Fengmei Yao

Combination of multi-sensor remote sensing data for drought monitoring over Southwest China

Abstract Drought is one of the most frequent climate-related disasters occurring in Southwest China, where the occurrence of drought is complex because of the varied landforms, climates and vegetation types. To monitor the comprehensive information of drought from meteorological to vegetation aspects, this paper intended to propose the optimized meteorological drought index (OMDI) and the optimized vegetation drought index (OVDI) from multi-source satellite data to monitor drought in three bio-climate regions of Southwest China. The OMDI and OVDI were integrated with parameters such as precipitation, temperature, soil moisture and vegetation information, which were derived from Tropical Rainfall Measuring Mission (TRMM), Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST), AMSR-E Soil Moisture (AMSR-E SM), the soil moisture product of China Land Soil Moisture Assimilation System (CLSMAS), and MODIS Normalized Difference Vegetation Index (MODIS NDVI), respectively. Different sources of satellite data for one parameter were compared with in situ drought indices in order to select the best data source to derive the OMDI and OVDI. The Constrained Optimization method was adopted to determine the optimal weights of each satellite-based index generating combined drought indices. The result showed that the highest positive correlation and lowest root mean square error (RMSE) between the OMDI and 1-month standardized precipitation evapotranspiration index (SPEI-1) was found in three regions of Southwest China, suggesting that the OMDI was a good index in monitoring meteorological drought; in contrast, the OVDI was best correlated to 3-month SPEI (SPEI-3), and had similar trend with soil relative water content (RWC) in temporal scale, suggesting it a potential indicator of agricultural drought. The spatial patterns of OMDI and OVDI along with the comparisons of SPEI-1 and SPEI-3 for different months in one year or one month in different years showed significantly varied drought locations and areas, demonstrating regional and seasonal fluctuations, and suggesting that drought in Southwest China should be monitored in seasonal and regional level, and more fine distinctions of seasons and regions need to be considered in the future studies of this area.

Evaluation of Grassland Dynamics in the Northern-Tibet Plateau of China Using Remote Sensing and Climate Data

The grassland ecosystem in the Northern-Tibet Plateau (NTP) of China is very sensitive to weather and climate conditions of the region. In this study, we investigate the spatial and temporal variations of the grassland ecosystem in the NTP using the NOAA/AVHRR ten-day maximum NDVI composite data of 1981-2001. The relationships among Vegetation Peak-Normalized Difference Vegetation Index (VP-NDVI) and climate variables were quantified for six counties within the NTP. The notable and uneven alterations of the grassland in response to variation of climate and human impact in the NTP were revealed. Over the last two decades of the 20th century, the maximum greenness of the grassland has exhibited high increase, slight increase, no-change, slight decrease and high decrease, each occupies 0.27%, 8.71%, 77.27%, 13.06% and 0.69% of the total area of the NTP, respectively. A remarkable increase (decrease) in VP-NDVI occurred in the central-eastern (eastern) NTP whereas little change was observed in the western and northwestern NTP. A strong negative relationship between VP-NDVI and ET0 was found in sub-frigid, semi-arid and frigid- arid regions of the NTP (i.e., Nakchu, Shantsa, Palgon and Amdo counties), suggesting that the ET0 is one limiting factor affecting grassland degradation. In the temperate-humid, sub-frigid and sub-humid regions of the NTP (Chali and Sokshan counties), a significant inverse correlation between VP-NDVI and population indicates that human activities have adversely affected the grassland condition as was previously reported in the literature. Results from this research suggest that the alteration and degradation of the grassland in the lower altitude of the NTP over the last two decades of the 20th century are likely caused by variations of climate and anthropogenic activities.

Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.

Simulating urban growth processes by integrating cellular automata model and artificial optimization in Binhai New Area of Tianjin, China

This study presents an optimized algorithm into the cellular automata (CA) models for urban growth simulation in Binhai New Area of Tianjin, China. The optimized CA model by particle swarm optimization (PSO) was compared with the logistic-based cellular automata (LOGIT-CA) model to see the effects of the simulation. The study evaluated the stochastic disturbance in the development of urban growth using the Monte Carlo method; the coefficient d determined the state of urban growth. The validation was conducted by both cross-tabulation test and structural measurements. The results showed that the simulations of PSO-CA were better than LOGIT-CA model, indicating an improvement in the spatio-temporal simulation of urban growth and land use changes in study area. Since the simulations reached their best values when the coefficient was between 1 and 2, the urban growth in the study area was in the period of conversion from spontaneous growth to edge-expansion and infilling growth.

Validating the Modified Perpendicular Drought Index in the North China Region Using In Situ Soil Moisture Measurement

Soil moisture content is one of the most important variables for monitoring and assessing the drought condition. In this letter, a modified perpendicular drought index (MPDI) derived from the Moderate Resolution Imaging Spectroradiometer satellite data was validated using in situ soil moisture measurements in Henan province of North China. The soil moisture at different depths of a soil layer and time lag on usefulness of the MPDI for estimating soil moisture content was analyzed; the effectiveness of the MPDI was evaluated with the observed soil moisture content and the comprehensive drought index K for different soil types. The results showed that the MPDI was significantly correlated with soil moisture content for the top soil layer with 10 cm depth. For a time lag ranging from 0 to 4 days, the correlation of MPDI to soil moisture was significant with no time lag in the depth of top 10 cm (r = -0.867). For the stations of the same soil texture type of loam, the correlation coefficient between MPDI and soil moisture is higher than that of all soil texture types. In a regional scale, the MPDI reflected an obvious spatial pattern of drought under different growing stages in the drought years over the study area.

