Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers.

In this paper, an Urban Light Index (ULI) is constructed to facilitate analysis and quantitative evaluation of the process of urbanization and expansion rate by using DMSP/OLS Nighttime Light Data during the years from 1992 to 2010. A unit circle urbanization evaluation model is established to perform a comprehensive analysis of the urbanization process of 34 prefecture-level cities in Northeast China. Furthermore, the concept of urban light space is put forward. In this study, urban light space is divided into four types: the core urban area, the transition zone between urban and suburban areas, suburban area and fluorescent space. Proceeding from the temporal and spatial variation of the four types of light space, the pattern of morphologic change and space-time evolution of the four principal cities in Northeast China (Harbin, Changchun, Shenyang, Dalian) is analyzed and given particular attention. Through a correlation analysis between ULI and the traditional urbanization indexes (urban population, proportion of the secondary and tertiary industries in the regional GDP and the built-up area), the advantages and disadvantages as well as the feasibility of using the ULI in the study of urbanization are evaluated. The research results show that ULI has a strong correlation with urban built-up area (R2 = 0.8277). The morphologic change and history of the evolving urban light space can truly reflect the characteristics of urban sprawl. The results also indicate that DMSP/OLS Nighttime Light Data is applicable for extracting urban space information and has strong potential to urbanization research.

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This paper describes the long-term effects on vegetation following the catastrophic fire in 1987 on the northern Great Xing'an Mountain by analyzing the AVHRR GIMMS 15-day composite normalized difference vegetation index (NDVI) dataset. Both temporal and spatial characteristics were analyzed for natural regeneration and tree planting scenarios from 1984 to 2006. Regressing post-fire NDVI values on the pre-fire values helped identify the NDVI for burnt pixels in vegetation stands. Stand differences in fire damage were classified into five levels: Very High (VH), High (H), Moderate (M), Low (L) and Slight (S). Furthermore, intra-annual and inter-annual post-fire vegetation recovery trajectories were analyzed by deriving a time series of NDVI and relative regrowth index (RRI) values for the entire burned area. Finally, spatial pattern and trend analyses were conducted using the pixel-based post-fire annual stands regrowth index (SRI) with a nonparametric Mann-Kendall (MK) statistics method. The results show that October was a better period compared to other months for distinguishing the post-and pre-fire vegetation conditions using the NDVI signals in boreal forests of China because colored leaves on grasses and shrubs fall down, while the leaves on healthy trees remain green in October. The 6939MK statistics method is robustly capable of detecting vegetation trends in a relatively long time series. Because tree planting primarily occurred in the severely burned area (approximately equal to the Medium, High and Very High fire damage areas) following the Daxing'anling fire in 1987, the severely burned area exhibited a better recovery trend than the lightly burned regions. Reasonable tree planting can substantially quicken the recovery and shorten the restoration time of the target species. More detailed satellite analyses and field data will be required in the future for a more convincing validation of the results.

Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) time series (1982-2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal (MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends (P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.