Qisheng Feng

Toward Improved Daily Cloud-Free Fractional Snow Cover Mapping with Multi-Source Remote Sensing Data in China

With the high resolution of optical data and the lack of weather effects of passive microwave data, we developed an algorithm to map daily cloud-free fractional snow cover (FSC) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard daily FSC product, the Advanced Microwave Scanning Radiometer (AMSR2) snow water equivalent (SWE) product and digital elevation data. We then used the algorithm to produce a daily cloud-free FSC product with a resolution of 500 m for regions in China. In addition, we produced a high-resolution FSC map using a Landsat 8 Operational Land Imager (OLI) image as a true value to test the accuracy of the cloud-free FSC product developed in this study. The analysis results show that the daily cloud-free FSC product developed in this study can completely remove clouds and effectively improve the accuracy of snow area monitoring. Compared to the true value, the mean absolute error of our product is 0.20, and its root mean square error is 0.29. Thus, the synthesized product in this study can improve the accuracy of snow area monitoring, and the obtained snow area data can be used as reliable input parameters for hydrological and climate models. The land cover type and terrain factors are the main factors that limit the accuracy of the daily cloud-free FSC product developed in this study. These limitations can be further improved by improving the accuracy of the MODIS standard snow product for complicated underlying surfaces.

Modelling global-scale potential grassland changes in spatio-temporal patterns to global climate change

Grassland is one of the most widespread vegetation types worldwide and plays a significant role in global carbon cycling. Understanding the sensitivity of grassland to climate change and the effect of climate changes on the grassland ecosystems is a key issue in global carbon cycling. One of the goals of this study was to evaluate the three net primary productivity (NPP)–climate models, i.e. the Miami model, the Schuur model and the classification indices-based model. Results indicated that the classification indices-based model was the most effective model at estimating large-scale grassland NPP. In this research, changes in the spatial pattern of global potential grassland from recent past (1950–2000) to future (2001–2050) A2a scenario were analysed with the integrated orderly classification system of grassland (IOCSG) approach in a Geographic Information System (GIS) environment. NPP was evaluated with the classification indices-based model. Results indicate that under recent past climatic conditions, the main parts of global grassland are the savanna and tundra and alpine grassland and will be converted into the savanna, steppe and semi-desert grassland in A2a scenario. As a whole, areas of grassland will increase by 31.76 million hectares. The classification indices-based model estimated a 12.40% increase of total NPP in grassland from recent past to A2a scenario. It will impose a new issue for future grassland researches to support sustainable development and to provide action relevant knowledge to meet the challenge of climate change.

Outburst Flooding of the Moraine-Dammed Zhuonai Lake on Tibetan Plateau: Causes and Impacts

The Kekexili region of the Tibetan Plateau has become warmer and wetter since the 1960s, resulting in a significant expansion of Zhuonai Lake (+0.46 km2/year, p <; 0.05) before an outburst flood event occurred on September 15, 2011, and mapped by the Chinese Huanjing (HJ)-A/B satellites with a two-day revisit ability and a 360-km orbit swath. The direct cause of the outburst was due to relatively heavy precipitation from May to September 2011, specifically the continuous rainfall from later August to middle September. Two nearby earthquakes that occurred two months before the outburst might have impacted the natural structure of the lakebed and moraine dam to accelerate the outburst. The outburst event of Zhuonai Lake caused large environmental impacts on the region: 1) the desertification of the exposed lakebed of Zhuonai Lake; 2) the significant expansion of the three downstream lakes Kusai, Haidingnuoer, and Salt Lakes that not only caused the grassland reduction and deteriorations but also the potential threat to the operations of the Qing-Tibet Railway and Highway; and 3) the calving relocation of Tibetan antelopes to the shore area of Kusai Lake due to the deep cutting riverbanks caused by the overflow of Zhuonai Lake. This study provides some scientific clues or alerts for local or central governments to pay some attention on this very issue so that possible future devastative disasters and environmental damages would be avoided or mitigated.

Evaluation of Remote Sensing Inversion Error for the Above-Ground Biomass of Alpine Meadow Grassland Based on Multi-Source Satellite Data

It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013–2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVIMOD, NDVICCD and NDVIOLI, respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky–Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVIOLI, with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%–36.4% and 5.3%–29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.

