Mapping the forage nitrogen, phosphorus, and potassium contents of alpine grasslands by integrating Sentinel-2 and Tiangong-2 data.

Nitrogen (N), phosphorus (P), and potassium (K) contents are crucial quality indicators for forage in alpine natural grasslands and are closely related to plant growth and reproduction. One of the greatest challenges for the sustainable utilization of grassland resources and the development of high-quality animal husbandry is to efficiently and accurately obtain information about the distribution and dynamic changes in N, P, and K contents in alpine grasslands. A new generation of multispectral sensors, the Sentinel-2 multispectral instrument (MSI) and Tiangong-2 moderate-resolution wide-wavelength imager (MWI), is equipped with several spectral bands suitable for specific applications, showing great potential for mapping forage nutrients at the regional scale. This study aims to achieve high-accuracy spatial mapping of the N, P, and K contents in alpine grasslands at the regional scale on the eastern Qinghai-Tibet Plateau. The Sentinel-2 MSI and Tiangong-2 MWI data, coupled with multiple feature selection algorithms and machine learning models, are applied to develop forage N, P, and K estimation models from data collected at 92 sample sites ranging from the vigorous growth stage to the senescent stage. The results show that the spectral bands of both the Sentinel-2 MSI and Tiangong-2 MWI have an excellent performance in estimating the forage N, P, and K contents (the R2 values are 0.68-0.76, 0.54-0.73, and 0.74-0.82 for forage N, P, and K estimations, respectively). Moreover, the model integrating the spectral bands of these two sensors explains 78%, 74%, and 84% of the variations in the forage N, P, and K contents, respectively. These results indicate that the estimation ability of forage nutrients can be further improved by integrating Tiangong-2 MWI and Sentinel-2 MSI data. In conclusion, integration of the spectral bands of multiple sensors is a promising approach to map the forage N, P, and K contents in alpine grasslands with high accuracy at the regional scale. This study offers valuable information for growth monitoring and real-time determination of forage quality in alpine grasslands.

The urgent need to develop a new grassland map in China: based on the consistency and accuracy of ten land cover products.

Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not been updated for almost 40 years. Therefore, a reliable map depicting the current spatial distribution of grasslands across the country is urgently needed. In this study, we evaluated the grassland consistency and accuracy of ten land cover datasets (GLC2000, GlobCover, CCI-LC, MCD12Q1, CLUD, GlobeLand30, GLC-FCS30, CGLS-LC100, CLCD, and FROM-GLC) for 2000, 2010, and 2020 based on extensive fieldwork. We concluded that the area of these ten grassland products ranges from 107.80×104 to 332.46×104 km2, with CLCD and MCD12Q1 having the highest area consistency. The spatial and sample consistency is highest in the regions of east-central Inner Mongolia, the Qinghai-Tibet Plateau and northern Xinjiang, while the distribution of southern grasslands is scattered and differs considerably among the ten products. MCD12Q1 is significantly more accurate than the other nine products, with an overall accuracy (OA) reaching 77.51% and a kappa coefficient of 0.51; CLCD is slightly less accurate than MCD12Q1 (OA=73.02%, kappa coefficient=0.45) and is more conducive to the fine monitoring and management of grassland because of its 30-meter resolution. The highest accuracy of grassland was found in the Inner Mongolia-Ningxia region and Qinghai-Tibet Plateau, while the accuracy was worst in the southeastern region. In the future grassland mapping, cartographers should improve the accuracy of the grassland distribution in South China and regions where grassland is confused with forest, cropland and bare land. We specify the availability of valuable data in existing land cover datasets for China's grasslands and call for researchers and the government to actively produce a new generation of grassland maps.

Spatiotemporal dynamics of grassland aboveground biomass and its driving factors in North China over the past 20 years.

Although remote sensing has enabled rapid monitoring of grassland aboveground biomass (AGB) at a regional scale, it is still a difficult challenge to construct an accurate estimation model of grassland AGB in a vast region to support the AGB dynamics analysis over a long time series. In this study, extensive grassland AGB measurements (collected in North China during the grassland growing season of 2000-2019), MODIS data, and environmental factors (climate, topography and soil) were employed to construct the grassland AGB models using four machine learning algorithms (random forest, support vector machine, artificial neural network and extreme learning machine) combined with four variable selections. The spatial distributions of annual grassland AGB from 2000 to 2019 were simulated based on the optimal AGB model. The temporal change and future trend of AGB series from 2000 to 2019 were comprehensively analyzed by the slope model and Hurst exponent. The influences of natural and anthropogenic factors on grassland AGB dynamics were explored quantitatively using the Geodetector model. The results showed that (1) the random forest model constructed from the variables selected by the successive projections algorithm is the optimal grassland AGB model. (2) The 20-year average grassland AGB in North China showed an overall spatial distribution of being low in the central and western parts and high in the southeastern part. (3) The annual maximum grassland AGB in most regions (82.71%) showed an increasing trend during 2000-2019; and most of the grasslands with a decreasing trend of AGB were located in regions with low AGB values and arid climates. (4) The future trend of grassland AGB after the study period may be optimistic, as reflected by more grassland AGB was predicted to increase rather than decrease (70.38% vs. 29.62%). (5) The main driving factors of spatiotemporal dynamics of grassland AGB were precipitation, soil type, and livestock density; the interactive influence of two drivers on AGB showed mutual enhancement.

Satellite-derived NDVI underestimates the advancement of alpine vegetation growth over the past three decades.

