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Updated extent, area, and spatial distribution of tropical evergreen forests from inventory data provides valuable knowledge for research of the carbon cycle, biodiversity, and ecosystem services in tropical regions. However, acquiring these data in mountainous regions requires labor-intensive, often cost-prohibitive field protocols. Here, we report about validated methods to rapidly identify the spatial distribution of tropical forests, and obtain accurate extent estimates using phenology-based procedures that integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. Firstly, an analysis of temporal profiles of annual time-series MODIS Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) was developed to identify the key phenology phase for extraction of tropical evergreen forests in five typical lands cover types. Secondly, identification signatures of tropical evergreen forests were selected and their related thresholds were calculated based on Landsat NDVI, EVI, and LSWI extracted from ground true samples of different land cover types during the key phenology phase. Finally, a map of tropical evergreen forests was created by a pixel-based thresholding. The developed methods were tested in Xishuangbanna, China, and the results show: (1) Integration of Landsat and MODIS images performs well in extracting evergreen forests in tropical complex mountainous regions. The overall accuracy of the resulting map of the case study was 92%; (2) Annual time series of high-temporal-resolution remote sensing images (MODIS) can effectively be used for identification of the key phenology phase (between Julian Date 20 and 120) to extract tropical evergreen forested areas through analysis of NDVI, EVI, and LSWI of different land cover types; (3) NDVI and LSWI are two effective metrics (NDVI ≥ 0.670 and 0.447 ≥ LSWI ≥ 0.222) to depict evergreen forests from other land cover types during the key phenology phase in tropical complex mountainous regions. This method can make full use of the Landsat and MODIS archives as well as their advantages for tropical evergreen forests geospatial inventories, and is simple and easy to use. This method is suggested for use with other similar regions.

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Abstract.Root diseases are known to suppress forest regeneration and reduce growth rates, and they may become more common as susceptible tree species become maladapted in parts of their historic ranges due to climate change. However, current ecosystem models do not track the effects of root disease on net productivity, and there has been little research on how the dynamics of root disease affect carbon (C) storage and productivity across infected landscapes. We compared the effects of root disease against the effects of other types of forest disturbance across six national forest landscapes, 1990-2011. This was enabled by a monitoring tool called the Forest Carbon Management Framework (ForCaMF), which makes use of ground inventory data, an empirical growth model, and time series of Landsat satellite imagery. Despite several large fires that burned across these landscapes during the study period, retrospective ForCaMF analysis showed that fire and root disease had approximately equal impacts on C storage. Relative to C accumulation that would have occurred in their absence, fires from 1990 to 2011 were estimated to reduce regionwide C storage by 215.3 ± 19.1 g/m 2 C, while disease in the same period was estimated to reduce storage by 211.4 ± 59.9 g/m 2 C. Harvest (75.5 ± 13.5 g/m 2 C) and bark beetle activity (14.8 ± 12.5 g/m 2 C) were less important. While long-term disturbance processes such as root disease have generally been ignored by tools informing management of forest C storage, the recent history of several national forests suggests that such disturbances can be just as important to the C cycle as more conspicuous events like wildfires.

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