scholarly journals Spatio-Temporal Grassland Development in Inner Mongolia after Implementation of the First Comprehensive Nation-Wide Grassland Conservation Program

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 38
Author(s):  
Zhichao Xue ◽  
Martin Kappas ◽  
Daniel Wyss
Keyword(s):  
Inner Mongolia ◽  
Net Primary Productivity ◽  
Positive Impact ◽  
Meteorological Data ◽  
Impact Factors ◽  
Desert Grassland ◽  
Major Step ◽  
Grassland Conservation ◽  
Spatio Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Protection of the grassland’s ecological environment and improvement of people’s livelihoods are major tasks for the management of pastoral areas in Inner Mongolia. The comprehensive program for grassland conservation in China, the Subsidy and Incentive System for Grassland Conservation (SISGC), was launched in 2011. To comprehend the effects of this major step towards sustainable grassland development, this study focuses on the spatio-temporal development of grasslands in Inner Mongolia since 2011. Through the combination of MODIS (Moderate-resolution Imaging Spectroradiometer) satellite data with up to date meteorological data, we used the indicators of Fractional Vegetation Cover (FVC) and Net Primary Productivity (NPP) to analyze qualitative and quantitative grassland changes. A classification system on the pixel level, reflecting change trends and fluctuations of both FVC and NPP, was applied to monitor and analyze the grassland development from 2011 to 2019. In particular, the spatial transfer matrix of the recent two years (2018 to 2019) was analyzed to reveal the latest potential issues and random impact factors. The results show a positive overall but spatially unbalanced effect of SISGC, with a prominent positive impact in the semi-desert grassland area. The potential threats from both social and natural aspects as well as the importance of a forecast system for local stakeholders in the pastoral area are discussed.

Spatio-temporal variation of net primary productivity in a rapidly expanding artificial woodland area based on remote-sensing data

Erdkunde ◽  
2021 ◽  
Vol 75 (3) ◽  
pp. 191-207
Author(s):  
Qi Yi ◽  
Yuting Gao ◽  
Hongrong Du ◽  
Junxu Chen ◽  
Liang Emlyn Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Meteorological Data ◽  
Arable Land ◽  
Remote Sensing Data ◽  
Stand Age ◽  
Sensing Data ◽  
Spatio Temporal

The expansion of artificial woodlands in China has contributed significantly to regional land-cover changes and changes in the regional net primary productivity (NPP). This study used Ximeng County in the Yunnan Province as a case study to investigate the overall changes, associated amplitude, and spatio-temporal distribution of NPP from 2000–2015.The Carnegie-Ames-Stanford approach was used in the rapidly expanding artificial woodland area based on MODIS-NDVI data, meteorological data, and Landsat 5 TM data to calculate the NPP. The results show that (1) artificial woodlands experience a 10fold increase and account for 93 % of the land cover transfer, which was mainly from woodland areas. (2) The NPP was 906.2×109 gC·yr-1 in 2000 and 972.0×109 gC·yr-1 in 2015, presenting a total increase of 65.8×109 gC·yr-1 and a mean increase of 52.4 gC·m-2·yr-1 in Ximeng County. (3) The most notable NPP changes take place in the central and the western border regions, with the increasing NPP of artificial woodlands and arable land offsetting the negative effects of the decrease in woodland NPP. (4) The total NPP in the study area kept increasing, primarily due to the growing area of artificial woodlands as well as the stand age of the woods, whereas the mean value change of the NPP is mostly related to the increasing stand age. (5) The artificial woodlands increase the NPP value more than natural woodlands. While protecting and promoting ecologically valuable natural forests at the same time, it seems quite advantageous to establish regional plantations and coordinate their development on a scientific basis with a view to increasing NPP, economic development, but also the ecological stability of this mountain region. Our study reveals the changes in NPP and its distribution in a rapidly expanding area of artificial woodland in southwest China based on remote-sensing data and the CASA model, providing a decision-making basis for rational land-use management, the optimal utilization of land resources, and a county-scale assessment approach.