Multivariate drought frequency estimation using copula method in Southwest China

Drought over Southwest China occurs frequently and has an obvious seasonal characteristic. Proper management of regional droughts requires knowledge of the expected frequency or probability of specific climate information. This study utilized k-means classification and copulas to demonstrate the regional drought occurrence probability and return period based on trivariate drought properties, i.e., drought duration, severity, and peak. A drought event in this study was defined when 3-month Standardized Precipitation Evapotranspiration Index (SPEI) was less than −0.99 according to the regional climate characteristic. Then, the next step was to classify the region into six clusters by k-means method based on annual and seasonal precipitation and temperature and to establish marginal probabilistic distributions for each drought property in each sub-region. Several copula types were selected to test the best fit distribution, and Student t copula was recognized as the best one to integrate drought duration, severity, and peak. The results indicated that a proper classification was important for a regional drought frequency analysis, and copulas were useful tools in exploring the associations of the correlated drought variables and analyzing drought frequency. Student t copula was a robust and proper function for drought joint probability and return period analysis, which is important for analyzing and predicting the regional drought risks.

Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate

Recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2=0.60 ( RMSE = 18.72 W m−2) for all 27 sites and R2=0.62 ( RMSE = 18.21 W m−2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 ( RMSE = 20.74 W m−2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.

Interactive Effects of Elevated CO2 Concentration and Irrigation on Photosynthetic Parameters and Yield of Maize in Northeast China

Maize is one of the major cultivated crops of China, having a central role in ensuring the food security of the country. There has been a significant increase in studies of maize under interactive effects of elevated CO2 concentration ([CO2]) and other factors, yet the interactive effects of elevated [CO2] and increasing precipitation on maize has remained unclear. In this study, a manipulative experiment in Jinzhou, Liaoning province, Northeast China was performed so as to obtain reliable results concerning the later effects. The Open Top Chambers (OTCs) experiment was designed to control contrasting [CO2] i.e., 390, 450 and 550 µmol·mol−1, and the experiment with 15% increasing precipitation levels was also set based on the average monthly precipitation of 5–9 month from 1981 to 2010 and controlled by irrigation. Thus, six treatments, i.e. C550W+15%, C550W0, C450W+15%, C450W0, C390W+15% and C390W0 were included in this study. The results showed that the irrigation under elevated [CO2] levels increased the leaf net photosynthetic rate (P n) and intercellular CO2 concentration (C i) of maize. Similarly, the stomatal conductance (G s) and transpiration rate (T r) decreased with elevated [CO2], but irrigation have a positive effect on increased of them at each [CO2] level, resulting in the water use efficiency (WUE) higher in natural precipitation treatment than irrigation treatment at elevated [CO2] levels. Irradiance-response parameters, e.g., maximum net photosynthetic rate (P nmax) and light saturation points (LSP) were increased under elevated [CO2] and irrigation, and dark respiration (R d) was increased as well. The growth characteristics, e.g., plant height, leaf area and aboveground biomass were enhanced, resulting in an improved of yield and ear characteristics except axle diameter. The study concluded by reporting that, future elevated [CO2] may favor to maize when coupled with increasing amount of precipitation in Northeast China.

Integration of Multinomial-Logistic and Markov-Chain Models to Derive Land-Use Change Dynamics

AbstractLand-use change reflects the relationship between human activities and environmental processes over time and space. Modeling land-use change dynamics is of particular environmental, social, and economic importance at regional scales. This paper develops a new approach to model land-use changes by making use of multiple categories that incorporate socioeconomic and environmental factors with multinomial logistic models and Markov chains to quantify the impact of these variables on land-use change dynamics for the years 1990, 2000, and 2010. Spatial autocorrelation and colinearity tests were utilised to screen the most suitable independent variables before modeling. The multinomial logistic model was evaluated by means of a likelihood ratio test and pseudo R2. A Markov transition matrix was then designed for integration with the multinomial logistic model to illustrate the temporal land-use change dynamics from 1990 to 2010 and to visualise the predicted land-use change map. The approach was calibra...

Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China’s Environment Satellite (HJ-1) Data

In this paper, we intent to use the remotely sensed MODerate resolution Imaging Spectroradiometer (MODIS) data and China’s Environment Satellite (HJ-1) data for extracting the corn cultivated area over a regional scale. The high resolution HJ-1 data was to extract corn distribution at a small scale class with Support Vector Machine (SVM). The mean Enhanced Vegetation Index (EVI) time series curve of corn from MODIS was derived for the reference area and validated in a larger area. The MODIS-EVI time series curve derived from the reference area instead of the MODIS-EVI time series curve derived from the study area after validation, which was taken as the standard MODIS-EVI time series curve in for generating a standard MODIS-EVI image of corn. The mean absolute distance (MAD) between the standard MODIS-EVI image of corn and the MODIS-EVI time series image was used to detect the maximum possible extent of corn distribution in the study area. The results showed that the overall accuracy of the method was 82.17xa0%, with commission and omission errors of 16.85 and 15.40xa0%, respectively; at the county level, the satellite-estimated corn area and statistical data were well correlated (R2u2009=u20090.85, Nu2009=u200950) for the whole Jilin Province. It indicated that the MODIS data integrated with higher spatial resolution of HJ-1 satellite data could be utilized to enhance the extraction accuracy of corn cultivated area at a larger scale.