Interrelation among climate factors, snow cover, grassland vegetation, and lake in the Nam Co basin of the Tibetan Plateau

Abstract Taking Nam Co basin as an example, we examine the interrelationship among vegetation growth, lake expansion, snow cover, and climate change, based on meteorological data and multisource remote sensing datasets. Results show that the climate has become warmer and wetter during the period of time from 1961 to 2010, with rates of + 0.04 ° C / year ( P < 0.001 ) for annual mean temperature and + 1.66     mm / year ( P = 0.007 ) for annual precipitation, while the snow-covered index experienced a decreasing trend ( − 31.94     km 2 · day / year , P = 0.129 ) from 2003 to 2010. In response, the vegetation growth was deteriorative in most parts of the basin. Conversely, both the lake’s area and water level increased ( + 2.15     km 2 / year and + 0.12     m / year , respectively). Although the enhanced vegetation index in the basin negatively correlates well with the lake’s area ( R 2 = 0.75 , P = 0.001 ), the correlation shows gradual decrease as distance away from the lake’s shoreline, from 25 km (zone A), to 25–50 km (zone B), and to 50–95 km (zone C). Two main factors might have contributed to this: (1) lake expansion buried grassland vegetation in zone A and (2) more gravel buildup and soil erosion due to runoff from snow melted water in zone A than in zones B and C. This study provides a scientific basis for the evaluation of changes in alpine grassland, lake, snow cover, and their responses to climate change.

Spatio-temporal dynamics on the distribution, extent, and net primary productivity of potential grassland in response to climate changes in China

Net primary productivity (NPP) of grassland is one of the key components in measuring the carrying capacity of livestock. Not only are grassland researchers concerned with the performance of NPP simulation models under current climate conditions, they also need to understand the behaviour of NPP–climate models under projected climatic changes. One of the goals of this study was to evaluate the three NPP–climate models: the Miami Model, the Schuur Model, and the Classification Indices-based Model. Results indicated that the Classification Indices-based Model was the most effective model at estimating large-scale grassland NPP. Both the Integrated Orderly Classification System of Grassland and the Classification Indices-based Model were then applied to analyse the succession of grassland biomes and to measure the change in total NPP (TNPP) of grassland biomes from the recent past (1950–2000) to a future scenario (2001–2050) in a geographic information system environment. Results of the simulations indicate that, under recent-past climatic conditions, the major biomes of China’s grassland are the tundra and alpine steppe, and steppe, and these would be converted into steppe and semi-desert grassland in the future scenario; the potential grassland TNPP in China was projected to be 0.72 PgC under recent-past climatic conditions, and would be 0.83 Pg C under the future climatic scenario. The ‘safe’ carrying capacity of livestock that best integrates a wide range of factors, such as grassland classes, climatic variability, and animal nutrition, is discussed as unresolved. Further research and development is needed to identify the regional trends for the ‘safe’ carrying capacity of livestock to maintain sustainable resource condition and reduce the risk of resource degradation. This important task remains a challenge for all grassland scientists and practitioners.

Spatio-temporal change of vegetation on Tibetan Plateau based on AVHRR-NDVI data

Long time series of vegetation dynamics is one of the core study areas for evaluating terrestrial ecosystems, and it is meaningful in global change research. In this study, the characteristics of climate change and dynamics of vegetation were analyzed systematically on Tibetan Plateau (TP) from 1981 to 2010. The results show that: 1) The annual average temperature and annual precipitation of the Tibetan Plateau increased by 0.7 °C and 12.4 mm every 10 years during 1981 to 2010, respectively. It had a significant increasing trend in temperature and precipitation over the past 30 years, but it would be warming and drying in the west, and warming and humid in the most of eastern areas in the future. 2) The spatial distribution of NDVI had remarkable longitude zonality that presented a stairs type to continuously rise from west to east. 3) In overall, the ecological environment of vegetation on the TP in the recent 30 years has been improved in the most areas, but degraded in part of local areas. About 33.91% vegetation had no significant change, the restoration area of vegetation (36.81%) was greater than that of the degradation area (29.28%).