scholarly journals Spatio-Temporal Variation in AOD and Correlation Analysis with PAR and NPP in China from 2001 to 2017

2020 ◽  
Vol 12 (6) ◽  
pp. 976 ◽  
Author(s):  
Xin Li ◽  
Hongyu Liang ◽  
Weiming Cheng
Keyword(s):  
Net Primary Productivity ◽  
Negative Impact ◽  
Positive Impact ◽  
Ground Surface ◽  
Terrestrial Ecosystems ◽  
Temporal Variations ◽  
Plant Production ◽  
Positive Correlation ◽  
Increasing Trend ◽  
Spatio Temporal

Atmospheric aerosols can elicit variations in how much solar radiation reaches the ground surface due to scattering and absorption, which may affect plant photosynthesis and carbon uptake in terrestrial ecosystems. In this study, the spatio-temporal variations in aerosol optical depth (AOD) are compared with that in photosynthetically active radiation (PAR) and net primary productivity (NPP) during 2001–2017 in China using multiple remote sensing data. The correlations between them are analyzed at different scales. Overall, the AOD exhibited a northeast-to-southwest decreasing pattern in space. A national increasing trend of 0.004 year−1 and a declining trend of −0.007 year−1 of AOD are observed during 2001–2008 and 2009–2017. The direct PAR (PARdir) and diffuse PAR (PARdif) present consistent and opposite tendency with AOD during two periods, respectively. The total PAR (PARtotal) shows a similar variation pattern with PARdir. In terms of annual variation, the peaks of AOD coincide with the peaks of PARdif and the troughs of PARdir, indicating that aerosols have a significant positive impact on PARdir and a negative impact on PARdif. Furthermore, the PARdir has a stronger negative association with AOD than the positive correlation between PARdif and AOD at national and regional scales, indicating that PARdir is more sensitive to aerosol changes. The NPP has higher values in the east than in the west and exhibits a significant increasing trend of 0.035 gCm−2day−1 after 2008. The NPP has a negative correlation (−0.4–0) with AOD and PARdif and a positive correlation (0–0.4) with PARdir in most areas of China. The area covered by forests has the highest NPP-PAR correlation, indicating that NPP in forests is more sensitive to the PAR than is the NPP in grasslands and croplands. This study is beneficial for interpreting the aerosol-induced PAR impact on plant growth and for predicting plant production on haze days.

scholarly journals A First Assessment of the 2018 European Drought Impact on Ecosystem Evapotranspiration

2020 ◽  
Vol 13 (1) ◽  
pp. 16
Author(s):  
Kazi Rifat Ahmed ◽  
Eugénie Paul-Limoges ◽  
Uwe Rascher ◽  
Alexander Damm
Keyword(s):  
Western Europe ◽  
Extreme Event ◽  
Meteorological Data ◽  
Surface Air Temperature ◽  
Weather Forecasts ◽  
The North ◽  
Moderate Resolution ◽  
Spatio Temporal ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Agricultural Areas

The combined heatwave and drought in 2018 notably affected the state and functioning of European ecosystems. The severity and distribution of this extreme event across ecosystem types and its possible implication on ecosystem water fluxes are still poorly understood. This study estimates spatio-temporal changes in evapotranspiration (ET) during the 2018 drought and heatwave and assesses how these changes are distributed in European ecosystems along climatic gradients. We used the ET eight-day composite product from the MODerate Resolution Imaging Spectroradiometer (MODIS) together with meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF ERA5). Our results indicate that ecosystem ET was strongly reduced (up to −50% compared to a 10-year reference period) in areas with extreme anomalies in surface air temperature (Tsa) and precipitation (P) in central, northern, eastern, and western Europe. Northern and Eastern Europe had prolonged anomalies of up to seven months with extreme intensities (relative and absolute) of Tsa, P, and ET. Particularly, agricultural areas, mixed natural vegetation, and non-irrigated agricultural areas were the most affected by the increased temperatures in northern Europe. Our results show contrasting drought impacts on ecosystem ET between the North and South of Europe as well as on ecosystem types.

