size and proximity to natural wetlands and water bodies. We obtained three priority classes, among which the high-priority class would be used as the areas for the recent conversion of farmlands to wetlands. The area of this class was 75,888 ha and accounted for 1.3% of the total farmland area.     

Spatial scaling between leaf area index maps of different resolutions

We developed algorithms for spatial scaling of leaf area index (LAI) using sub-pixel information. The study area is located near Liping County, Guizhou Province, in China. Methods for LAI spatial scaling were investigated on LAI images with 960 m resolution derived in two ways. LAI from distributed calculation (LAID) was derived using Landsat ETM+ data (30 m), and LAI from lumped calculation (LAIL) was obtained from the coarse (960 m) resolution data derived through resampling the ETM+ data. We found that lumped calculations can be considerably biased compared to the distributed (ETM+) case, suggesting that global and regional LAI maps can be biased if surface heterogeneity within the mapping resolution is ignored. Based on these results, we developed algorithms for removing the biases in lumped LAI maps using sub-pixel land cover-type information, and applied these to correct one coarse resolution LAI product which greatly improved its accuracy.     

Soil organic carbon decomposition and carbon pools in temperate and sub-tropical forests in China

Decomposition of soil organic carbon (SOC) is a critical component of the global carbon cycle, and accurate estimates of SOC decomposition are important for forest carbon modeling and ultimately for decision making relative to carbon sequestration and mitigation of global climate change. We determined the major pools of SOC in four sites representing major forest types in China: temperate forests at Changbai Mountain (CBM) and Qilian Mountain (QLM), and sub-tropical forests at Yujiang (YJ) and Liping (LP) counties. A 90-day laboratory incubation was conducted to measure CO(2) evolution from forest soils from each site, and data from the incubation study were fitted to a three-pool first-order model that separated mineralizable soil organic carbon into active (C(a)), slow (C(s)) and resistant (C(r)) carbon pools. Results indicate that: (1) the rate of SOC decomposition in the sub-tropical zone was faster than that in the temperature zone, (2) The C(a) pool comprised approximately 1-3% of SOC with an average mean residence time (MRT) of 219 days. The C(s) pool comprised approximately 25-65% with an average MRT of 78 yr. The C(r) pool accounted for approximately 35-80% of SOC, (3) The YJ site in the sub-tropical zone had the greatest C(a) pool and the lowest MRT, while the QLM in the temperature zone had the greatest MRT for both the C(a) and C(s) pools. The results suggest a higher capacity for long-term C sequestration as SOC in temperature forests than in sub-tropical forests.     

A focus group study of factors that promote and constrain the use of satellite-derived fire products by resource managers in southern Africa

Semi-structured focus group interviews were employed to examine factors that affect the likelihood that resource managers in southern Africa will use information on vegetation fires provided by two satellite-derived products: an active fire product and a burned area product. The two products are updated regularly and aim to deliver the state-of-the-art in the global monitoring of fires from satellite remote-sensing. Both products are derived from data transmitted by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors carried onboard NASA's Aqua and Terra satellites. The active fire product can be accessed for free via the internet and on media by users working anywhere in the world; the burned area product will be accessible in a similar manner in 2006. The MODIS fire products provide systematic, near-global coverage and are freely available; as such, they give resource managers new opportunities to obtain or supplement information they need to manage vegetation fires effectively. However, the availability of these products does not mean that resource managers will use them, and many other factors are involved. To understand factors that affect whether southern African resource managers will use the two products, two focus groups were held with members of the Southern African Fire Network (SAFNet) in Malawi, Africa, August 2004. Analysis of the group discussions reveals a number of factors that influence whether they will use the products. The qualitative, in depth nature of the group discussions revealed 12 main factors that influence product use; not least the low international internet bandwidths for African countries outside of South Africa. Analysis of the group discussions also suggests how the uptake of MODIS fire products by resource managers in southern Africa might be enhanced by affecting specific changes to how MODIS products are packaged and delivered.     

