Drought is one of the most serious natural disasters affecting global food security and human life. Traditional drought monitoring is based on the impact of water deficit on crop morphology. Due to the time lag of traditional drought monitoring based on vegetation index and land surface temperature, crop yield has been adversely affected when drought detection is delayed. Early drought warning is an effective way to prevent disasters and reduce damage. An early warning model, using the percentage of anomaly vegetation and fluorescence (PAVF) was proposed to determine drought risk level and development trends. The principle of PAVF is to analyze the change in the percentage of anomaly vegetation (PAV) using the normalized difference vegetation index (NDVI), from the moderate-resolution imaging spectroradiometer sensor, and percentage anomaly fluorescence (PAF), from the Global Ozone Monitoring Experiment. A standard score of seasonal cumulative NDVI and Sun-induced chlorophyll fluorescence based on the data from 2007 to 2017 was used as a proxy for the evaluation index. Because PAV and PAF have different sensitivities to monitoring vegetation physiological changes, these parameters can reflect the occurrence and development trend of drought from different perspectives. Drought risk analysis in Xilingol League of China in 2018 demonstrated that the drought warning model could depict the evolution process of agricultural drought and can efficiently predict the occurrence of drought in the next half month. The proposed PAVF provides an available and innovative way to address the complexity of agricultural drought warnings.
The Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting (SSCEDMF) is an important component of Chinese satellites earth observation system. The first stage of SSCEDMF is composed by “2+1” satellites. The 2 optical satellites (HJ-1-A and HJ-1-B) and 1 S band microwave satellite (HJ-1-C) were successful launched on September 6, 2008 and November 19, 2012 respectively. This article introduced SSCEDMF characteristic and the disaster reduction application system and satellites on-orbit test works, and also analyzed the application capacity in natural disasters included flood, ice flooding, wild fire, severely drought, snow disasters, large area landslide and debris flow, sea ice, earthquake recovering, desertification and plant diseases and insect pests. Furthermore, we show some cases of China’s and other countries’ new natural disasters forecasting, monitoring, assessment and recovery construction.
Tibetan Plateau serves as the sources of several big rivers such as Yangtze River and Yellow River. Due to the high
elevation of the plateau, it has profound thermal and dynamical influence on both local and global climate and
atmospheric circulation. Land surface temperature (LST) plays a significant role in climate change and glacier melting.
In this paper we present our study on mapping land surface temperature variations for the years 2005-2006 in the plateau
using MODIS satellite data. Since the plateau has a very rough ground surface that is difficult to estimate the necessary
parameters for the mapping, we have developed a practical approach for LST retrieval from the MODIS thermal band
data. The approach was alternated from the split window algorithm proposed by Qin et al. (2001) for NOAA-AVHRR
data. Detailed methods for atmospheric transmittance and ground emissivity have been presented in the paper. Results
from our study indicate that ground temperature in the plateau is featured with obvious spatial and temporal variations.
Generally the temperature in winter and spring is less than 0°C and it is also not very high in summer, due to the high
altitude. Because of topological form, Chaidamu Basin of the plateau has the highest temperature in summer, usually
high up to above 40°C. Our study provides an alternative to understand the environmental changes in the plateau that
shape significant impacts on atmospheric dynamics of East Asia and South Asia.
As an important pasture region, Tibet has about 82 million hectares of natural grassland, accounting for 68.11% of its
total territory. Above 90% of Tibetan grassland belongs to the types of alpine meadow steppe and alpine steppe with
highly nutritious forage plant. Animal husbandry constitutes a major part of agricultural economy in Tibet. It is believed
that snow disaster become a significant threat to the development of animal husbandry in Tibet. The disaster often
happens in winter and spring as a result of complicated mountainous features and mutable climatic conditions. Statistics
indicates that, on average, there is a slight snow disaster for each 3-year, a medium disaster within 5 to 6 years, and a big
disaster in 8-10 years. Large numbers of animals died of hungry and cold during the disaster period. Huge economic loss
due to the disaster had brought giant difficulties to local herdsmen in Tibet. Accurate and timely monitoring of snow
cover for snow disaster evaluating is very important to provide the required information for decision-making in
anti-disaster campaigns. Remote sensing has many advantages in snow disaster monitoring hence been extensively
applied as the main approach for snow cover monitoring. In this paper we present our study of snow cover monitoring
and snow disaster evaluating in Tibet. An applicable approach has been developed in the study for the monitoring and
evaluating. The approach is based on the normalize difference of snow index (NDSI) and DEM retrieved from MODIS
and GIS data. Using the approach, we analyzed the snowstorm occurring in mid-March 2007 in southern Tibet. Results
from our analysis indicated that the new approach is able to provide an accurate estimate of snow cover area and snow
depth in southern Tibet. Thus we may conclude that the approach can be used as an efficient alternative for snow cover
monitoring and snow disaster evaluating in Tibet.
