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West Africa is one of the fastest growing regions in the world with annual population growth rates of more than three percent for several countries. Since the 1950s, West Africa experienced a fivefold increase of inhabitants, from 71 to 353 million people in 2015 and it is expected that the region’s population will continue to grow to almost 800 million people by the year 2050. This strong trend has and will have serious consequences for food security since agricultural productivity is still on a comparatively low level in most countries of West Africa. In order to compensate for this low productivity, an expansion of agricultural areas is rapidly progressing. The mapping and monitoring of agricultural areas in West Africa is a difficult task even on the basis of remote sensing. The small scale extensive farming practices with a low level of agricultural inputs and mechanization make the delineation of cultivated land from other land cover and land use (LULC) types highly challenging. In addition, the frequent cloud coverage in the region considerably decreases the availability of earth observation datasets. For the accurate mapping of agricultural area in West Africa, high temporal as well as spatial resolution is necessary to delineate the small-sized fields and to obtain data from periods where different LULC types are distinguishable. However, such consistent time series are currently not available for West Africa. Thus, a spatio-temporal data fusion framework was developed in this thesis for the generation of high spatial and temporal resolution time series.
Data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) enjoyed increasing popularity during recent years but they have hardly been used for the application on larger scales. In order to make it applicable for this purpose and to increase the input data availability, especially in cloud-prone areas such as West Africa, the ESTARFM framework was developed in this thesis introducing several enhancements. An automatic filling of cloud gaps was included in the framework in order to use even partly cloud-covered Landsat images for the fusion without producing gaps on the output images. In addition, the ESTARFM algorithm was improved to automatically account for regional differences in the heterogeneity of the study region. Further improvements comprise the automation of the time series generation as well as the significant acceleration of the processing speed through parallelization. The performance of the developed ESTARFM framework was tested by fusing an 8-day NDVI time series from Landsat and MODIS data for a focus area of 98,000 km² in the border region between Burkina Faso and Ghana. The results of this test show the capability of the ESTARFM framework to accurately produce high temporal resolution time series while maintaining the spatial detail, even in such a heterogeneous and cloud-prone region.
The successfully tested framework was subsequently applied to generate consistent time series as the basis for the mapping of agricultural area in Burkina Faso for the years 2001, 2007, and 2014. In a first step, high temporal (8-day) and high spatial (30 m) resolution NDVI time series for the entire country and the three years were derived with the ESTARFM framework. More than 500 Landsat scenes and 3000 MODIS scenes were automatically processed for this purpose. From the fused ESTARFM NDVI time series, phenological metrics were extracted and together with the single time steps of NDVI served as input for the delineation of rainfed agricultural areas, irrigated agricultural areas and plantations. The classification was conducted with the random forest algorithm at a 30 m spatial resolution for entire Burkina Faso and the three years 2001, 2007, and 2014. For the training and validation of the classifier, a randomly sampled reference dataset was generated from Google Earth images based on expert knowledge of the region. The overall classification accuracies of 92% (2001), 91% (2007), and 91% (2014) indicate the well-functioning of the developed methodology. The resulting maps show an expansion of agricultural area of 91% from about 61,000 km² in 2001 to 116,900 km² in 2014. While rainfed agricultural areas account for the major part of this increase, irrigated areas and plantations also spread considerably. Especially the expansion of irrigation systems and plantation area can be explained by the promotion through various national and international development projects. The increase of agricultural areas goes in line with the rapid population growth in most of Burkina Faso’s provinces which still had available land resources for an expansion of agricultural area. An analysis of the development of agricultural areas in the vicinity of protected areas highlighted the increased human pressure on these reserves. The protection of the remnant habitats for flora and fauna while at the same time improving food security for a rapidly growing population, are the major challenges for the region in the future.
The developed ESTARFM framework showed great potential beyond its utilization for the mapping of agricultural area. Other large-scale research that requires a sufficiently high temporal and spatial resolution such as the monitoring of land degradation or the investigation of land surface phenology could greatly benefit from the application of this framework.
As a cradle of ancient Chinese civilization, the Yellow River Basin has a very long human-environment interrelationship, where early anthropogenic activities re- sulted in large scale landscape modifications. Today, the impact of this relationship
has intensified further as the basin plays a vital role for China’s continued economic
development. It is one of the most densely-populated, fastest growing, and most dynamic
regions of China with abundant natural and environmental resources providing a livelihood for almost 190 million people. Triggered by fundamental economic reforms, the
basin has witnessed a spectacular economic boom during the last decades and can be
considered as an exemplary blueprint region for contemporary dynamic Global Change
processes occurring throughout the country, which is currently transitioning from an
agrarian-dominated economy into a modern urbanized society. However, this resourcesdemanding growth has led to profound land use changes with adverse effects on the Yellow
River social-ecological systems, where complex challenges arise threatening a long-term
sustainable development.
