Forest plays an important role in global carbon, hydrological and atmospheric cycles and provides a wide range of valuable ecosystem services. Timely and accurate forest-type mapping is an essential topic for forest resource inventory supporting forest management, conservation biology and ecological restoration. Despite efforts and progress having been made in forest cover mapping using multi-source remotely sensed data, fine spatial, temporal and spectral resolution modeling for forest type distinction is still limited. In this paper, we proposed a novel spatial-temporal-spectral fusion framework through spatial-spectral fusion and spatial-temporal fusion. Addressing the shortcomings of the commonly-used spatial-spectral fusion model, we proposed a novel spatial-spectral fusion model called the Segmented Difference Value method (SEGDV) to generate fine spatial-spectra-resolution images by blending the China environment 1A series satellite (HJ-1A) multispectral image (Charge Coupled Device (CCD)) and Hyperspectral Imager (HSI). A Hierarchical Spatiotemporal Adaptive Fusion Model (HSTAFM) was used to conduct spatial-temporal fusion to generate the fine spatial-temporal-resolution image by blending the HJ-1A CCD and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The spatial-spectral-temporal information was utilized simultaneously to distinguish various forest types. Experimental results of the classification comparison conducted in the Gan River source nature reserves showed that the proposed method could enhance spatial, temporal and spectral information effectively, and the fused dataset yielded the highest classification accuracy of 83.6% compared with the classification results derived from single Landsat-8 (69.95%), single spatial-spectral fusion (70.95%) and single spatial-temporal fusion (78.94%) images, thereby indicating that the proposed method could be valid and applicable in forest type classification.

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.

Floods occur frequently, impacting large areas, displacing thousands of people and causing great losses. Unmanned Aerial Vehicle (UAV) remote sensing is easy to obtain high‐resolution image that is immensely helpful to timely assess the flood situation and provide scientific decision‐making for emergency rescue. The important roles of UAV remote sensing have been widely recognized. However, the sudden onset of floods and lack of UAV resources deployed nearby have restricted rapid response of UAVs in emergency rescue. A UAV remote sensing observation network on a regional scale has been proposed to deal with these emergencies. However, how to build this UAV network and where to deploy UAV resources are still mysterious. In this study, the Maximum Covering Location Problem (MCLP) model was improved to distribute a series number of droneports in China. Finally, 81 droneports were selected from 268 potential facility points, which covered 61.84% risk of total demand area. Droneports have been allocated near flood‐prone areas and most floods in China can be monitored within 2 hours, which is critical for saving lives and reducing losses. The construction of UAV airport networks will surely contribute to an integrated disaster emergency observation system combining satellite, airplane, UAV, and ground observations in China.

Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites.

Super-resolution (SR) technology has shown great potential for improving the performance of the mapping and classification of multispectral satellite images. However, it is very challenging to solve ill-conditioned problems such as mapping for remote sensing images due to the presence of complicated ground features. In this paper, we address this problem by proposing a super-resolution reconstruction (SRR) mapping method called the mixed sparse representation non-convex high-order total variation (MSR-NCHOTV) method in order to accurately classify multispectral images and refine object classes. Firstly, MSR-NCHOTV is employed to reconstruct high-resolution images from low-resolution time-series images obtained from the Gaofen-4 (GF-4) geostationary orbit satellite. Secondly, a support vector machine (SVM) method was used to classify the results of SRR using the GF-4 geostationary orbit satellite images. Two sets of GF-4 satellite image data were used for experiments, and the MSR-NCHOTV SRR result obtained using these data was compared with the SRR results obtained using the bilinear interpolation (BI), projection onto convex sets (POCS), and iterative back projection (IBP) methods. The sharpness of the SRR results was evaluated using the gray-level variation between adjacent pixels, and the signal-to-noise ratio (SNR) of the SRR results was evaluated by using the measurement of high spatial resolution remote sensing images. For example, compared with the values obtained using the BI method, the average sharpness and SNR of the five bands obtained using the MSR-NCHOTV method were higher by 39.54% and 51.52%, respectively, and the overall accuracy (OA) and Kappa coefficient of the classification results obtained using the MSR-NCHOTV method were higher by 32.20% and 46.14%, respectively. These results showed that the MSR-NCHOTV method can effectively improve image clarity, enrich image texture details, enhance image quality, and improve image classification accuracy. Thus, the effectiveness and feasibility of using the proposed SRR method to improve the classification accuracy of remote sensing images was verified.

Human-induced dramatic loss and fragmentation of wetlands need further understanding through historical backtracking analysis at a geographical landscape scale. In this study, we investigated time-series wetlands maps from 1975, 1983, 1989, 2000, 2006, and 2013 derived from Landsat images based on the object-oriented classification of wetlands across the Sanjiang Plain north of the Wandashan Mountains. The spatial and temporal changes in the wetlands that occurred at different time periods and the Euclidean distances between artificial land-use types and natural land-cover areas were evaluated for their impact. Our results showed that wetland was the dominant landscape in 1975; however, arable land became the main land coverage in 2013 owing to severe changes in agricultural development over the past decades. The closer to arable land, the greater the wetland loss during the entire investigated period; agriculture activities were the dominant driving force for the degradation of wetlands based on landscape changes; secondary was the rapid expansion in building land use (i.e., human settlement, transportation, and establishment of irrigation canals). More specifically, the rapid loss of wetland areas over 1975–2000 was mainly owing to extensive agricultural reclamation. The mitigated loss of wetland areas over 2000–2013 was because of the protection and restored implementation of wetlands under governmental policies. The wetlands of the study area suffered severe human disturbance, and our analysis may help explain the loss process of wetlands, but more effective management and administration is still needed to address the issues around the balance between agricultural production and wetland protection for further sustainable development.