Satellite-derived normalized difference vegetation index (NDVI) data are increasingly relied on to reveal the growth responses of vegetation to climate change, yet the vegetation growth tracking accuracy of these data remains unclear due to a lack of long-term field data. Here, we adopted a unique field-measured seasonal aboveground biomass dataset from 1982-2014 to assess the potential of using satellite-derived NDVI data to match field data in regard to the interannual variability in seasonal vegetation growth in a Tibetan alpine grassland. We revealed that Global Inventory Monitoring and Modeling System (GIMMS) NDVI data captured the advancement of field-measured vegetation growth throughout the entire study period but not from 2000-2014, while MODIS NDVI data still observed this advancing trend after 2000 to a limited extent. However, satellite-derived NDVI data consistently underestimated the advancement degree of field-measured vegetation growth, regardless of whether GIMMS or MODIS NDVI data were considered. We tentatively attribute this underestimation to an increased ratio of grass biomass to forb biomass, which could delay the advancement of NDVI development but not affect that of field-measured biomass development. Our results suggest that satellite-derived NDVI data may miss critical responses of vegetation growth to global climate change, potentially due to long-term shifts in plant community composition.

Publisher Correction: Grassland dynamics in response to climate change and human activities in Xinjiang from 2000 to 2014.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

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.

[Early warning and risk assessment of snow disaster in pastoral area of northern Xinjiang].

Based on the analysis of animal husbandry production and of distribution characteristics of snow disaster in northern Xinjiang, and by using RS and GIS techniques and field survey data, 9 early warning factors were selected from the three subsystems of grassland' s disaster-resistant capability, livestock's disaster-bearing capacity, and disaster-causing potential, and the death rate of livestock caused by snow disaster was used as a factor of risk assessment. An index system of snow disaster' s early warning and risk assessment for completely grazing grassland was established, and the early warning model of snow disaster, its distinguishing model, and risk assessment model were built by using multi-hierarchical synthetic and multi-objective linear weight function methods to predict the resistant capability of grassland and livestock against snow disaster, and to assess the potential risk loss from snow disaster in northern Xinjiang. The accuracy of the early warning model and risk assessment model was 85% and 72% , respectively.

[Establishment of snow disaster remote sensing monitoring and damage estimation systems in Altai pastoral region of Xinjiang].

It is of significance to establish an integrated evaluation system of snow disaster in northern pastoral areas. Based on the NOAA satellite digital images, field observation data, and maps of grassland type and seasonal pastureland, this paper selected the winter and spring pasturelands in Aletai region of Xinjiang as the main area of snow disaster-remote sensing monitoring. With affecting factors of economy and the characteristics of natural resource distribution comprehensively analyzed, and using 3S techniques and field survey information, a fundamental information processing model for integrated evaluation of snow disaster was built up, and snow disaster-spatial evaluation indices and damage level systems were constructed. Natural and social systems and 20 indices were selected in snow disaster evaluation indicator system. Four principal factors, i.e., snow cover area, snow depth on grassland, persistence days of low temperature, and livestock death rate, were used as the grading indices of snow disaster damage level, and the models of snow disaster identification and loss estimatation were set up to quantitatively analyze snow disaster. The results indicated that the system could accurately reflect the details of snow hazard grade and the situation of a disaster in temporal and spatial scales, which would help to carry out the dynamic monitoring and scientific estimatation of big area's snow disaster in pastoral region.

[Snow disaster in Aletai Region--its remote sensing monitoring model and evaluation method].

In this paper, a monitoring model of snow depth was built based on the 4 scenes of NOAA satellite digital images under sunshiny condition and the corresponding ground observation data from 20 meteorological stations during 2 snow disasters from 1996 to 1997 in North Xinjiang. The pixel-based snow coverage rate and snow spatial classification were studied by using linear mixture spectrum disassembling method, and two grid data layers based quantified indices used for estimating snow hazard grade of grassland and animal husbandry were put forward. The results indicated that by using the snow monitoring model and linear mixture spectrum disassembling method, the image cell based snow depth and snow coverage rate could be calculated, and the precision of snow classification could be improved. The image cell based snow hazard index could systematically express the spatial distribution of snow, grass, animal and climate conditions, and reflect the snow hazard grade of grassland and animal husbandry.

[Features of grassland resources and their classified management in Alatai region of Xinjiang].

Grassland degradation, an important problem in grassland management, has affected the sustainable development of animal husbandry in Alatai region of Xinjiang. Based on the analysis of the community features, ecological service value and multiple functions, and main function of grassland types in the regional development, the classified management for grassland was designed. The results showed that Alatai grassland could be divided into three sectors, i.e., ecological function region, economic function region and mixed function region. The ecological function region made up 164.66 x 10(4) hm2 or 16.73% of total Alatai grassland, and the grassland types included alpine meadow, alpine rangeland, swamp, mountain desert steppe, mountain steppe desert and part of plain desert. For the ecological function region, the main management strategy was to prohibit grazing and cropping. The economic function region of 116.33 x 10(4) hm2 accounted for 11.82% of the total Alatai grassland, the grassland types included mountain meadow, flat meadow and meadow steppe, and the main management strategy to enhance the productivity was agricultural measures, such as fertilization and irrigation. The mixed function region which included mountain steppe, plain desert steppe and most part of plain desert was 703.21 x 10(4) hm2 or 71.45% of total Alatai grassland, and the main management strategy was rotational grazing.