Hulun Buir represents the best grassland in Inner Mongolia. Due to intensive anthropogenic activities especially unreasonable grazing, desertification has been an important environmental problem in the grassland. In the paper we intend to develop an applicable approach for desertification monitoring in the grassland. Since vegetation is the most essential factor of grassland and desertification actually implies the declination of vegetation in the grassland, an index indication desertification severity has been constructed from vegetation cover fraction. Using MODIS satellite data, we firstly computed NDVI and then computed vegetation cover rate in the grassland. The rate is consequently used to construct the desertification index (DI) for evaluation of desertification severity. Using precipitation and temperature data from 45 points, we validate the capability of DI in representing the severity of actual desertification in the grassland. The general accordance of precipitation and temperature with DI demonstrates the applicability of the proposed approach for desertification in the grassland. Using the approach, we analyzed the changes of desertification in the grassland in recent years. Results showed that desertification process in the grassland are accelerating in recent years, with rate of 1% annually. The acceleration of desertification implies that grassland ecosystem is under evolution of degradation in spite of rapid economic development in the region. Our study suggests that necessary measures should be urgently employed to protect the grassland from further desertification.
Land use/cover change (LUCC) has significant impacts on regional environment. Land surface temperature (LST) is an
important indicator for assessment of regional environment especially in big cities where urban heat island is very
obvious. In this study, remote sensing and geographic information systems (GIS) were used to detect LUCC for
assessment of its impacts on spatial variation of LST in Urumqi, a big city in northwestern China. Two Landsat
TM/ETM+ images respectively in 1987 and 2002 were examined for LUCC detection. LST and NDVI were computed
from the images for different land use/cover types. Impacts of LUCC on regional environment can be assessment
through LST difference during the period. Our results showed that land use/cover changes were very obvious in Urumqi
between 1987 and 2002 due to rapid expansion of the city. Urban/built-up land increased by almost twice in the period,
while the barren land, the forestland and water area declined. The increase of urban/built-up land was mainly from the
barren land. Spatial distribution of LST in the city has been highly altered as a result of urban expansion. The
urban/built-up area had LST increase by 4.48% during the period. The LST difference between built-up land and other
land use/cover types also significantly increased between 1978 and 2002, with high LST increase area corresponding to
the urban expansion regions. Moreover, changes of vegetation also had shaped many impacts on spatial variation of LST
in the city. We found that NDVI has a negative correlation with LST among the land use/cover types. This probably is
due to the ecological function of vegetation in cooling down the surface from high evapotranspiration. The study
demonstrated that combination of remote sensing and GIS provided an efficient way to examine LUCC for assessment of
its impacts on regional environment in big cities.
Rangeland in Inner Mongolia is an arid ecosystem with vulnerability. Anthropogenic activities especially over-grazing
have been believed to be a leading factor shifting the vulnerability into actual degradation in the ecosystem. Net primary
productivity (NPP) is an important indicator for vulnerability monitoring in arid ecosystem. In this study we use the
vegetation photosynthesis model to estimate NPP of rangeland ecosystem in Inner Mongolia. The objective is to examine
the spatial variation of NPP in Inner Mongolia and to highlight vulnerable areas for sustainable development. Several
improvements have been done to the model especially in its parameterization. Land surface temperature required by the
model was estimated from split window algorithm proposed for MODIS thermal band data. Using the MODIS image
data and the ground climate datasets, we applied the improved model to estimate the NPP in 2003 in Inner Mongolia.
