Tropical Rainfall Measurement Mission (TRMM) is one of the most popular global high resolution satellite-based precipitation products with a goal of measuring precipitation over the oceans and tropics. However, in recent years, the TRMM mission has come to an end. Its successor, Global Precipitation Measurement (GPM) mission was launched to measure the earth's precipitation structure, with an aim to improve upon the TRMM project. Both of the precipitation products have their own strengths and weaknesses in resolution, accuracy, and availability. The aim of this study is to evaluate the hydrologic utilization of the TRMM and GPM products in a humid basin of China. The main findings of this study can be summarized as follows: (1) 3B42V7 generally outperforms 3B42V6 in terms of hydrologic performance. Meanwhile, 3B42RTV7 significantly outperforms 3B42RTV6, and showed close performance with the bias-adjusted TRMM Multi-satellite Precipitation Analysis (TMPA) products. (2) The GPM showed better agreement with gauge observation than the TMPA products with lower RB and higher correlation coefficient (CC) values at different time scales. (3) The VIC hydrological model generally outperformed the XAJ hydrological model with lower RB, higher Nash–Sutcliffe Coefficient of Efficiency (NSCE) and CC values; though the 3B42RTV6 and 3B42RTV7 showed higher CC values in simulating the streamflow hydrograph by using the VIC and XAJ hydrological models. It can be found that the conceptual hydrological model was enough for the hydrologic evaluation of TRMM and GPM IMERG satellite-based precipitation in a humid basin of China. This study provides a reference for the comparison of multiple models on watershed scale.
The severity of wildfire burns in interior lands of western US ecosystems has been increasing. However, less is known about its coastal mountain ecosystems, especially under extreme weather conditions, raising concerns about the vulnerability of these populated areas to catastrophic fires. Here we examine the fine-scale association between burn severity and a suite of environmental drivers including explicit fuel information, weather, climate, and topography, for diverse ecosystems in California’s northern coastal mountains. Burn severity was quantified using Relative difference Normalized Burn Ratio from Landsat multispectral imagery during 1984–2017. We found a significant increasing trend in burned areas and severity. During low-precipitation years, areas that burned had much lower fuel moisture and higher climatic water deficit than in wetter years, and the percentage of high-severity areas doubled, especially during the most recent 2012–2016 drought. The random forest (RF) machine learning model achieved overall accuracy of 79% in classifying categories of burn severity. Aspect, slope, fuel type and availability, and temperature were the most important drivers, based on both classification and regression RF models. We further examined the importance of drivers under four climatic conditions: dry vs. wet years, and during two extended drought periods (the 2012–2016 warmer drought vs. the 1987–1992 drought). During warm and dry years, the spatial variability of burn severity was a mixed effect of slope, long-term minimum temperature, fuel amount, and fuel moisture. In contrast, climatic water deficit and short-term weather became dominant factors for fires during wetter years. These results suggest that relative importance of drivers for burn severity in the broader domain of California’s northern coastal mountains varied with weather scenarios, especially when exacerbated by warm and extended drought. Our findings highlight the importance of targeting areas with high burn severity risk for fire adaptation and mitigation strategies in a changing climate and intensifying extremes.
Abstract:Building type information is crucial to many urban studies, including fine-resolution population estimation, urban planning, and management. Although scientists have developed many methods to extract buildings via remote sensing data, only a limited number of them focus on further classification of the extracted results. This paper presents a novel building type classification scheme based on the integration of building height information from LiDAR, textural, spectral, and geometric information from high-resolution remote sensing images, and super-object information from the integrated dataset. Building height information is firstly extracted from LiDAR point clouds using a progressive morphological filter and then combined with high-resolution images for object-oriented segmentation. Multi-resolution segmentation of the combined image is performed to collect super-object information, which provides more information for classification in the next step. Finally, the segmentation results, as well as their super-object information, are inputted into the random forest classifier to obtain building type classification results. The classification scheme proposed in this study is tested through applications in two urban village areas, a type of slum-like land use characterized by dense buildings of different types, heights, and sizes, in Guangzhou, China. Segment level classification of the study area and validation area reached accuracies of 80.02% and 76.85%, respectively, while the building-level results reached accuracies of 98.15% and 87.50%, respectively. The results indicate that the proposed building type classification scheme has great potential for application in areas with multiple building types and complex backgrounds. This study also proves that both building height information and super-object information play important roles in building type classification. More accurate results could be obtained by incorporating building height information and super-object information and using the random forest classifier.
Climate change mitigation policies have usually considered forest-based actions as cheap and fast options to reduce CO 2 concentration in the atmosphere and slow down global warming. Most economic analyses, however, have ignored the effects of these actions on land surface albedo and the resulting effect on energy balance and temperature. This study estimates the marginal cost of forest mitigation associated with both carbon sequestration and albedo change, by introducing regional and forest-specific albedo information in a global dynamic forestry model. Our analysis indicates that traditional forest sequestration policies have underestimated the costs of climate mitigation, driving forest-based actions in regions where subsequent changes in albedo are significant. To reduce this inefficiency, this paper proposes a novel approach where both carbon sequestration and albedo effect are incorporated into pricing. Our results suggest that, under the same carbon price path, the integrative policy provides greater net global mitigation in absolute terms and per hectare of forest, and thus it is more efficient and less intrusive than the traditional policy.
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