Guozhuang Shen

Lake variations in response to climate change in the Tibetan Plateau in the past 40 years

The Qinghai-Tibetan Plateau plays an important role in global climate and environmental change and holds the largest lake area in China, with a total surface area of 36,900 km2. The expansion and shrinkage of these lakes are critical to the water cycle and ecological and environmental systems across the plateau. In this paper, surface areas of major lakes within the plateau were extracted based on a topographic map from 1970, and Landsat MSS, TM and ETM+ satellite images from the 1970s to 2008. Then, a multivariate correlation analysis was conducted to examine the relationship between the changes in lake surface areas and the changes in climatic variables including temperature, precipitation, evaporation, and sunshine duration. Initial results suggest that the variations in lake surface areas within the plateau are closely related to the warming, humidified climate transition in recent years such as the rise of air temperature and the increase in precipitation. In particular, the rising temperature accelerates melting of glaciers and perennial snow cover and triggers permafrost degradation, and leads to the expansion of most lakes across the plateau. In addition, different distributions and types of permafrost may cause different lake variations in the southern Tibetan Plateau.

Patterns and potential drivers of dramatic changes in Tibetan lakes, 1972-2010.

Most glaciers in the Himalayas and the Tibetan Plateau are retreating, and glacier melt has been emphasized as the dominant driver for recent lake expansions on the Tibetan Plateau. By investigating detailed changes in lake extents and levels across the Tibetan Plateau from Landsat/ICESat data, we found a pattern of dramatic lake changes from 1970 to 2010 (especially after 2000) with a southwest-northeast transition from shrinking, to stable, to rapidly expanding. This pattern is in distinct contrast to the spatial characteristics of glacier retreat, suggesting limited influence of glacier melt on lake dynamics. The plateau-wide pattern of lake change is related to precipitation variation and consistent with the pattern of permafrost degradation induced by rising temperature. More than 79% of lakes we observed on the central-northern plateau (with continuous permafrost) are rapidly expanding, even without glacial contributions, while lakes fed by retreating glaciers in southern regions (with isolated permafrost) are relatively stable or shrinking. Our study shows the limited role of glacier melt and highlights the potentially important contribution of permafrost degradation in predicting future water availability in this region, where understanding these processes is of critical importance to drinking water, agriculture, and hydropower supply of densely populated areas in South and East Asia.

Object oriented method for detection of inundation extent using multi-polarized synthetic aperture radar image

With the all-weather and day/night imaging capability, synthetic aperture radar (SAR) plays an important role in inundation extent detection. The inundation area detection using SAR will be easy as a result of the dark image tones yielded by specular reflection to the radar wave. Object oriented method (OOM) was applied to detect inundation extent using multi-polarized ENVISAT ASAR data. The traditional pixel-based methods used in information extraction and classification focus on the single pixel, so when they are applied in SAR imagery no perfect results can be achieved because of the speckle of SAR imagery. On the other hand, the pixel-based methods have limitations for detecting inundation extent and flood monitoring because of the neglect of the information of the adjacent pixels. The OOM, which no longer looks at individual pixels, but rather homogeneity areas (image objects), would be much more effective. In this paper, the OOM is applied in the ENVISAT ASAR alternative polarized (VV/VH) images using the software eCognition. The study site is located in Poyang Lake wetland, which has different inundation extent at different time. The images were segmented firstly, then the standard nearest neighbor classifier and the membership function classifier were used to classify the image objects, finally the different inundation areas were detected. The classification accuracies for two classifiers from the OOM are 95.78% and 92.24%, which are higher than that of the maximum likelihood classifier, 86.02%.

