Organic matter modeling at the landscape scale based on multitemporal soil pattern analysis using RapidEye data

This study proposes the development of a landscape-scale multitemporal soil pattern analysis (MSPA) method for organic matter (OM) estimation using RapidEye time series data analysis and GIS spatial data modeling, which is based on the methodology of Blasch et al. The results demonstrate (i) the potential of MSPA to predict OM for single fields and field composites with varying geomorphological, topographical, and pedological backgrounds and (ii) the method conversion of MSPA from the field scale to the multi-field landscape scale. For single fields, as well as for field composites, significant correlations between OM and the soil pattern detecting first standardized principal components were found. Thus, high-quality functional OM soil maps could be produced after excluding temporal effects by applying modified MSPA analysis steps. A regional OM prediction model was developed using four representative calibration test sites. The MSPA-method conversion was realized applying the transformation parameters of the soil-pattern detection algorithm used at the four calibration test sites and the developed regional prediction model to a multi-field, multitemporal, bare soil image mosaic of all agrarian fields of the Demmin study area in Northeast Germany. Results modeled at the landscape scale were validated at an independent test site with a resulting prediction error of 1.4 OM-% for the main OM value range of the Demmin study area

Mapping tillage operations over a peri-urban region using combined SPOT4 and ASAR/ENVISAT images

This study aimed at assessing the potential of combining synchronous SPOT4 and ENVISAT/ASAR images (HH and HV polarizations) for mapping tillage operations (TOs) of bare agricultural fields over a peri-urban area characterized by conventional tillage system in the western suburbs of Paris (France). The reference spatial units for spatial modeling are 57 within-field areas named "reference zones" (RZs) homogeneous for their soil properties, constructed in the vicinity of 57 roughness measurement locations, spread across 20 agricultural fields for which TOs were known. The total RZ dataset was half dedicated to successive random selections of training/validating RZs, the remaining half (29 RZs) being kept for validating the final map results. Five supervised per-pixels classifiers were used in order to map 2 TOs classes (seedbed&harrowed and late winter plough) in addition to 4 landuse classes (forest, urban, crops and grass, water bodies): support vector machine with polynomial kernel (pSVM), SVM with radial basis kernel (rSVM), artificial neural network (ANN), Maximum Likelihood (ML), and regression tree (RT). All 5 classifiers were implemented in a bootstrapping approach in order to assess the uncertainty of map results. The best results were obtained with pSVM for the SPOT4/ASAR pair with producer's and user's mean validation accuracies (PmVA/UmVA) of 91.7%/89.8% and 73.2%/73.3% for seedbed&harrowed and late winter plough conditions, respectively. Whatever classifier, the SPOT4/ASAR pair appeared to perform better than each of the single images, particularly for late winter plough: PmVA/UmVA of 61.6%/53.0% for the single SPOT4 image; 0%/6% for the single ASAR image. About 73% of the validation agricultural fields (79% of the RZs) were correctly predicted in terms of TOs in the best pSVM-derived final map. Final map results could be improved through masking non-agricultural areas with land use identification system layer prior to classifying images. Such knowledge of agricultural operations is likely to facilitate the mapping of agricultural systems which otherwise proceed from time-consuming surveys to farmers

Deriving crop productivity indicators from satellite synthetic aperture radar to assess wheat production at field-scale.

Richter, G. M. Industrial supervisor ( Rothamsted Research) Burgess, Paul J. and Meersmans, Jeroen Associate supervisorsThe deployment of high-revisit satellite-based radar sensors raises the question of whether the data collected can provide quantitative information to improve agricultural productivity. This thesis aims to develop and test mathematical algorithms to describe the dynamic backscatter of high-resolution Synthetic Aperture Radar (Sentinel-1) in order to describe the development and productivity of wheat at field-scale. A time series of the backscatter ratio (VH/VV), collected over a cropping season, could be characterised by a growth and a senescence logistic curve and related to critical phases of crop development. The curve parameters, referred to as Crop Productivity Indicators (CPIs), compared well with the crop production for three years at the Rothamsted experimental farm. The combination of different parameters (e.g. midpoints of the two curves) helped to define CPIs, such as duration, that significantly (r = 0.61, p = 0.05) correlated with measured yields. Field observations were used to understand the wheat evolution by sampling canopy characteristics across the seasons. The correlation between the samples and the CPIs showed that structural changes, like biomass increase, influence the CPIs during the growth phase, and that declining plant water content was correlated with VH/VV values during maturation. The methodology was upscaled to other farms in Hertfordshire and Norfolk. The ANOVA identified significant effects (p<0.001) of farm management, year (weather conditions) and the interaction between soil type and year on the selected CPIs. Multilinear regression models between yields and selected CPIs displayed promising predictive power (R²= 0.5) across different farms in the same year. However, these models could not explain yield differences within high-yielding farms across seasons because of the dominant effect of weather patterns on the CPIs in each year. The potential impact of the research includes estimation of yield across the landscape, phenology monitoring and indication biophysical parameters. Future work on SAR-derived CPIs should focus on improving the correlations with biophysical properties, applying of the methodology in other crops, with different soils and climates.PhD in Environment and Agrifoo