Our results showed that mean NPP was 192.03gC m-2 Gr-1 in Inner Mongolia in 2003. Spatial variation of the NPP was
very obvious. Very low NPP was observed in the western parts while relatively high NPP could be seen in the eastern
and northeastern parts. For various type rangelands, temperate alpine meadow is the highest. Although the mean NPP of
temperate steppe is not high, its area is the largest in Inner Mongolia, so it has the highest ratio to total NPP. Comparison
of our NPP with similar studies from conventional methods confirms the accuracy of our estimation.
High spatial resolution ASTER data have 5 thermal bands, of which band 13 and 14 are especially suitable for land surface temperature (LST) estimation. Generally, LST retrieval from two thermal bands is done through so-called split window technique. In the past two decades above 17 split window algorithms have been proposed. However, such algorithm for ASTER data has not been reported, probably due to the new availability of the data for environmental application. In the study, a new split window algorithm has been developed for LST retrieval from ASTER data. Our algorithm only involves two essential parameters for LST retrieval while keeping the same accuracy as those having more parameters. Detailed derivation of the split window algorithm has been given in the paper, which including formulation of thermal radiation transfer equation, determination of algorithm constants, and estimation of the essential parameters. Comparison of our algorithm with the existing ones for validation of its accuracy and applicability in the real world indicates that our algorithm has an average root mean square (RMS) error of 0.67°C when transmittance has an error of 0.05 and emissivity has an error of 0.01. Thus we can conclude that our algorithm is a very good alternative for accurate LST retrieval from ASTER data. Application of the algorithm to Wuxi-Suchou region in Yangtze River Delta produces a very reseasonable LST image of the region, hence confirms the applicability of the algorithm.
Sangong waddi basin in the north piedmont of Tianshan Mountains is a typical inland arid ecosystem in China. Desert environment especially land cover and land use in the basin changes dramatically in recent decades under the anthropogenic impacts. In order to develop an approach to highlight the environmental changes, we present a case study in the paper to examine the effects of different vegetation indices to the spatial changes of desert environment in the basin using Moderate-resolution Imaging Spectroradiometer (MODIS) data. First we compute the different vegetation indices including Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) for the basin from MODIS data and then compare their applicability to indicate the seasonal changes and spatial variation of vegetation in the basin. The results show that when the two vegetation index EVI and NDVI were used at the same time in monitoring the desert vegetation situation, Smaller the difference value between their values were, less the human activities interfere. The vegetation gradient variation of the desert vegetation is distinct in the basin. Therefore EVI can be used to highlight vegetation growth over the alluvial fans while NDVI is suitable to monitor vegetation growth in the hilly regions. With this finding, we further develop an approach to examine the desert environment changes in the basin. Based on the examination, several policy recommendations have been proposed in the study for better administration and utilization of arid land resources in the basin.
Landsat TM has a thermal band (TM6) operating in 10.45-12.6mm, which can be used for land surface temperature (LST) retrieval. Land surface emissivity (LSE) is an essential parameter for LST retrieval. However, LSE information is generally no available for many applications. In this paper we intend to develop an applicable approach for LSE estimation so that LST can be retrieved from Landsat TM6 data. Spatial resolution of TM6 is 120m under nadir. Pixels under this scale can be viewed as composed of three land cover patterns for most natural surfaces: vegetation, bare soil/rock and water. Emissivities of these land cover patterns are relatively stable and well known, which enables us to propose a method for LSE estimation using the visible and near infrared (NIR) bands. The composition ratio of vegetation and bare soil or building under pixel scale can be estimated from bands 3 and 4 (TM3 and TM4). LSE for TM6 can then be estimated through thermal radiance equation with the composition ratio and the emissivities of the patterns known. The proposed methodology for LSE estimation is simple and easy to use, hence provides opportunity to promote the application of TM6 data to agriculture and environments. Finally we apply this methodology to Lingxian region of Shangdong Province in North China Plain, the most important agricultural region in China, for LSE estimation and LST retrieval, which has produced a reasonable estimation of thermal variation of the region.
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