Grassland dynamics in response to climate change and human activities in Xinjiang from 2000 to 2014

Climate change and human activities are two key factors that affect grassland ecosystem. Accurately estimating the effects of these two factors on grassland dynamics and understanding the driving forces of the dynamics are important in controlling grassland degradation. In this study, the potential Net Primary productivity (NPPP) and the difference between NPPP and actual NPP (NPPA) are used as indicators of climate change and human activities on grassland ecosystem in Xinjiang. An overall grassland NPPA increase than decrease (69.7% vs 30.3%) is found over the study period of 2000 to 2014. While human activities played a dominant role for such a NPPA increase, both human activities and climate change contributed almost equally to the grassland NPPA decrease. Within the three types of grasslands in Xinjiang, the desert grassland showed the greatest NPPA increasing trend that mostly attributed to human activities; the meadow showed an overall NPPA decreasing trend that was mainly caused by human activities; the steppe showed similar NPPA decreasing and increasing trend in terms of area percentage. Based on this study, our recommendations are (1) to continue the grazing prohibition policy in desert grassland and (2) to extensively implement the rest grazing policy in steppe and meadow grasslands.

Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015)

Multi-source remote sensing data were used to generate 500-m resolution cloud-free daily snow cover images for the Northern Hemisphere. Simultaneously, the spatial and temporal dynamic variations of snow in the Northern Hemisphere were evaluated from 2000 to 2015. The results indicated that (1) the maximum, minimum, and annual average snow-covered area (SCA) in the Northern Hemisphere exhibited a fluctuating downward trend; the variation of snow cover in the Northern Hemisphere had well-defined inter-annual and regional differences; (2) the average SCA in the Northern Hemisphere was the largest in January and the smallest in August; the SCA exhibited a downward trend for the monthly variations from February to April; and the seasonal variation in the SCA exhibited a downward trend in the spring, summer, and fall in the Northern Hemisphere (no pronounced variation trend in the winter was observed) during the 2000–2015 period; (3) the spatial distribution of the annual average snow-covered day (SCD) was related to the latitudinal zonality, and the areas exhibiting an upward trend were mainly at the mid to low latitudes with unstable SCA variations; and (4) the snow reduction was significant in the perennial SCA in the Northern Hemisphere, including high-latitude and high-elevation mountainous regions (between 35° and 50°N), such as the Tibetan Plateau, the Tianshan Mountains, the Pamir Plateau in Asia, the Alps in Europe, the Caucasus Mountains, and the Cordillera Mountains in North America.

Modeling of Alpine Grassland Cover Based on Unmanned Aerial Vehicle Technology and Multi-Factor Methods: A Case Study in the East of Tibetan Plateau, China

Grassland cover and its temporal changes are key parameters in the estimation and monitoring of ecosystems and their functions, especially via remote sensing. However, the most suitable model for estimating grassland cover and the differences between models has rarely been studied in alpine meadow grasslands. In this study, field measurements of grassland cover in Gannan Prefecture, from 2014 to 2016, were acquired using unmanned aerial vehicle (UAV) technology. Single-factor parametric and multi-factor parametric/non-parametric cover inversion models were then constructed based on 14 factors related to grassland cover, and the dynamic variation of the annual maximum cover was analyzed. The results show that (1) nine out of 14 factors (longitude, latitude, elevation, the concentrations of clay and sand in the surface and bottom soils, temperature, precipitation, enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI)) exert a significant effect on grassland cover in the study area. The logarithmic model based on EVI presents the best performance, with an R2 and RMSE of 0.52 and 16.96%, respectively. Single-factor grassland cover inversion models account for only 1–49% of the variation in cover during the growth season. (2) The optimum grassland cover inversion model is the artificial neural network (BP-ANN), with an R2 and RMSE of 0.72 and 13.38%, and SDs of 0.062% and 1.615%, respectively. Both the accuracy and the stability of the BP-ANN model are higher than those of the single-factor parametric models and multi-factor parametric/non-parametric models. (3) The annual maximum cover in Gannan Prefecture presents an increasing trend over 60.60% of the entire study area, while 36.54% is presently stable and 2.86% exhibits a decreasing trend.