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

Multi-layer Land Cover Data for Remote-Sensing based Vegetation Modelling for South Korea

Land cover data is essential input for vegetation productivity models that are often driven by coarse resolution data. In this study, we analyze how well 1 km land cover data represent land cover at 30 m for South Korea. We derive multi-layer 1 km land cover classes and coverages and analyze how much of land cover heterogeneity is represented by the successive layers. Comparison to global land cover data shows varying agreement. The multilayer land cover data can be used for example for net primary productivity modelling. Especially, for models that can include more than one vegetation type per pixel, multi-layer land cover data and their corresponding coverages are a major asset

Spatio-temporal patterns and dynamics of net primary productivity for Kazakhstan

Monitoring of net primary productivity (NPP) is especially important for the fragile ecosystems in arid and semi-arid regions. Great interest exists in observing large-scale vegetation dynamics and understanding spatial and temporal patterns of NPP in these areas. In this study we present results of NPP obtained with the model BETHY/DLR for Kazakhstan for 2003-2011 and its spatial and temporal dynamics. The spatial distribution of vegetation productivity shows a gradient from North to South and clear differences between individual vegetation classes. The monthly NPP values show the highest productivity in June. Differences between rain-fed and irrigated areas indicate the dependency on water availability. Annual NPP variability was high for agricultural areas, but showed low values for natural vegetation. The analysis of different patterns in vegetation productivity provides valuable information for the identification of regions that are vulnerable to a possible climate change. This information may thus substantially support a sustainable land management

Modeling the spatial distribution of grazing intensity in Kazakhstan

<div><p>With increasing affluence in many developing countries, the demand for livestock products is rising and the increasing feed requirement contributes to pressure on land resources for food and energy production. However, there is currently a knowledge gap in our ability to assess the extent and intensity of the utilization of land by livestock, which is the single largest land use in the world. We developed a spatial model that combines fine-scale livestock numbers with their associated energy requirements to distribute livestock grazing demand onto a map of energy supply, with the aim of estimating where and to what degree pasture is being utilized. We applied our model to Kazakhstan, which contains large grassland areas that historically have been used for extensive livestock production but for which the current extent, and thus the potential for increasing livestock production, is unknown. We measured the grazing demand of Kazakh livestock in 2015 at 286 Petajoules, which was 25% of the estimated maximum sustainable energy supply that is available to livestock for grazing. The model resulted in a grazed area of 1.22 million km², or 48% of the area theoretically available for grazing in Kazakhstan, with most utilized land grazed at low intensities (average off-take rate was 13% of total biomass energy production). Under a conservative scenario, our estimations showed a production potential of 0.13 million tons of beef additional to 2015 production (31% increase), and much more with utilization of distant pastures. This model is an important step forward in evaluating pasture use and available land resources, and can be adapted at any spatial scale for any region in the world.</p></div

Remote Sensing for large-scale agricultural investment areas in Ethiopia – agricultural monitoring based on Earth observation time-series

Ethiopia is known to be currently food insecure and suffering from considerable food deficits. The Government of Ethiopia strives to increase the agricultural production and its efficiency. Therefore, Ethiopia has been promoting large-scale agricultural investment (LSAI) to transform the agricultural sector. However, the progress by agricultural development has been limited. Investors only developed a small fraction of the transferred land. Therefore, there is a great need for monitoring of the implementation and actual state of land use of every LSAI project. The use of remote sensing can substantially support agricultural monitoring. In this study, Earth observation time series are analyzed to examine the land used for agricultural production and to differentiate crop types grown within the three study areas. Current land use/land cover (LULC) is analyzed using Sentinel-2 time series to identify cropland areas. In a second step, remote-sensing time-series of Sentinel-1 and Sentinel-2 are used to differentiate among 20 different crop types grown in the region. The developed classification methods have been applied to derive information products for three study regions in Ethiopia including the LSAI areas within the provinces of Amhara, Benishangul, and Gambella. The methods and derived information products on LULC and crop types will be made available to GIZ and regional experts to support agricultural monitoring of developed land in Ethiopia

Application of geospatial and remote sensing data to support locust management

Negative impacts on agricultural activities by different locust species are well documented and have always been one of the major threats to food security and livelihoods, especially for local communities. Locust management and control have led to less frequent and intense plagues and outbreaks worldwide. However, political insecurity and armed conflicts affect locust management, and can as well as changing climate, and land use management contribute to new outbreaks. In the context of the increasing world population and higher demand for agricultural production, locust pests will remain of high concern. Geospatial and remote sensing data have become an important source of information for different applications within locust research and management. However, there is still a gap between available information and actual practical usage. In this study, we demonstrate the importance of geospatial and remote sensing data and how this information can be prepared for a straightforward application for stakeholders. For this purpose, we use the h3-hexagonal hierarchical geospatial indexing system to simplify and structure spatial information into standardized hexagon units. The presented concept provides decision makers and ground teams with a simplified information database that contains area-wide information over time and space and can be used without detailed geospatial knowledge and background. The concept is designed for the use case of Italian locust management in the Pavlodar region (Kazakhstan) and based on actual practices. It can be extrapolated to any other study area or species of interest. Our results underline the importance of actual land management on locust presence. Up-to-date land management information can be derived from time-series analyses of remote sensing data. Furthermore, essential meteorological data are used to generate locust-specific climatic characteristics within the h3-system. Within this system, areal prioritizing for locust management can be achieved based on the included spatial information and experience from ongoing practices