Retrieving leaf area index for coniferous forest in Xingguo County, China with Landsat ETM+ images

Spatial distributions of the leaf area index (LAI) needed for carbon cycle modeling in Xingguo County, China were estimated based on correlations between the field-measurements and vegetation indices (VIs). After making geometric and atmospheric corrections to two Landsat ETM+ images, one in January 2000 and the other in May 2003, three VIs (SR, NDVI, and RSR) were derived, and their separate correlations with ground LAI measurements were established. The correlation with RSR was the highest among the three VIs. The retrieved LAI values for January 2000 were lower than those for May 2003 because of a small seasonal variation in the coniferous forests (predominantly masson pine) and the decrease in the understorey vegetation during winter.     

Tests of soil organic carbon density modeled by InTEC in China's forest ecosystems

The integrated terrestrial ecosystem C-budget model (InTEC) developed by Chen and co-workers has been used successfully to predict carbon dynamics of forests in Canada. It was tested here for forest soil organic carbon (SOC) density of China's northern temperate zone and southern subtropical zone. The results show that the simulated SOC density is highly correlated and in broad agreement with observations in Liping and in Changbaishan, representing the southern subtropical zone and the northern temperate zone in China, respectively. SOC density ranged from 2.2 to 11.2 kg/m(2) in Liping and from 3.4 to 14.8 kg/m(2) in Changbaishan. The correlation coefficients (r(2)) are 0.63 (N=16) and 0.76 (N=14) between the simulated and measured data in Liping and Changbaishan, respectively. The SOC densities under different vegetation types in Liping decrease in the order of mixed forest, broadleaf forest, Chinese fir, couch grass, and Chinese redpine, and in Changbaishan in the order of mixed forest, silver fir, larch forest, and birch forest.     

峨眉山景区旅游商品的开发与营销模式

通过对旅游商品定义的综述,界定了旅游商品的定义、范围和功能,同时对峨眉山景区旅游商品的开发、需求、购买力等抽取420份问卷作为样本,分析了峨眉山旅游商品的需求现状,并以旅游者需求现状为依据,对峨眉山旅游商品的开发和营销提出了“4P＋4C＋4R”营销策略,以期对其它景区的旅游商品开发和营销具有一定的借鉴意义。     

Assessing Satellite and Ground‐Based Potential Evapotranspiration for Hydrologic Applications in the Colorado River Basin

This study evaluates a remotely sensed and two ground‐based potential evapotranspiration (PET) products for hydrologic application in the Upper Colorado River Basin (UCRB). The remotely sensed Moderate Resolution Imaging Spectroradiometer product (MODIS‐PET) is a continuous, daily time series with 250 m resolution derived using the Priestley‐Taylor (P‐T) equation. The MODIS‐PET is evaluated against regional flux tower data as well as a synthetic pan product (Epan; 0.125°, daily) derived from the North American Land Data Assimilation System (NLDAS) and a Hargreaves PET derived from DAYMET variables (DAYMET‐PET; 1 km, daily). Compared to point‐scale PET computed using regional flux tower data, the MODIS‐PET had lower errors, with RMSE values ranging from 2.24 to 2.85 mm/day. Epan RMSE values ranged from 3.70 to 3.76 mm/day and DAYMET‐PET RMSE values ranged from 3.55 to 4.58 mm/day. Further investigation showed biases in temperature and radiation data contribute to uncertainty in the MODIS‐PET values, while bias in NLDAS temperature, downward shortwave (SW↓), and downward longwave (LW↓) propagate in the Epan estimates. Larger discrepancies between methods were observed in the warmer, drier regions of the UCRB, however, the MODIS‐PET was more responsive to landcover transitions and better captured basin heterogeneity. Results indicate the satellite‐based MODIS product can serve as a viable option for obtaining spatial PET values across the UCRB.     

