Shadows in high-spatial-resolution remote-sensing images become more pronounced. The detection of shadows is an essential requirement for both detailed high-spatial land-cover classification and applications such as three-dimensional (3D) reconstruction of buildings as well as cloud removal. This article presents a method for integrating the photochemical reflectance index (PRI) and Red Edge normalized difference vegetation index (RENDVI) for shadow identification (IPRSI) using high-spatial-resolution airborne hyperspectral data. This method detects shadows by setting thresholds to the PRI and RENDVI to separate shadows from vegetated and non-vegetated areas. The proposed method outperformed the invariant colour spaces model and the object-based method in terms of shadow extraction accuracy. The overall shadow identification accuracy of the IPRSI was 88.97% with an F-score of 90.96 (81.32% with F-score 81.97 for the invariant colour spaces model and 78.02% with F-score 82.07 for the object-based method). The IPRSI is a potential method with the wide application of hyperspectral data in high spatial resolution that is increasingly easier to be obtained with the development of remote-sensing platforms (such as unmanned aerial vehicles (UAVs), small satellites, and airships).

As an important economic resource, rubber has rapidly grown in Xishuangbanna of Yunnan Province, China, since the 1990s. Tropical rainforests have been replaced by extensive rubber plantations, which has resulted in ecological problems such as the loss of biodiversity and local water shortages. It is vitally important to accurately map the rubber plantations in this region. Although several rubber mapping methods have been proposed, few studies have investigated methods based on optical remote sensing time series data with high spatio-temporal resolution due to the cloudy and foggy weather conditions in this area. This study presented a rubber plantation identification method that used spatio-temporal optical remote sensing data fusion technology to obtain vegetation index data at high spatio-temporal resolution within the optical remote sensing window in Xishuangbanna. The analysis of the proposed method shows that (1) fused optical remote sensing data with high spatio-temporal resolution could map the rubber distribution with high accuracy (overall accuracy of up to 89.51% and kappa of 0.86). (2) Fused indices have high R² (R² greater than 0.8, where R is the correlation coefficient) with the indices that were derived from the Landsat observed data, which indicates that fusion results are dependable. However, the fusion accuracy is affected by terrain factors including elevation, slope, and slope aspects. These factors have obvious negative effects on the fusion accuracy of high spatio-temporal resolution optical remote sensing data: the highest fusion accuracy occurred in areas with elevations between 1201 and 1400 m.a.s.l., and the lowest accuracy occurred in areas with elevations less than 600 m.a.s.l. For the 5 fused time series indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and tasseled cap angle (TCA)), the fusion accuracy decreased with increasing slope, and increasing slope had the least impact on the EVI, but the greatest negative impact on the NDVI; the slope aspect had a limited influence on the fusion accuracies of the 5 time series indices, but fusion accuracy was lowest on the northwest slope. (3) EVI had the highest accuracy of rubber plantation classification among the 5 time series indices, and the overall classification accuracies of the time series EVI for the four different years (2000, 2005, 2010, and 2015) reached 87.20% (kappa 0.82), 86.91% (kappa 0.81), 88.85% (kappa 0.84), and 89.51% (kappa 0.86), respectively. The results indicate that the method is a promising approach for rubber plantation mapping and the detection of changes in rubber plantations in this tropical area.

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural events is the dominant factor in inland lake surface area change. In this study, we proposed a method that combines the Douglas-Peucker simplification algorithm and the bend simplification algorithm to locate major lake surface area disturbances. These disturbances were used to extract the features that been used to classify disturbances into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31-year long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records were used as references to aid the feature extraction and disturbance identification accuracy assessment. Results of our method for disturbance location and disturbance identification could be concluded as follows: (1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the time series of lake surface area in our study area was 94.73%. (2) Our proposed method achieved an overall accuracy of 87.75%, with an F-score of 85.71 for anthropogenic disturbances and an F-score of 88.89 for natural disturbances. (3) According to our results, lakes in Yunnan Province of China have undergone intensive disturbances. Human-induced disturbances occurred almost twice as much as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or a natural event, and to monitor whether disturbances of lake surface area are intensified for a region.