Aboveground net primary productivity of vegetation along a climate-related gradient in a Eurasian temperate grassland: spatiotemporal patterns and their relationships with climate factors

Accurate assessments of spatiotemporal patterns in net primary productivity and their links to climate are important to obtain a deeper understanding of the function, stability and sustainability of grassland ecosystems. We combined a satellite-derived NDVI time-series dataset and field-based samples to investigate spatiotemporal patterns in aboveground net primary productivity (ANPP), and we examined the effect of growing season air temperate (GST) and precipitation (GSP) on these patterns along a climaterelated gradient in an eastern Eurasian grassland. Our results indicated that the ANPP fluctuated with no significant trend during 2001-2012. The spatial distribution of ANPP was heterogeneous and decreased from northeast to southwest. The interannual changes in ANPP were mainly controlled by year-to-year GSP; a strong correlation of interannual variability between ANPP and GSP was observed. Similarly, GSP strongly influenced spatial variations in ANPP, and the slopes of fitted linear functions of the GSP-ANPP relationship increased from arid temperate desert grassland to humid meadow grassland. An exponential function could be used to fit the GSP-ANPP relationship for the entire region. An improved moisture index that combines the effects of GST and GSP better explained the variations in ANPP compared with GSP alone. In comparisons with the previous studies, we found that the relationships between spatiotemporal variations in ANPP and climate factors were probably scale dependent. We imply that the quantity and spatial range of analyzed samples contribute to these different results. Multi-scale studies are necessary to improve our knowledge of the response of grassland ANPP to climate change.ArticleENVIRONMENTAL EARTH SCIENCES.76(1):56(2017)journal articl

Spatio-Temporal Patterns and Climate Variables Controlling of Biomass Carbon Stock of Global Grassland Ecosystems from 1982 to 2006

Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y−1 during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management

Modelling Net Primary Productivity and Above-Ground Biomass for Mapping of Spatial Biomass Distribution in Kazakhstan

Biomass is an important ecological variable for understanding the responses of vegetation to the currently observed global change. The impact of changes in vegetation biomass on the global ecosystem is also of high relevance. The vegetation in the arid and semi-arid environments of Kazakhstan is expected to be affected particularly strongly by future climate change. Therefore, it is of great interest to observe large-scale vegetation dynamics and biomass distribution in Kazakhstan. At the beginning of this dissertation, previous research activities and remote-sensing-based methods for biomass estimation in semi-arid regions have been comprehensively reviewed for the first time. The review revealed that the biggest challenge is the transferability of methods in time and space. Empirical approaches, which are predominantly applied, proved to be hardly transferable. Remote-sensing-based Net Primary Productivity (NPP) models, on the other hand, allow for regional to continental modelling of NPP time-series and are potentially transferable to new regions. This thesis thus deals with modelling and analysis of NPP time-series for Kazakhstan and presents a methodological concept for derivation of above-ground biomass estimates based on NPP data. For validation of the results, biomass field data were collected in three study areas in Kazakhstan. For the selection of an appropriate model, two remote-sensing-based NPP models were applied to a study area in Central Kazakhstan. The first is the Regional Biomass Model (RBM). The second is the Biosphere Energy Transfer Hydrology Model (BETHY/DLR). Both models were applied to Kazakhstan for the first time in this dissertation. Differences in the modelling approaches, intermediate products, and calculated NPP, as well as their temporal characteristics were analysed and discussed. The model BETHY/DLR was then used to calculate NPP for Kazakhstan for 2003–2011. The results were analysed regarding spatial, intra-annual, and inter-annual variations. In addition, the correlation between NPP and meteorological parameters was analysed. In the last part of this dissertation, a methodological concept for derivation of above-ground biomass estimates of natural vegetation from NPP time-series has been developed. The concept is based on the NPP time-series, information about fractional cover of herbaceous and woody vegetation, and plants’ relative growth rates (RGRs). It has been the first time that these parameters are combined for biomass estimation in semi-arid regions. The developed approach was finally applied to estimate biomass for the three study areas in Kazakhstan and validated with field data. The results of this dissertation provide information about the vegetation dynamics in Kazakhstan for 2003–2011. This is valuable information for a sustainable land management and the identification of regions that are potentially affected by a changing climate. Furthermore, a methodological concept for the estimation of biomass based on NPP time-series is presented. The developed method is potentially transferable. Providing that the required information regarding vegetation distribution and fractional cover is available, the method will allow for repeated and large-area biomass estimation for natural vegetation in Kazakhstan and other semi-arid environments

Time Series Analysis of Long-Term Vegetation Trends, Phenology, and Ecosystem Service Valuation for Grasslands in the U.S. Great Plains

