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

Estimation of grass photosynthesis rates in mixed-grass prairie using field and remote sensing approaches

With the increase in atmospheric CO2 concentrations, and the resulting potential for climate change, there has been increasing research devoted to understanding the factors that determine the magnitude of CO2 fluxes and the feedback of ecosystem fluxes on climate. This thesis is an effort to investigate the feasibility of using alternate methods to measure and estimate the CO2 exchange rates in the northern mixed grass prairie. Specifically, the objectives are to evaluate the capability of using ground-level hyperspectral, and satellite-level multispectral data in the estimation of mid-season leaf CO2 exchange rates as measured with a chamber, in and around Grasslands National Park (GNP), Saskatchewan. Data for the first manuscript was collected during June of 2004 (the approximate period for peak greenness for the study area). Spectral reflectance and CO2 exchange measurements were collected from 13 sites in and around GNP. Linear regression showed that the Photochemical Reflectance Index (PRI) calculated from hyperspectral ground-level data explained 46% of the variance seen in the CO2 exchange rates. This indicates that the PRI, which has traditionally been used only in laboratory conditions to predict CO2 exchange, can also be applied at the canopy level in grassland field conditions. The focus of the second manuscript is to establish if the relationship found between ground-level hyperspectral data and leaf CO2 exchange is applicable to satellite-level derived vegetation indices. During June of 2005, biophysical and CO2 exchange measurements were collected from 24 sites in and around GNP. A SPOT satellite image was obtained from June 22, midway through the field data collection. Cubic regression showed that Normalized Difference Vegetation Index (NDVI) explained 46% of the variance observed in the CO2 exchange rates. To our knowledge, this is the first time that a direct correlation between satellite images and leaf CO2 fluxes has been shown within the grassland biome

Impact of agricultural land use in Central Asia: a review

International audienceAbstractAgriculture is major sector in the economy of Central Asia. The sustainable use of agricultural land is therefore essential to economic growth, human well-being, social equity, and ecosystem services. However, salinization, erosion, and desertification cause severe land degradation which, in turn, degrade human health and ecosystem services. Here, we review the impact of agricultural land use in the five countries of Central Asia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, during 2008–2013 in 362 articles. We use the Land Use Functions framework to analyze the type and relative shares of environmental, economic, and social topics related to agricultural land use. Our major findings are (1) research on land use in Central Asia received high levels of international attention and the trend in the number of publications exceeded the global average. (2) The impacts of land use on abiotic environmental resources were the most explored. (3) Little research is available about how agricultural land use affects biotic resources. (4) Relationships between land degradation, e.g., salinization and dust storms, and human health were the least explored. (5) The literature is dominated by indirect methods of data analysis, such as remote sensing and mathematical modeling, and in situ data collection makes up only a small proportion

Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the twenty-first century

During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision-makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia’s role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large-scale water withdrawals, land use, and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that integrated assessment models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts

Integration of remote sensing, modeling, and field approaches for rangeland management and endangered species conservation in Central Asia

Integration of robust scientific approaches and on-the-ground conservation practice to “bridge the gap” between biologists and field managers is a perennial challenge in biodiversity conservation. In this thesis I present five, related case studies of integrating key scientific approaches (remote sensing techniques, habitat modeling and suitability analysis, and population modeling) with field practices to facilitate sustainable and locally accepted rangeland management, support conservation of snow leopard and Altai argali, and suggest options for tiger restoration in Central Asia. My synthesis of these case studies reveals that to advance regional long-term conservation initiatives, conservation science has to address relevant conservation problem directly, suggest solutions and recommendations that can be implemented by conservation managers given their capacity levels, fit into local knowledge systems as they pertain to the ecosystems under consideration, and focus on sharing lessons learned across projects

Study on Regional Responses of Pan-Arctic Terrestrial Ecosystems to Recent Climate Variability Using Satellite Remote Sensing

I applied a satellite remote sensing based production efficiency model (PEM) using an integrated AVHRR and MODIS FPAR/LAI time series with a regionally corrected NCEP/NCAR reanalysis surface meteorology and NASA/GEWEX shortwave solar radiation inputs to assess annual terrestrial net primary productivity (NPP) for the pan-Arctic basin and Alaska from 1983 to 2005. I developed a satellite remote sensing based evapotranspiration (ET) algorithm using GIMMS NDVI with the above meteorology inputs to assess spatial patterns and temporal trends in ET over the pan-Arctic region. I then analyzed associated changes in the regional water balance defined as the difference between precipitation (P) and ET. I finally analyzed the effects of regional climate oscillations on vegetation productivity and the regional water balance. The results show that low temperature constraints on Boreal-Arctic NPP are decreasing by 0.43% per year ( P \u3c 0.001), whereas a positive trend in vegetation moisture constraints of 0.49% per year ( P = 0.04) are offsetting the potential benefits of longer growing seasons and contributing to recent drought related disturbances in NPP. The PEM simulations of NPP seasonality, annual anomalies and trends are similar to stand inventory network measurements of boreal aspen stem growth ( r = 0.56; P = 0.007) and atmospheric CO2 measurement based estimates of the timing of growing season onset (r = 0.78; P \u3c 0.001). The simulated monthly ET results agree well (RMSE = 8.3 mm month-1; R2 = 0.89) with tower measurements for regionally dominant land cover types. Generally positive trends in ET, precipitation and available river discharge measurements imply that the pan-Arctic terrestrial water cycle is intensifying. Increasing water deficits occurred in some boreal and temperate grassland regions, which agree with regional drought records and recent satellite observations of vegetation browning and productivity decreases. Climate oscillations including Arctic Oscillation and Pacific Decadal Oscillation influence NPP by regulating seasonal patterns of low temperature and moisture constraints to photosynthesis. The pan-Arctic water balance is changing in complex ways in response to climate change and variability, with direct linkages to terrestrial carbon and energy cycles. Consequently, drought induced NPP decreases may become more frequent and widespread, though the occurrence and severity of drought events will depend on future water cycle patterns

Desertification

IPCC SPECIAL REPORT ON CLIMATE CHANGE AND LAND (SRCCL) Chapter 3: Climate Change and Land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystem