Given the ability of effectively observing vegetation at a variety of spatial and temporal scales, remote sensing has been widely used to monitor and understand the change of mangrove forest extent. Yet, a systematic cognition of the unique contribution of remote sensing on studying mangrove forest extent dynamic is lacking, which prevents us from clearly identifying and further overcoming current deficiencies of studying the change of mangrove forest extent with remote sensing. Therefore, in this review, we summarized remotely sensed extraction methods and data related to mangrove forest extent change monitoring and all discoveries from mangrove forest extent change observation and driver analysis. We found that mangrove forest in most of the study areas has declined over the past decades, and the loss rate is accelerating. We also found that remote sensing plays an important role in revealing how the extent of mangrove forest responds to climate change and human impacts. In addition, we identified several recurring limitations from previous studies. This review highlights the role of remote sensing on studying mangrove forest extent change, and is helpful to make better use of remotely sensed data in this field.

The existence of noise in hyperspectral imagery (HSI) seriously affects image quality. Noise removal is one of the most important and challenging tasks to complete before hyperspectral information extraction. Though many advances have been made in alleviating the effect of noise, problems, including a high correlation among bands and predefined structure of noise covariance, still prevent us from the effective implementation of hyperspectral denoising. In this letter, a new algorithm named the penalized linear discriminant analysis (PLDA) and noise adjusted principal components transformation (NAPCT) was proposed. PLDA was applied to search for the best noise covariance structure, while the NAPCT was employed to remove the noise. The results of the tests with both HJ-1A HSI and EO-1 Hyperion showed that the proposed PLDA-NAPCT method could remove the noise effectively and that it could preserve the spectral fidelity of the restored hyperspectral images. Specifically, the recovered spectral curves using the proposed method are visually more similar to the original image compared with the control methods; quantitative matrices, including the noise reduction ration and mean relative deviation, also showed that the PLDA-NAPCT produced less bias than the control methods. Furthermore, the PLDA-NAPCT method is sensor-independent, and it could be easily adapted for removing the noise from different sensors.