AVHRR NDVI Compositing Method Comparison and Generation of Multi-decadal Time Series —A TIMELINE Thematic Processor

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument provides daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change related environmental processes. For monitoring vegetation condition, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as there is no standard procedure established. 13 different compositing methods have been implemented, daily, decadal and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best performing compositing algorithm considering all investigated aspects. However, also the combination of NDVI value and viewing and illumination angles as criteria for best-pixel selection proved to be a promising approach. The generated NDVI time series, currently ranging from 1981 - 2018, shows a consistent behavior and a close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites

The hazard of locust outbreaks - Examples from EO to support locust management

The goal of this research was to explore whether ecological niche modelling (ENM) and a habitat suitability index (HSI) model can be combined to refine results for actual breeding areas of three different locust pests. With the application of ENM as part of HSI, the information value based on climatic and soil preference components defining locust species’ ecological niche are maintained. In addition, up to date land surface parameters, vegetation development and other species relevant environmental parameters were incorporated in the HSI model. Moreover, human interaction and actual land surface dynamics play a crucial role for locust outbreaks and influence and define suitable breeding areas. Therefore, modelling based only on climatic and edaphic variables provides only the ecological niche of a species without considering actual changes of the landscape or situation. Here, we demonstrated a way to account for this issue by implementing different variables derived from Sentinel-2 time-series analysis, which describe the actual state of the land and in this way further narrow suitable breeding areas within an HSI model. The presented approach for mapping egg-pod incubation and breeding suitability was tested for Italian locust in Pavlodar oblast (Kazakhstan), for Moroccan locust in Turkistan oblast (Kazakhstan), and for desert locust in the Awash river basin (Ethiopia, Djibouti, Somalia)

Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product

Remote sensing multi-decadal time-series provide important information for analysing long-term environmental change. The Advanced Very High Resolution Radiometer (AVHRR) has been providing data since the early 1980s. Normalised Difference Vegetation Index (NDVI) timeseries derived thereof can be used for monitoring vegetation conditions. This study presents the novel TIMELINE NDVI product, which provides a consistent set of daily, 10-day, and monthly NDVI composites at a 1 km spatial resolution based on AVHRR data for Europe and North Africa, currently spanning the period from 1981 to 2018. After investigating temporal and spatial data availability within the TIMELINE monthly NDVI composite product, seasonal NDVI trends have been derived thereof for the period 1989–2018 to assess long-term vegetation change in Europe and northern Africa. The trend analysis reveals distinct patterns with varying NDVI trends for spring, summer and autumn for different regions in Europe. Integrating the entire growing season, the result shows positive NDVI trends for large areas within Europe that confirm and reinforce previous research. The analyses show that the TIMELINE NDVI product allows long-term vegetation dynamics to be monitored at 1 km resolution on a pan-European scale and the detection of specific regional and seasonal patterns

Earth observation time series for the monitoring of droughts in Cyprus: Patterns and drivers of vegetation dynamics

With amplified climate warming, climate extremes over Europe become more frequent. Since the 2000's, many years have been characterized by extreme events such as droughts and heat waves. For example, in Central Europe, extreme droughts and heat waves took place in the years 2003 and 2018. In comparison, Cyprus experienced strong droughts during 2003 and 2016-2018. Such extreme climate events can have severe impacts on agricultural yields, the productivity of natural vegetation, and on water resources. In this regard, long-term Earth observation (EO) time series are essential to quantitatively assess and analyse changes on the land surface, including vegetation condition. In this study, a joint analysis of geoscientific time series over the last two decades, including EO-based MODIS vegetation indices and meteorological variables is performed to assess drought events and analyse trends as well as potential drivers of vegetation dynamics in Cyprus. The analysis of drought events and vegetation trends is based on the full archive of MODIS imagery at 250 m spatial resolution covering the period 2000-2022. In detail, climate-related effects on vegetation were analysed by means of the deviations of MODIS 16-day vegetation index composites from their long-term mean. Next, trends of the MODIS vegetation index were calculated to evaluate spatial patterns of vegetation change over the investigated period. These analyses were additionally performed for geographically stratified regions, including diverse vegetation classes such as cropland and grassland. Furthermore, the application of a causal discovery algorithm reveals linkages within a multivariate feature space, in particular between vegetation greenness and climatic drivers. Preliminary analyses showed that drought patterns differ with respect to seasons and the investigated vegetation class. For example, the strong drought year 2008 is clearly reflected in the results, whereas forest areas appear to be least affected by the drought during the spring months. Moreover, considering the significant trends over the last two decades, an increase in vegetation greenness could be observed