scholarly journals Estimating Carbon, Nitrogen, and Phosphorus Contents of West–East Grassland Transect in Inner Mongolia Based on Sentinel-2 and Meteorological Data

2022 ◽  
Vol 14 (2) ◽  
pp. 242
Author(s):  
Haiyang Pang ◽  
Aiwu Zhang ◽  
Shengnan Yin ◽  
Jiaxin Zhang ◽  
Gang Dong ◽  
...  
Keyword(s):  
Inner Mongolia ◽  
Meteorological Data ◽  
Least Squares Regression ◽  
Desert Grassland ◽  
Continuous Change ◽  
Large Span ◽  
Feature Enhancement ◽  
Spectral Enhancement ◽  
Enhancement Algorithm ◽  
Sentinel 2

Estimating the carbon (C), nitrogen (N), and phosphorus (P) contents of a large-span grassland transect is essential for evaluating ecosystem functioning and monitoring biogeochemical cycles. However, the field measurements are scattered, such that they cannot indicate the continuous gradient change in the grassland transect. Although remote sensing methods have been applied for the estimation of nutrient elements at the local scale in recent years, few studies have considered the effective estimation of C, N, and P contents over large-span grassland transects with complex environment including a variety of grassland types (i.e., meadow, typical grassland, and desert grassland). In this paper, an information enhancement algorithm (involving spectral enhancement, regional enhancement, and feature enhancement) is used to extract the weak information related to C, N, and P. First, the spectral simulation algorithm is used to enhance the spectral information of Sentinel-2 imagery. Then, the enhanced spectra and meteorological data are fused to express regional characteristics and the fractional differential (FD) algorithm is used to extract sensitive spectral features related to C, N, and P, in order to construct a partial least-squares regression (PLSR) model. Finally, the C, N, and P contents are estimated over a West–East grassland transect in Inner Mongolia, China. The results demonstrate that: (i) the contents of C, N, and P in large-span transects can be effectively estimated through use of the information enhancement method involving spectral enhancement, regional feature enhancement, and information enhancement, for which the estimation accuracies (R2) were 0.88, 0.78, and 0.85, respectively. Compared with the estimation results of raw Sentinel-2 imagery, the RMSE was reduced by 3.42 g/m2, 0.14 g/m2, and 13.73 mg/m2, respectively; and (ii) the continuous change trend and spatial distribution characteristics of C, N, and P contents in the west–east transect of the Inner Mongolia Plateau were obtained, which showed decreasing trends in C, N, and P contents from east to west and the characteristics of meadow > typical grassland > desert grassland. Thus, the information enhancement algorithm can help to improve estimates of C, N, and P contents when considering large-span grassland transects.

Study on Effect of Drought Based on Time Series on Grassland Vegetation in Eastern Inner Mongolia

2012 ◽  
Vol 518-523 ◽  
pp. 5306-5315 ◽  
Author(s):  
Gui Xiang Liu ◽  
Zhuo Yi ◽  
Feng Ming Yu ◽  
Chun Long Jiang
Keyword(s):  
Time Series ◽  
Inner Mongolia ◽  
Meteorological Data ◽  
Remote Sensing Data ◽  
Desert Grassland ◽  
Grassland Vegetation ◽  
Spi Index ◽  
Growth Status ◽  
Ndvi Index ◽  
Eastern Inner Mongolia

This paper, based on the long sequence meteorological data and the MODIS remote sensing data, calculates the every-ten-day NDVI index and SPI index of the grassland vegetation in the Eastern Inner Mongolia between 2006 and 2010. It applies the SPI index to indicate the degree of drought and the NDVI index to represent the growth status of the grassland vegetation. This paper analyzes the relationship between the NDVI index and the SPI index by the Time Series Spectrum Analysis Method, leading to the conclusion that the vegetations are sensitive to the drought in the green-turning and yellowing period, but relatively not that sensitive in the budding and maturation period, and that, the vegetations in meadow grassland, typical grassland and desert grassland vary in the responses to the drought.

