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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.
Mapping threatened dry deciduous dipterocarp forest in South-east Asia for conservation management
(2014)
Habitat loss is the primary reason for species extinction, making habitat conservation a critical strategy for maintaining global biodiversity. Major habitat types, such as lowland tropical evergreen forests or mangrove forests, are already well represented in many conservation priorities, while others are underrepresented. This is particularly true for dry deciduous dipterocarp forests (DDF), a key forest type in Asia that extends from the tropical to the subtropical regions in South-east Asia (SE Asia), where high temperatures and pronounced seasonal precipitation patterns are predominant. DDF are a unique forest ecosystem type harboring a wide range of important and endemic species and need to be adequately represented in global biodiversity conservation strategies. One of the greatest challenges in DDF conservation is the lack of detailed and accurate maps of their distribution due to inaccurate open-canopy seasonal forest mapping methods. Conventional land cover maps therefore tend to perform inadequately with DDF. Our study accurately delineates DDF on a continental scale based on remote sensing approaches by integrating the strong, characteristic seasonality of DDF. We also determine the current conservation status of DDF throughout SE Asia. We chose SE Asia for our research because its remaining DDF are extensive in some areas but are currently degrading and under increasing pressure from significant socio-economic changes throughout the region. Phenological indices, derived from MODIS vegetation index time series, served as input variables for a Random Forest classifier and were used to predict the spatial distribution of DDF. The resulting continuous fields maps of DDF had accuracies ranging from R-2 = 0.56 to 0.78. We identified three hotspots in SE Asia with a total area of 156,000 km(2), and found Myanmar to have more remaining DDF than the countries in SE Asia. Our approach proved to be a reliable method for mapping DDF and other seasonally influenced ecosystems on continental and regional scales, and is very valuable for conservation management in this region.