Consistent and continuous remote sensing-based monitoring of recent and past land
cover and land use change is a fundamental requirement to mitigate the adverse impacts
of Global Change processes. Nowadays, technical advancement and the multitude of
available satellite sensors, in combination with the opening of data archives, allow the
creation of new research perspectives in regional land cover applications over heterogeneous landscapes at large spatial scales. Despite the urgent need to better understand the
prevailing dynamics and underlying factors influencing the current processes, detailed
regional specific land cover data and change information are surprisingly absent for this
region.
In view of the noted research gaps and contemporary developments, three major objectives are defined in this thesis. First (i), the current and most pressing social-ecological
challenges are elaborated and policy and management instruments towards more sustainability are discussed. Second (ii), this thesis provides new and improved insights on
the current land cover state and dynamics of the entire Yellow River Basin. Finally (iii),
the most dominant processes related to mining, agriculture, forest, and urban dynamics
are determined on finer spatial and temporal scales.
The complex and manifold problems and challenges that result from long-term abuse
of the water and land resources in the basin have been underpinned by policy choices,
cultural attitude, and institutions that have evolved over centuries in China. The tremendous economic growth that has been mainly achieved by extracting water and exploiting
land resources in a rigorous, but unsustainable manner, might not only offset the economic benefits, but could also foster social unrest. Since the early emergence of the first Chinese dynasties, flooding was considered historically as a primary issue in river management and major achievements have been made to tame the wild nature of the Yellow
River. Whereas flooding is therefore largely now under control, new environmental and
social problems have evolved, including soil and water pollution, ecological degradation,
biodiversity decline, and food security, all being further aggravated by anthropogenic
climate change. To resolve the contemporary and complex challenges, many individual
environmental laws and regulations have been enacted by various Chinese ministries.
However, these policies often pursue different, often contradictory goals, are too general
to tackle specific problems and are usually implemented by a strong top-down approach.
Recently, more flexible economic and market-based incentives (pricing, tradable permits,
investments) have been successfully adopted, which are specifically tailored to the respective needs, shifting now away from the pure command and regulating instruments.
One way towards a more holistic and integrated river basin management could be the
establishment of a common platform (e.g. a Geographical Information System) for data
handling and sharing, possibly operated by the Yellow River Basin Conservancy Commission (YRCC), where available spatial data, statistical information and in-situ measures
are coalesced, on which sustainable decision-making could be based. So far, the collected
data is hardly accessible, fragmented, inconsistent, or outdated.
The first step to address the absence and lack of consistent and spatially up-to-date
information for the entire basin capturing the heterogeneous landscape conditions was
taken up in this thesis. Land cover characteristics and dynamics were derived from
the last decade for the years 2003 and 2013, based on optical medium-resolution hightemporal MODIS Normalized Differenced Vegetation Index (NDVI) time series at 250 m.
To minimize the inherent influence of atmospheric and geometric interferences found in
raw high temporal data, the applied adaptive Savitzky-Golay filter successfully smoothed
the time series and substantially reduced noise. Based on the smoothed time series
data, a large variety of intra-annual phenology metrics as well as spectral and multispectral annual statistics were derived, which served as input variables for random
forest (RF) classifiers. High quality reference data sets were derived from very high
resolution imagery for each year independently of which 70 % trained the RF models. The
accuracy assessments for all regionally specific defined thematic classes were based on the
remaining 30 % reference data split and yielded overall accuracies of 87 % and 84 % for
2003 and 2013, respectively. The first regional adapted Yellow River Land Cover Products
(YRB LC) depict the detail spatial extent and distribution of the current land cover status
and dynamics. The novel products overall differentiate overall 18 land cover and use
classes, including classes of natural vegetation (terrestrial and aquatic), cultivated classes,
mosaic classes, non-vegetated, and artificial classes, which are not presented in previous
land cover studies so far.
Building on this, an extended multi-faceted land cover analysis on the most prominent
land cover change types at finer spatial and temporal scales provides a better and more
detailed picture of the Yellow River Basin dynamics. Precise spatio-temporal products
about mining, agriculture, forest, and urban areas were examined from long-trem Landsat
satellite time series monitored at annual scales to capture the rapid rate of change in four
selected focus regions. All archived Landsat images between 2000 and 2015 were used to
derive spatially continuous spectral-temporal, multi-spectral, and textural metrics. For
each thematic region and year RF models were built, trained and tested based on a stablepixels reference data set. The automated adaptive signature (AASG) algorithm identifies those pixels that did not change between the investigated time periods to generate a
mono-temporal reference stable-pixels data set to keep manual sampling requirements
to a minimum level. Derived results gained high accuracies ranging from 88 % to 98 %.
Throughout the basin, afforestation on the Central Loess Plateau and urban sprawl are
identified as most prominent drivers of land cover change, whereas agricultural land
remained stable, only showing local small-scale dynamics. Mining operations started in
2004 on the Qinghai-Tibet Plateau, which resulted in a substantial loss of pristine alpine
meadows and wetlands.
In this thesis, a novel and unique regional specific view of current and past land cover
characteristics in a complex and heterogeneous landscape was presented by using a
multi-source remote sensing approach. The delineated products hold great potential for
various model and management applications. They could serve as valuable components
for effective and sustainable land and water management to adapt and mitigate the
predicted consequences of Global Change processes.