Doctor of PhilosophyDepartment of GeographyJ. M. Shawn HutchinsonGrasslands are one of the largest, most biodiverse, and productive terrestrial biomes but they receive very low levels of protection. The temperate grasslands in the United States are one of the most threatened grassland ecosystems. Every year, a significant portion of grasslands in the Great Plains are converted to agricultural use, with almost 96% of the historical extent lost. Other factors that affect existing grassland health include significant climatic changes, invasion of woody, non-native species, fragmentation, lack or inadequate burning, and excessive grazing. The impact of the combination of these factors on grasslands in the US Great Plains is still unknown. The goal of this research is to investigate the long-term grassland vegetation conditions using a well-known indicator (greenness) and assesses its impact on the provision of select grassland ecosystem services within the US Great Plains from 2001 to 2017. The above goal was achieved with three objectives addressed in three chapters. In Chapter 3, a time-series analysis of Moderate Resolution Imaging Spectrometer (MODIS) 16-day maximum value composite Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data (MOD13Q1 Collection 6) was performed to assess long-term trends in vegetation greenness across the Great Plains ecoregion of the United States. The Breaks for Additive Season and Trend (BFAST) decomposition method was applied to a time series of images from 2001 to 2017 to derive spatially explicit estimates of gradual interannual change. Results show more 'greening' trends than 'browning' and 'no change' trends during the study period. Comparing the trend results from both vegetation indices suggests that EVI is more suitable for this analysis in the study area, especially in areas with high biomass. In Chapter 4, a time-series analysis of Moderate Resolution Imaging Spectrometer (MODIS) 16-day maximum value composite Enhanced Vegetation Index (EVI) data (MOD13Q1 Collection 5) is used to explore spatial patterns of vegetation phenology and to assess long-term phenology trends across the region. The program TIMESAT was used to extract key measures of vegetation phenological development from 2001 to 2017, including the phenometrics (1) season length, (2) start of growing season, (3) end of growing season, (4) middle of the growing season, (5) maximum NDVI value, (6) small integral, (7) left derivative, and (8) right derivative. Results show important variation in phenological patterns across the region such as a shift to a later start, earlier end, and shorter the growing season length, especially in the southern parts of the region. As shown in the small integral and maximum EVI, vegetation productivity appears to have increased over many areas in the Great Plains ecoregion. Finally, Chapter 5 focuses on developing a methodological improvement to the widely used Invest ecosystem services model that uses remotely sensed inputs to capture the interannual spatio-temporal dynamics of grassland vegetation on the provision of grassland ecosystem services across the US Great Plains. A selected set of grassland ecosystem services was quantified (economic and biophysical values) for the period between 2001 and 2017. This exploratory study will be a basis for highlighting the role grasslands play in providing essential ecosystem services and how improved long-term vegetation monitoring can benefit land-use decisions locally and regionally

Application of remote sensing and GIS in modelling bison carrying capacity in mixed-grass prairie

Understanding carrying capacity of plains bison (B. bison bison) is critical for protecting this wild species and grassland ecosystem in mixed-grass prairie. The overall goal of this study is to examine plains bison carrying capacity in the mixed-grass prairie. There are four specific objectives: 1) investigate annual space use of plains bison and their seasonal core ranges, 2) assess seasonal Resources Selection Functions (RSFs) of plains bison, 3) estimate vegetation biomass and productivity of mixed-grass prairie, and 4) estimate carrying capacity taking into account RSFs. I used Kernel Density Estimator to address the first objective. Generalized Linear Mixed Effects models were used for the second objective. The last two objectives were completed using Sentinel-2 Multispectral Image (MSI). This study highlights the power of remote sensing and Geographic Information Systems (GIS) techniques in estimating key driver of bison carrying capacity (available forage) and adjusting factor (RSFs). Results show that bison family groups in Grasslands National Park frequent specific areas. They mainly use the northeast corner of the West Block and expand the core range when it comes to dormant season. Vegetation type information and other landscape factors (slope, distance to water, roads, fences, and prairie dog town) are influencing seasonal RSFs of bison family groups. Vegetation productivity is 734 kg ha-1 supporting 671 - 959 Bison Unit as the carrying capacity. Our study not only contributes to a better bison management plan for Grasslands National Park, one of seven conservation areas of wild plains bison in Canada, but also assists in understanding the interaction of this wild species with the mixed-grass prairie ecosystem

Phenologically-Tuned MODIS NDVI-Based Time Series (2000-2012) For Monitoring Of Vegetation and Climate Change in North-Eastern Punjab, Pakistan

One of the main factors determining the daily variation of the active surface temperature is the state of the vegetation cover It can well be characterized by the Normalized Difference Vegetation Index NDVI The NDVI has the potential ability to signal the vegetation features of different eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles The vegetation phenology is the expression of the seasonal cycles of plant processes and contributes vital current information on vegetation conditions and their connections to climate change The NDVI is computed using near-infrared and red reflectances and thus has both an accuracy and precision A gapless time series of MODIS NDVI MOD13A1 composite raster data from 18th February 2000 to 16th November 2012 with a spatial resolution of 500 m was utilized Time-series terrestrial parameters derived from NDVI have been extensively applied to global climate change since it analyzes each pixel individually without the setting of thresholds to detect change within a time serie