Monitoring lake-level changes in the Qinghai–Tibetan Plateau using radar altimeter data (2002–2012)

Abstract Lake-level change can be an important indicator for the water balance in the Qinghai–Tibetan Plateau (QTP). However, it is not feasible to perform continuous in-situ measurements for a large number of lakes because of the remoteness and harsh climatic conditions on this plateau. Satellite altimetry has been successfully used for monitoring water-level changes of inland lakes in recent years. In this study, water-level changes between 2002 and 2012 of 51 lakes on the QTP were monitored using multialtimeter data from Envisat/RA-2, Cryosat-2/Siral, Jason-1/Poseidon-2, and Jason-2/Poseidon-3. The water levels of 42 of the lakes have a mean rising trend of 0.275     m / a , whereas the water levels of nine lakes have a mean decreasing trend of − 0.144     m / a . Overall, the water level of these lakes had a mean increasing trend of 0.201       m / a in the past 10 years. For the lakes distributed over the entire plateau, it was found that the lake levels in different basins had different change characteristics: the lake levels in the southern plateau show a decreasing trend, whereas lake levels in the northern plateau show an increasing trend. In the central plateau, the water levels of most lakes also show an increasing trend but the water levels of a small number of the lakes show a decreasing trend. In addition, the winter or summer linear trends for the levels of individual lakes appear to be opposite to the trends observed for the yearly average. The combined use of data from several different altimeters makes the temporal resolution of lake-level change measurements higher than for those results derived using only one kind of altimeter data. However, the number of lakes monitored in this study is affected by the footprints of satellites orbits on the Earth’s surface. After taking into account anomalous lake levels, systematic elevation differences, and periodic changes in the water level, the biggest root-mean-square-error for the lakes monitored in this study is < 40     cm / a showing that the results have a high degree of accuracy. Lake-level change is mainly related to rising temperatures, increasing precipitation, and decreasing evaporation. In particular, rising temperatures accelerate the melting of glaciers and perennial snow cover and trigger permafrost degradation, leading to an increase in the water level of most lakes across the plateau.

Biomass estimation of wetland vegetation in Poyang Lake area using ENVISAT advanced synthetic aperture radar data

Abstract Biomass estimation of wetlands plays a role in understanding dynamic changes of the wetland ecosystem. Poyang Lake is the largest freshwater lake in China, with an area of about 3000     km 2 . The lake’s wetland ecosystem has a significant impact on leveraging China’s environmental change. Synthetic aperture radar (SAR) data are a good choice for biomass estimation during rainy and dry seasons in this region. In this paper, we discuss the neural network algorithms (NNAs) to retrieve wetland biomass using the alternating-polarization ENVISAT advanced synthetic aperture radar (ASAR) data. Two field measurements were carried out coinciding with the satellite overpasses through the hydrological cycle in April to November. A radiative transfer model of forest canopy, the Michigan Microwave Canopy Scattering (MIMICS) model, was modified to fit to herbaceous wetland ecosystems. With both ASAR and MIMICS simulations as input data, the NNA-estimated biomass was validated with ground-measured data. This study indicates the capability of NNA combined with a modified MIMICS model to retrieve wetland biomass from SAR imagery. Finally, the overall biomass of Poyang Lake wetland vegetation has been estimated. It reached a level of 1.09 × 10 9 , 1.86 × 10 8 , and 9.87 × 10 8     kg in April, July, and November 2007, respectively.

Poyang Lake wetland vegetation biomass inversion using polarimetric RADARSAT-2 synthetic aperture radar data

Abstract. Poyang Lake is the largest freshwater lake in China and one of the most important wetlands in the world. Vegetation, an important component of wetland ecosystems, is one of the main sources of the carbon in the atmosphere. Biomass can quantify the contribution of wetland vegetation to carbon sinks and carbon sources. Synthetic aperture radar (SAR), which can operate in all day and weather conditions and penetrate vegetation to some extent, can be used to retrieve information about vegetation structure and the aboveground biomass. In this study, RADARSAT-2 polarimetric SAR data were used to retrieve aboveground vegetation biomass in the Poyang Lake wetland. Based on the canopy backscatter model, the vegetation backscatter characteristics in the C-band were studied, and a good relation between simulated backscatter and backscatter in the RADARSAT-2 imagery was achieved. Using the backscatter model, pairs of training data were built and used to train the back propagation artificial neural network. The biomass was retrieved using this ANN and compared with the field survey results. The root-mean-square error in the biomass estimation was 45.57  g/m2. This shows that the combination of the model and polarimetric decomposition components can efficiently improve the inversion precision.

