The human–environment relationship within the Yellow River basin has a long history, because favorable environmental circumstances allowed the early emergence of societies along the river banks, and hence, the Yellow River basin was the birthplace of ancient Chinese civilization. On the other hand, the Yellow River is “China’s sorrow” due to the constant occurrences of flooding events throughout history. In recent decades, the Yellow River basin is facing a spectacular economic boom, but mainly achieved at the expenses of the environment by over-exploiting the natural resources provided within the basin, which causes various challenges on ecology and society. Water scarcity, pollution, and ecosystem degradation accompanied with biodiversity decline have been further aggravated by anthropogenic-induced climate change. To address the pressing socio-ecological challenges, various conservation and management plans and strategies have been issued, often consulted by international bodies. This article is a comprehensive overview of the current state and recent developments that have occurred in the Yellow River basin and presents and discusses current and pressing socio-ecological challenges. Additionally, we address different policy and management instruments that have been launched to ensure a long-term sustainable development within the basin.

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 R² = 0.56 to 0.78. We identified three hotspots in SE Asia with a total area of 156,000 km², 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.

The Upper Parana Atlantic Forest (BAAPA) in Paraguay is one of the most threatened tropical forests in the world. The rapid growth of deforestation has resulted in the loss of 91% of its original cover. Numerous efforts have been made to halt deforestation activities, however farmers’ perception towards the forest and its benefits has not been considered either in studies conducted so far or by policy makers. This research provides the first multi-temporal analysis of the dynamics of the forest within the BAAPA region on the one hand, and assesses the way farmers perceive the forest and how this influences forest conservation at the farm level on the other. Remote sensing data acquired from Landsat images from 1999 to 2016 were used to measure the extent of the forest cover and deforestation rates over 17 years. Farmers’ influence on the dynamics of the forest was evaluated by combining earth observation data and household survey results conducted in the BAAPA region in 2016. Outcomes obtained in this study demonstrate a total loss in forest cover of 7500 km². Deforestation rates in protected areas were determined by management regimes. The combination of household level and remote sensing data demonstrated that forest dynamics at the farm level is influenced by farm type, the level of dependency/use of forest benefits and the level of education of forest owners. An understanding of the social value awarded to the forest is a relevant contribution towards preserving natural resources.

Since its launch in 2007, TerraSAR-X observations have been widely used in a broad range of scientific applications. Particularly in wetland research, TerraSAR-X’s shortwave X-band synthetic aperture radar (SAR) possesses unique capabilities, such as high spatial and temporal resolution, for delineating and characterizing the inherent spatially and temporally complex and heterogeneous structure of wetland ecosystems and their dynamics. As transitional areas, wetlands comprise characteristics of both terrestrial and aquatic features, forming a large diversity of wetland types. This study reviews all published articles incorporating TerraSAR-X information into wetland research to provide a comprehensive study of how this sensor has been used with regard to polarization, and the function of the data, time-series analyses, or the assessment of specific wetland ecosystem types. What is evident throughout this literature review is the synergistic fusion of multi-frequency and multi-polarization SAR sensors, sometimes optical sensors, in almost all investigated studies to attain improved wetland classification results. Due to the short revisiting time of the TerraSAR-X sensor, it is possible to compute dense SAR time-series, allowing for a more precise observation of the seasonality in dynamic wetland areas as demonstrated in many of the reviewed studies.

The Yellow River Basin is one of China’s most densely-populated, fastest growing and most dynamic regions, with abundant natural resources and intense agricultural production. Major land policies have recently resulted in remarkable landscape modifications throughout the basin. The availability of precise regional land cover change information is crucial to better understand the prevailing dynamics and underlying factors influencing the current processes in such a complex system and can additionally serve as a valuable component for modeling and decision making. Such comprehensive and detailed information is lacking for the Yellow River Basin so far. In this study, we derived land cover characteristics and dynamics from the complete last decade based on optical high-temporal MODIS Normalized Differenced Vegetation Index (NDVI) time series for the whole Yellow River Basin. After filtering and smoothing for noise reduction with the use of the adaptive Savitzky–Golay filter, the processed time series was used to derive a large variety of phenological and annual metrics. The final classifications for the basin (2003 and 2013) were based on a random forest classifier, trained by reference samples from very high-resolution imagery. The accuracy assessment for all 18 thematic classes, which was based on a 30% reference data split, yielded an overall accuracy of 87% and 84% for 2003 and 2013, respectively. Major land cover and land use changes during the last decade have occurred on the Loess Plateau, where land and conservation reforms triggered large-scale recovery of grassland and shrubland habitat that had been previously covered by agriculture or sparse vegetation. Agricultural encroachment and urban area expansion are other processes influencing the dynamics in the basin. The necessity for regionally-adapted land cover maps becomes obvious when our land cover products are compared to existing global products, where thematic accuracy remains low, particularly in a heterogeneous landscape, such as the Yellow River Basin. The basin-wide novel land cover and land use products of the Yellow River Basin hold a large potential for climate, hydrology and biodiversity modelers, as well as river basin and regional governmental authorities and will be shared upon request.