Forest Dynamics in the Eastern Ghats of Tamil Nadu,India

The primary deciduous forests in the Eastern Ghats (EG) of Tamil Nadu (TN) India have undergone many changes owing to various need-based forest managements, such as timber extraction for industry, railway sleepers, charcoal, and forest clearance for hydroelectric projects and agriculture, during preindependence and postindependence periods (i.e., from 1800 to 1980). The enactment of a forest conservation act during the 1980s changed the perception of forest managers from utilization to conservation. This study was taken up to assess the forests dynamics in the EG of TN spatially between 1990 and 2003 and nonspatially between 1900 and the 1980s. Landsat Thematic Mapper (TM) and Indian Remote Sensing satellite (IRS) 1D Linear Imaging and Self Scanning (LISS III) data were used to assess forests during 1990 and 2003, respectively. Field floristic survey and secondary data (such as published literature, floras, books, and forest working plans) were used to assess the forest dynamics in terms of forest type and species composition among the preindependence period, the postindependence period, and the present (i.e., before and after 1980). The satellite data analysis revealed a considerable amount of changes in all forest types during the 13 years. The comparison of species composition and forest types between the past and present revealed that need-based forest management along with anthropogenic activity have altered the primary deciduous forest in to secondary and postextraction secondary forests such as southern thorn and southern thorn scrub forests in the middle [400–900 m above mean sea level (MSL)] and lower slopes (<400 m MSL). However, the evergreen forests present at the upper slope (>900 m MSL) and plateau seemed not to be much affected by the forest management. The changes estimated by the satellite data processing in the major forest types such as evergreen, deciduous, southern thorn, and southern thorn scrub are really alarming because these changes have occurred after the implementation of a forest conservation act. The dependence of local people on forests for various purposes in this region is also considerably high, which might be a key factor for the changes in the forests. The results of this study not only provide an outlook on the present status of the forests and the change trends but also provide the basis for further studies on forests in the EG of TN.     

Application of Satellite Microwave Images in Estimating Snow Water Equivalent1

Abstract: Flood forecast and water resource management requires reliable estimates of snow pack properties [snow depth and snow water equivalent (SWE)]. This study focuses on application of satellite microwave images to estimate the spatial distribution of snow depth and SWE over the Great Lakes area. To estimate SWE, we have proposed the algorithm which uses microwave brightness temperatures (Tb) measured by the Special Sensor Microwave Imager (SSM/I) radiometer along with information on the Normalized Difference Vegetation Index (NDVI). The algorithm was developed and tested over 19 test sites characterized by different seasonal average snow depth and land cover type. Three spectral signatures derived from SSM/I data, namely T19V‐T37V (GTV), T19H‐T37H (GTH), and T22V‐T85V (SSI), were examined for correlation with the snow depth and SWE. To avoid melting snow conditions, we have used observations taken only during the period from December 1‐February 28. It was found that GTH, and GTV exhibit similar correlation with the snow depth/SWE and are most should be used over deep snowpack. In the same time, SSI is more sensitive to snow depth variations over a shallow snow pack. To account for the effect of dense forests on the scattering signal of snow we established the slope of the regression line between GTV and the snow depth as a function of NDVI. The accuracy of the new technique was evaluated through its comparison with ground‐based measurements and with results of SWE analysis prepared by the National Operational Hydrological Remote Sensing Center (NOHRSC) of the National Weather Service. The proposed algorithm was found to be superior to previously developed global microwave SWE retrieval techniques.     

Carbon sinks and sources in China's forests during 1901-2001

This paper reports the annual carbon (C) balance of China's forests during 1901-2001 estimated using the Integrated Terrestrial Ecosystem C-budget model (InTEC). Annual carbon source and sink distributions are simulated for the same period using various spatial datasets including land cover and leaf area index (LAI) obtained from remote sensing, soil texture, climate, forest age, and nitrogen deposition. During 1901-1949, China's forests were a source of 21.0+/-7.8 Tg C yr(-1) due to disturbances (human activities). Its size increased to 122.3+/-25.3 Tg C yr(-1) during 1950-1987 due to intensified human activities in the late 1950s, early 1960s, 1970s and early 1980s. The forests became large sinks of 176.7+/-44.8 Tg C yr(-1) during 1988-2001, owing to large-scale plantation and forest regrowth in previously disturbed areas as well as growth stimulation by nondisturbance factors such as climatic warming, atmospheric CO(2) fertilization, and N deposition. From 1901 to 2001, China's forests were a small carbon source of 3.32 Pg C, about 32.9+/-22.3 Tg C yr(-1). The overall C balance in biomass from InTEC generally agrees with previous results derived from forest inventories of China's forests. InTEC results also include C stock variation in soils and are therefore more comprehensive than previous results. The uncertainty in InTEC results is still large, but it can be reduced if a detailed forest age map becomes available.     