Assessment of changes in the state of the rangelands of Inner Mongolia, China between 1998 and 2007 using remotely sensed data

2012 ◽  
Vol 34 (1) ◽  
pp. 103 ◽  
Author(s):  
Z. M. Hu ◽  
S. G. Li ◽  
J. W. Dong ◽  
J. W. Fan
Keyword(s):  
Inner Mongolia ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Temporal Dynamics ◽  
Remotely Sensed ◽  
The State ◽  
Mean Annual Precipitation ◽  
Aboveground Net Primary Productivity ◽  
Remotely Sensed Data ◽  
Normalised Difference Vegetation Index

The spatial annual patterns of aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of the rangelands of the Inner Mongolia Autonomous Region of China, a region in which several projects for ecosystem restoration had been implemented, are described for the years 1998–2007. Remotely sensed normalised difference vegetation index and ANPP data, measured in situ, were integrated to allow the prediction of ANPP and PUE in each 1 km2 of the 12 prefectures of Inner Mongolia. Furthermore, the temporal dynamics of PUE and ANPP residuals, as indicators of ecosystem deterioration and recovery, were investigated for the region and each prefecture. In general, both ANPP and PUE were positively correlated with mean annual precipitation, i.e. ANPP and PUE were higher in wet regions than in arid regions. Both PUE and ANPP residuals indicated that the state of the rangelands of the region were generally improving during the period of 2000–05, but declined by 2007 to that found in 1999. Among the four main grassland-dominated prefectures, the recovery in the state of the grasslands in the Erdos and Chifeng prefectures was highest, and Xilin Gol and Chifeng prefectures was 2 years earlier than Erdos and Hunlu Buir prefectures. The study demonstrated that the use of PUE or ANPP residuals has some limitations and it is proposed that both indices should be used together with relatively long-term datasets in order to maximise the reliability of the assessments.

Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales – Geostatistical interpolation framework

2021 ◽  
Author(s):  
Micha Eisele ◽  
Maximilian Graf ◽  
Abbas El Hachem ◽  
Jochen Seidel ◽  
Christian Chwala ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Pearson Correlation ◽  
Urban Catchment ◽  
Space And Time ◽  
Geostatistical Interpolation ◽  
Primary Network ◽  
Ground Measurement ◽  
Catchment Areas ◽  
Important Input ◽  
Spatio Temporal

<p>Precipitation - highly variable in space and time - is the most important input for many hydrological models. As these models become more and more detailed in space and time, high-resolution input data are required. Especially for modeling and prediction in fast reacting catchments, such as urban catchment areas, a higher space-time resolution is needed than the current ground measurement networks operated by national weather services usually provide. With the increasing number and availability of opportunistic sensors such as commercial microwave links (CMLs) and personal weather stations (PWS) in recent years, new opportunities for measuring meteorological data are emerging.</p><p>We developed a geostatistical interpolation framework which allows a combination of different opportunistic sensors and their specific features and geometric properties, e.g. point and line information. In this framework, a combined kriging approach is introduced, taking into account not only the point information of a reliable primary network, e.g., from national weather services, but also the higher uncertainty of the PWS- and CML-based precipitation. The path-averaged information of the CMLs is included through a block kriging-type approach.</p><p>The methodology was applied for two 7-months periods in Germany using an hourly temporal and a 1x1 km spatial resolution. By incorporating CMLs and PWS, the Pearson correlation could be increased from 0.56 to 0.73 compared to using only primary network for interpolation. The resulting precipitation maps also provided good agreement compared to gauge adjusted radar products.</p>

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