Satellite-derived vegetation phenology has been recognized as a key indicator for detecting changes in the terrestrial biosphere in response to global climate change. However, multi-decadal changes and spatial variation of vegetation phenology over the Northern Hemisphere and their relationship to climate change have not yet been fully investigated. In this article, we investigated the spatial variability and temporal trends of vegetation phenology over the Northern Hemisphere by calibrating and analyzing time series of the satellite-derived normalized difference vegetation index (NDVI) during 1982–2012, and then further examine how vegetation phenology responds to climate change within different ecological zones. We found that during the period from 1982 to 2012 most of the high latitude areas experienced an increase in growing period largely due to an earlier beginning of vegetation growing season (BGS), but there was no significant trend in the vegetation growing peaks. The spatial pattern of phenology within different eco-zones also experienced a large variation over the past three decades. Comparing the periods of 1982–1992, 1992–2002 with 2002–2012, the spatial pattern of change rate of phenology shift (RPS) shows a more significant trend in advancing of BGS, delaying of EGS (end of growing season) and prolonging of LGS (length of growing season) during 2002–2012, overall shows a trend of accelerating change. Temperature is a major determinant of phenological shifts, and the response of vegetation phenology to temperature varied across different eco-zones.

Water quality in alpine rivers on the Tibetan Plateau is a key indicator for eco-environment security in China, which, however, is difficult to be monitored over the plateau. In this study, several regression methods and physicals models based on hyperspectral satellite data and field observations are proposed to monitor critical water quality parameters, including water turbidity (WT), total nitrogen (TN), total phosphorus (TP), and total organic carbon (TOC) in alpine rivers. Three remote sensing and field joint observation experiments were firstly carried out in these rivers in different seasons. Then, novel statistical and semi-analytical models were built to calculate the water quality parameters in these alpine rivers using hyperspectral remote sensing data. An integrated air-ground database containing long-term water quality parameters of alpine rivers was further generated to capture the spatial heterogeneity of the water environment of these rivers by using the novel models. The results indicate that WT, TP, TN, and TOC concentrations showed clear interannual cyclical variations and seasonal variations, which reflect that the water environment has a strong seasonality and are mainly controlled by climate change in the alpine plateau.

Automatic detection of buildings from very high resolution (VHR) satellite images is a current research hotspot in remote sensing and computer vision. However, many irrelevant objects with similar spectral characteristics to buildings will cause a large amount of interference to the detection of buildings, thus making the accurate detection of buildings still a challenging task, especially for images captured in complex environments. Therefore, it is crucial to develop a method that can effectively eliminate these interferences and accurately detect buildings from complex image scenes. To this end, a new building detection method based on the morphological building index (MBI) is proposed in this study. First, the local feature points are detected from the VHR remote sensing imagery and they are optimized by the saliency index proposed in this study. Second, a voting matrix is calculated based on these optimized local feature points to extract built-up areas. Finally, buildings are detected from the extracted built-up areas using the MBI algorithm. Experiments confirm that our proposed method can effectively and accurately detect buildings in VHR remote sensing images captured in complex environments.

Water turbidity is an important indicator for water security and environmental security in the Yellow River estuary. However, due to the complex terrain and harsh climatic environment, it is difficult to monitor the water turbidity over the complex surface of the estuary. In this study we applied a self‐organizing map clustering method, an artificial neural network clustering method, to extract turbidity patterns from the long‐term remote sensing data sets in the Yellow River estuary. Based on the Moderate Resolution Imaging Spectroradiometer data from 2000 to 2015, six turbidity patterns were identified by using the self‐organizing map clustering method: high turbidity pattern, moderate turbidity pattern, low turbidity pattern, very low turbidity pattern, extreme high turbidity pattern and sea ice pattern, and the first four patterns appear every year. All patterns have significant seasonal characteristics, and monthly turbidity is dominated by one of these turbidity patterns. The water turbidity in the estuary has decreased in the past 16 years, and the interannual variation of the turbidity pattern is the result of the combination of the sediment transported into the sea by the Yellow River and the wind and waves on the sea surface.

Over the past decade, rapid landscape pattern change has taken place in many arid and semi-arid regions of China, such as the Yellow River Basin. In this paper, landscape evolution was investigated by the combined use of satellite remote sensing, geographic information system (GIS) and landscape modelling technologies. The aim was to improve our understanding of landscape changes so that sustainable land use could be established. First, the changes in various landscape metrics were analysed using the landscape structure analysis programme. Second, the mathematical methodology was explored and developed for landscape pattern change, which included: the status and trends change model for individual landscape types, the 1-km² area percentage data model and the transition matrix of landscape types. The results show that the area of the Yellow River Basin was about 794 000 km² during the period from 1990 to 2000; cropland, built-up land and unused land expanded significantly whereas woodland, grassland and water bodies contracted substantially. The area of cropland increased dramatically by 2817 km², and the areas of grassland and woodland decreased by 4669 and 33 km², respectively. Meanwhile, the landscape pattern in the study area also experienced numerous changes over the past decade. The major factors that caused the landscape changes in this area over the past decade were found to be governmental policies for environmental protection, population growth, and meteorological and environmental conditions.