CLIMATE CHANGE IMPACTS ON THE HYDROLOGY AND PRODUCTIVITY OF A PINE PLANTATION1

ABSTRACT: There are increasing concerns in the forestry community about global climate change and variability associated with elevated atmospheric CO₂. Changes in precipitation and increases in air temperature could impose additional stress on forests during the next century. For a study site in Carteret County, North Carolina, the General Circulation Model, HADCM2, predicts that by the year 2099, maximum air temperature will increase 1.6 to 1.9°C, minimum temperature will increase 2.5 to 2.8°C, and precipitation will increase 0 to 10 percent compared to the mid‐1990s. These changes vary from season to season. We utilized a forest ecosystem process model, PnET‐II, for studying the potential effects of climate change on drainage outflow, evapotranspiration, leaf area index (LAI) and forest Net Primary Productivity (NPP). This model was first validated with long term drainage and LAI data collected at a 25‐ha mature loblolly pine (Pinus taeda L.) experimental watershed located in the North Carolina lower coastal plain. The site is flat with poorly drained soils and high groundwater table. Therefore, a high field capacity of 20 cm was used in the simulation to account for the topographic effects. This modeling study suggested that future climate change would cause a significant increase of drainage (6 percent) and forest productivity (2.5 percent). Future studies should consider the biological feedback (i.e., stomata conductance and water use efficiency) to air temperature change.     

THERMAL MAPPING OF STREAMS FROM AIRBORNE RADIOMETRIC SCANNING1

This paper begins with a brief review of radiation theory as applied to water temperature determinations. Errors introduced in “radiant” temperature measurements due to nonblackness of the water surface and the effects of the atmosphere are included in this discussion. The airborne scanner system is described. Analysis and display of scanner data using the Laboratory for Applications of Remote Sensing (LARS) display system are discussed. Thermal maps of four sections of the Wabash River are included and points of interest of each map are discussed in the text.     

A Multidisciplinary Decision Support System for Forest Fire Crisis Management

A wildland fire is a serious threat for forest ecosystems in Southern Europe affecting severely and irreversibly regions of significant ecological value as well as human communities. To support decision makers during large-scale forest fire incidents, a multidisciplinary system has been developed that provides rational and quantitative information based on the site-specific circumstances and the possible consequences. The systems architecture consists of several distinct supplementary modules of near real-time satellite monitoring and fire forecast using an integrated framework of satellite Remote Sensing, GIS, and RDBMS technologies equipped with interactive communication capabilities. The system may handle multiple fire ignitions and support decisions regarding dispatching of utilities, equipment, and personnel that would appropriately attack the fire front. The operational system was developed for the region of Penteli Mountain in Attika, Greece, one of the mountain areas in the country most hit by fires. Starting from a real fire incident in August 2000, a scenario is presented to illustrate the effectiveness of the proposed approach.     

基于贝叶斯网络分类的土壤盐渍化遥感监测

贝叶斯网络是一种将贝叶斯概率方法和有向无环图的网络拓扑结构有机结合的概率模型.采用贝叶斯网络分类对具有典型干旱特征的库车县土壤盐渍化情况进行监测,首先应用条件独立性测试原理建立贝叶斯网络结构,把研究区遥感数据进行离散化,然后应用贝叶斯定理作为分类原则,将每个像元分为像元最大概率的类别.研究结果表明该方法来分类6种地类的整体分类精度达到96%,并为该区盐渍地面积,空间分布等特征监测提供较好的依据.     

Using Landsat data to estimate evapotranspiration of winter wheat

An evapotranspiration (ET) model that accurately estimates daily water use and soil moisture on a regional basis is required for many agricultural and hydrological studies. The model should use meterological data that are readily available and crop information that is responsive to the changing vigor of the plants.We evaluated an ET model with a weighing lysimeter and then applied it to winter wheatfields at four Kansas locations. Model inputs are solar radiation, temperature, precipitation, and leaf area index (LAI); included in the outputs are estimates of transpiration, evaporation, and soil moisture. An equation was developed to estimate LAI from Landsat data. Because LAI can be estimated from satellites, the ET model can potentially be used on a regional basis.