Assessment of Paleo-Landscape Features using Advanced Remote Sensing Techniques, Modelling and GIS Methods in the Lake Manyara Basin, Northern Tanzania

In researching the evolution of hominids, the East African Rift System acts as a vital region. The rift valleys enabled some of the most sensational hominid findings to date. Various hypotheses have been developed in the last decades, which try to explain the influence of changes in paleo-climate, paleo-landscape and paleo-environment on hominin evolution in the Quaternary. Additionally, the sediments and the morphology of the East African Rift System provide excellent terrestrial archives for paleo-environmental reconstruction. Lake Manyara is located in an endorheic basin in the eastern arm of the East African Rift System in northern Tanzania. The surroundings of the Lake Manyara are in the focus of paleontological and archaeological investigations. For instance, two hominin bearing sites were found within the catchment of the Makuyuni River, as well as artefacts and fossils are periodically uncovered. The study area, which is located east of the present-day lake, provides an insight into relevant geological and geomorphological drivers of paleo-landscape evolution of the whole region. This thesis aims at contributing to the understanding of landscape evolution in the Lake Manyara region. Compared to other regions in the East African rift system, few landscape evolution studies took place for the Lake Manyara basin. As such, an integrative scientific investigation of the spatial situation of paleo-landscape features and of paleo-lake level fluctuations is missing. The proposed study utilizes state-of-the-art remote sensing based research methods in evaluating the landscape, and in concluding from present-day landforms and processes, how the landscape developed during the Pleistocene and Holocene. In striving to accomplish this goal, this cumulative dissertation comprises eight central research questions, which are introduced in a conceptual framework. The research questions have been considered in seven scientific publications, which describe the applied methodologies and results in detail. The framework of the thesis provides a coherent and detailed interpretation and discussion of the scientific findings. The research questions and outcomes of the analyses are listed below. Key drivers of landscape development in the East African Rift System are tectonic and tectonically induced processes. Drainage network, stream longitudinal profiles and basin analysis based on topographic analyses, as well as lineaments extracted from remote sensing images, were successfully used as methods in identifying tectonic activity and related features in rift areas. The application of a gully erosion model suggests that the gully channel systems in the study area are relatively stable and that they had developed prior to the last significant lake regression. The paleo-landscape and the paleo-environment are closely connected to lake level changes of the paleo-Lake Manyara. Hence, a key question concerns the extent of the Manyara Beds, which are lacustrine deposits that indicate the maximum extent of the paleo-Lake Manyara. A combined analysis, utilizing ASTER multispectral indices and topographic parameters from a digital elevation model, led to the spatial delineation of lacustrine sediments. Their extent indicates a relation to lacustrine sediments in the southern part of the basin, and reveals lacustrine / palustrine deposits further east. A methodological comparison of Support Vector Machines and Boosted Regression Trees, which served as classification methods to identify the lacustrine sediments, exhibited high accuracies for both approaches, with minor advantages for Support Vector Machines. Closely related to the previous research question is the question on the spatial distribution of surface substrates. By incorporating a WorldView-2 scene and Synthetic Aperture Radar data to the previously mentioned datasets, it was possible to distinguish between nine topsoil and lithological target classes in the study area. The surface substrates indicate the underlying lithologies, sediments and soils, as well as soil formation processes. Between the village of Makuyuni and the present-day Lake Manyara, paleo-shorelines and terraces were formed by various paleo-lake levels. Questions arise, at which elevation these features occur and what is the maximum elevation, which was reached. ALOS PALSAR and TerraSAR-X backscatter intensity information provided the possibility of an area-wide mapping of those morphological features. Some radiometric dates exist for stromatolites from a distinct paleo-shoreline level, which support the interpretation of the lake fluctuations. The paleo-shoreline, which was identified with the highest elevation, coincides with the elevation of the lowest possible outlet of the closed Manyara basin. It can be assumed that the paleo-Lake Manyara over-spilled into the neighboring Engaruka and Natron-Magadi basins. The question of the location of sites with a high probability of artefact and/or fossil presence is important for future archaeological and paleontological research. ASTER remote sensing data and topographic indices contributed likewise to the predictive modelling of probabilities of archaeological and paleontological sites in the study area. Generally, paleontological sites are found on a higher elevation, compared to Stone Age sites. In addition, fossil sites seem to be related to stable paleo-landscape features according to this study’s findings. The results of this dissertation provide new insights in the landscape development of the Lake Manyara basin. The scientific findings contribute to the understanding of the landscape evolution for the study area, as well as for the neighboring basins in the East African Rift System. The applied geospatial methodologies can be transferred to other study areas with similar research needs