Chunxiang Cao

Temperature and vegetation seasonality diminishment over northern lands

Pronounced increases in winter temperature result in lower seasonal temperature differences, with implications for vegetation seasonality and productivity. Research now indicates that temperature and vegetation seasonality in northern ecosystems have diminished to an extent equivalent to a southerly shift of 4°– 7° in latitude, and may reach the equivalent of up to 20° over the twenty-first century.

Recent change of vegetation growth trend in China

Using satellite-derived normalized difference vegetation index (NDVI) data, several previous studies have indicated that vegetation growth significantly increased in most areas of China during the period 1982?99. In this letter, we extended the study period to 2010. We found that at the national scale the growing season (April?October) NDVI significantly increased by 0.0007?yr?1 from 1982 to 2010, but the increasing trend in NDVI over the last decade decreased in comparison to that of the 1982?99 period. The trends in NDVI show significant seasonal and spatial variances. The increasing trend in April and May (AM) NDVI (0.0013?yr?1) is larger than those in June, July and August (JJA) (0.0003?yr?1) and September and October (SO) (0.0008?yr?1). This relatively small increasing trend of JJA NDVI during 1982?2010 compared with that during 1982?99 (0.0012?yr?1) (Piao et?al 2003 J. Geophys. Res.?Atmos. 108?4401) implies a change in the JJA vegetation growth trend, which significantly turned from increasing (0.0039?yr?1) to slightly decreasing (???0.0002?yr?1) in 1988. Regarding the spatial pattern of changes in NDVI, the growing season NDVI increased (over 0.0020?yr?1) from 1982 to 2010 in southern China, while its change was close to zero in northern China, as a result of a significant changing trend reversal that occurred in the 1990s and early 2000s. In northern China, the growing season NDVI significantly increased before the 1990s as a result of warming and enhanced precipitation, but decreased after the 1990s due to drought stress strengthened by warming and reduced precipitation. Our results also show that the responses of vegetation growth to climate change vary across different seasons and ecosystems.

Automated Methods for Measuring DBH and Tree Heights with a Commercial Scanning Lidar

Accurate forest structural parameters are crucial to forest inventory, and modeling of the carbon cycle and wildlife habitat. Lidar (Light Detection and Ranging) is particularly suitable to the measurement of forest structural parameters. In this paper, we describe a pilot study to extract forest structural parameters, such as tree height, diameter at breast height (DBH), and position of individual tree using a terrestrial lidar (LMS-Z360i; Riegel, Inc.). The lidar was operated to acquire both vertical and horizontal scanning in the field in order to obtain a point cloud of the whole scene. An Iterative Closet Point (ICP) algorithm was introduced to obtain the transformation matrix of each range image and to mosaic multiple range images together. Based on the mosaiced data set, a variable scale and threshold filtering method was used to separate ground from the vegetation. Meanwhile, a Digital Elevation Model (DEM) and a Canopy Height Model (CHM) were generated from the classified point cloud. A stem detection algorithm was used to extract the location of individual trees. A slice above 1.3 m from the ground was extracted and rasterized. A circle fitting algorithm combined with the Hough transform was used to retrieve the DBH based on the rasterized grid. Tree heights were calculated using the height difference between the minimum and maximum Z values within the position of each individual tree with a 1 m buffer. All of the 26 trees were detected correctly, tree height and DBH were determined with a precision of 0.76 m and 3.4 cm, respectively, comparing with those visually measured in the lidar data. Our methods and results confirm that terrestrial lidar can provide nondestructive, high-resolution, and automatic determination of parameters required in forest inventory.

Change detection of an earthquake-induced barrier lake based on remote sensing image classification

The earthquake-induced barrier lakes that are caused by landslides are at risk of bursting after excessive rainfall, and they are a serious threat to the surrounding residents. This paper proposes a change detection approach of the barrier lake using the method of post-classification comparison combined with background subtraction. The case studied in the paper is the detection of the Tangjiashan barrier lake, which is one of the largest lakes induced by the Wenchuan Earthquake on 12 May 2008. Both the pre- (31 March 2007) and post-earthquake (16 May 2008) images of the Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer type 2 (AVINER-2) were collected in the paper. The results show that the river widened through the Zhangping area, increasing from 90 m pre-earthquake to 545 m post-earthquake, and the surface area of the barrier lake increased from 0.945 km2 to 1.471 km2.

Forest stand biomass estimation using ALOS PALSAR data based on LiDAR-derived prior knowledge in the Qilian Mountain, western China

Studies are needed to evaluate the ability of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) for forest aboveground biomass (AGB) extraction in mountainous areas. In this article, forest biomass was estimated at plot and stand levels, and different biomass grades, respectively. Light detection and ranging (LiDAR) data with about one hit per m2 were first used for forest biomass estimation at the plot level, with R 2 of 0.77. Then the LiDAR-derived biomass, as prior knowledge, was used to investigate the relationship between ALOS PALSAR data and biomass. The results showed that at each biomass level, the range of the back-scatter coefficient in HH and HV polarization (where H and V represent horizontal and vertical polarizations, respectively, and the first of the two letters refers to the transmission polarization and the second to the received polarization) was very large and there was no obvious relationship between the synthetic aperture radar (SAR) back-scatter coefficient and biomass at plot level. At stand level and in different biomass grades, the back-scatter coefficient increased with the increase of forest biomass, and a logarithm equation can be used to describe the relationship. The main reason may be that forest structure is complex at the plot level, while the average value could partly decrease the influence of forest structure at stand level. Meanwhile, terrain radiometric correction (TRC) was investigated and found effective for forest biomass estimation.