Study of RADARSAT-2 synthetic aperture radar data for observing sensitive factors of global environmental change

Abstract Global environmental change has gained widespread global attention. It is a complex system with special spatial and temporal evolutionary characteristics. Sensitive factors are indicators of global environmental change, and some can be observed with Earth observation technology. RADARSAT-2 is capable of polarimetric and interferometric observations, which can provide an effective way to document some sensitive factors of global environmental change. This study focuses on the usage of RADARSAT-2 data for observing sensitive factors of environmental change and building highly accurate application models that connect synthetic aperture radar data and observable sensitive factors. These include (1) extracting spatiotemporal distribution of large-scale alluvial fan, (2) extracting vegetation vertical structure, (3) detecting urban land cover change, and (4) monitoring seasonal floods. From this study, RADARSAT-2 data have been demonstrated to have excellent capabilities in documenting several sensitive factors related to global environmental change.

Neural network algorithm and backscattering model for biomass estimation of wetland vegetation in Poyang Lake area using Envisat ASAR data

Poyang Lake is the largest freshwater lake in China with an area of about 3000 km2. Its wetland ecosystem has a significant impact on Chinas environment change. In this paper, we discuss the neural network algorithms (NNA) to retrieve wetland vegetation biomass using the alternating polarization Envisat ASAR data. Two field measurements were carried out coincident with the satellite overpasses at this area through the hydrological cycle from April and November. Training data of the neural network are generated by the Michigan Microwave Canopy Scattering (MIMICS) model which is often used for the tree canopy. We modified the model to make it applicable to herbaceous wetland ecosystems. The model input parameters are defined according to the wetland circumstance. NNA retrieval results are validated with ground measured data. The inversion results show the NNA combined with MIMICS model is capable of performing the retrieval with good accuracy. Finally, the trained neural network is used to estimate the overall biomass of Poyang Lake wetland vegetation.

Detection of land surface change due to the Wenchuan earthquake using multitemporal advanced land observation satellite-phased array type L-band synthetic aperture radar data

A strong earthquake, with a magnitude of 8.0, hit in the Wenchuan area of China on May 12, 2008, resulting in significant changes to the land surface. Remote sensing (RS), especially synthetic aperture radar (SAR) RS technology can play a key role in detecting changes of the land surface. In the present paper, multitemporal Advanced Land Observation Satellite- Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) data acquired on Feb. 17 and May 19, 2008 were used to analyze the land surface changes caused by the Wenchuan earthquake. The characteristics of the land surface in several sites hit by the earthquake are presented, by comparison of pre- and post-seismic images. Subsequently, the land surface changes caused by the earthquake were extracted using change detection and classification methods. To this end, land surface change detection can provide a detailed basis for assessing an earthquakes impact.

Change vector analysis method for inundation change detection using multi-temporal multi-polarized SAR images

With the all-weather and day-night imaging capability, synthetic aperture radar (SAR) plays an important role in inundation extent change detection. Inundation extent change detection using SAR will be easy as a result of the dark image tones yielded by specular reflection. Change vector analysis (CVA) method, an effective change detection method, is also a valuable inundation extent change detection method. In CVA method, change magnitude and change direction can be generated separately, which can be used to determine change areas and change types. CVA method also has the ability to process any number of spectral bands and to produce detailed change information. In this paper, CVA method was applied to inundation extent change detection using multi-temporal multi-polarization ENVISAT ASAR alternative polarization images acquired on 2004-08-29, 2004-12-12 and 2005-03-27. The test site is located in Poyang Lake wetland, where land surface had different inundation extent when images were acquired. Firstly these 3 phases of images were registered together. Then the change vectors were calculated using these images. After that change magnitude and direction cosine images were produced. At last the change areas and the corresponding change type were extracted separately using decision tree method. The result indicates that CVA method has potential utility in inundation extent change detection.