Risk analysis for the highly pathogenic avian influenza in Mainland China using meta-modeling

A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 22 d. The potential risk of occurrence of HPAI in Mainland China for the period from January 23 to February 17, 2004 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.

Monitoring haze episodes over the Yellow Sea by combining multisensor measurements

Haze, which is composed of a wide range of aerosol particles, is one of the most hazardous weather conditions because of its adverse impact on health and its deleterious effect on visibility, leading to loss of maritime transportation. Satellite, ground-based sunphotometer and particulate matter (PM) concentration data were analysed to evaluate the causes of two severe haze episodes observed during 28–31 March and 3–6 June 2007 over the Yellow Sea. The first episode was clearly affected by the long-range transport of dust from southeastern Mongolia to eastern Inner Mongolia, covering the Onqin Daga and Horqin sandy land areas, which are important sources for the sand and dust storms (SDS) that occur frequently during the spring in East Asia. A backward trajectory analysis confirmed the transport of air mass from southeastern Mongolia. A very high aerosol optical depth (AOD) (> 2.0) and a high backscatter coefficient (about 5 × 10−2 km sr−1) of dust were observed by Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), respectively, during the first haze episode. A sudden increase in PM10 (particles ≤ 10 μm) concentration (maximum value 225 μg m−3) was observed during the haze period. A higher AOD was observed over the Yellow Sea on 6 June 2007 during the second haze event compared to the AOD observed during the first haze episode, which occurred during 4–6 June 2007. A few hotspots were detected by MODIS during the second haze episode. It was concluded that the second haze episode was probably dominated by smoke from open burning areas of crop residue in East China, when a rapid increase in PM10 concentration up to 192 μg m−3 was observed.

Epidemic risk analysis after the Wenchuan Earthquake using remote sensing

On 12 May 2008, Wenchuan Earthquake, magnitude 8.0, destroyed thousands of buildings, and resulted in thousands of people being buried in the collapsed buildings. In order to investigate the potential epidemic disease risk after earthquake, a Backward Propagation Neural Network (BPNN) was constructed to assess the potential epidemic risks by applying remote sensing technology to obtain Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) values, as well as by using a geographic information system (GIS) to gain ambient epidemic-related spatial factors over the earthquake region. In this study, a relationship was established between the change in environmental factors after earthquake and potential epidemic risks, which was found to be statistically significant. The result might be explained for three change perspectives, namely environmental risks, medical risks and psychological risks. The corresponding strategies for preparedness in case of epidemic disease were given.

Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA

A methodology to generate spatially continuous fields of tree heights with an optimized Allometric Scaling and Resource Limitations (ASRL) model is reported in this first of a multi-part series of articles. Model optimization is performed with the Geoscience Laser Altimeter System (GLAS) waveform data. This methodology is demonstrated by mapping tree heights over forested lands in the continental USA (CONUS) at 1 km spatial resolution. The study area is divided into 841 eco-climatic zones based on three forest types, annual total precipitation classes (30 mm intervals) and annual average temperature classes (2 °C intervals). Three model parameters (area of single leaf, α, exponent for canopy radius, η, and root absorption efficiency, γ) were selected for optimization, that is, to minimize the difference between actual and potential tree heights in each of the eco-climatic zones over the CONUS. Tree heights predicted by the optimized model were evaluated against GLAS heights using a two-fold cross validation approach (R2 = 0.59; RMSE = 3.31 m). Comparison at the pixel level between GLAS heights (mean = 30.6 m; standard deviation = 10.7) and model predictions (mean = 30.8 m; std. = 8.4) were also performed. Further, the model predictions were compared to existing satellite-based forest height maps. The optimized ASRL model satisfactorily reproduced the pattern of tree heights over the CONUS. Subsequent articles in this series will document further improvements with the ultimate goal of mapping tree heights and forest biomass globally.

The novel H1N1 Influenza A global airline transmission and early warning without travel containments

A novel influenza A (H1N1) has been spreading worldwide. Early studies implied that international air travels might be key cause of a severe potential pandemic without appropriate containments. In this study, early outbreaks in Mexico and some cities of United States were used to estimate the preliminary epidemic parameters by applying adjusted SEIR epidemiological model, indicating transmissibility infectivity of the virus. According to the findings, a new spatial allocation model totally based on the real-time airline data was established to assess the potential spreading of H1N1 from Mexico to the world. Our estimates find the basic reproductive number R0 of H1N1 is around 3.4, and the effective reproductive number fall sharply by effective containment strategies. The finding also implies Spain, Canada, France, Panama, Peru are the most possible country to be involved in severe endemic H1N1 spreading.