Active wildfire seasons in the western U.S. warrant the evaluation of post-fire forest management strategies. Ground-based salvage logging is often used to recover economic loss of burned timber. In unburned forests, ground-based logging often follows best management practices by leaving undisturbed areas near streams called stream buffers. However, the effectiveness of these buffers has not been tested in a post-wildfire setting. This experiment tested buffer width effectiveness with a novel field-simulated rill experiment using sediment-laden runoff (25 g/L) released over 40 min at evenly timed flow rates (50, 100 and 150 L/min) to measure surface runoff travel length and sediment concentration under unburned and high and low soil burn severity conditions at 2-, 10- and 22-month post-fire. High severity areas 2-month post-fire had rill lengths of up to 100 m. Rill length significantly decreased over time as vegetation regrowth provided ground cover. Sediment concentration and sediment dropout rate also varied significantly by soil burn severity. Sediment concentrations were 19 g/L for the highest flow 2-month post-fire and reduced to 6.9–14 g/L 10-month post-fire due to abundant vegetation recovery. The amount of sediment dropping out of the flow consistently increased over the study period with the low burn severity rate of 1.15 g L−1 m−1 approaching the unburned rate of 1.29 g L−1 m−1 by 2-year post-fire. These results suggest that an often-used standard, 15 m buffer, was sufficient to contain surface runoff and reduce sediment concentration on unburned sites, however buffers on high burn severity sites need to be eight times greater (120 m) immediately after wildfire and four times greater (60 m) 1-year post-fire. Low burn severity areas 1-year post-fire may need to be only twice the width of an unburned buffer (30 m), and 2-year post-fire these could return to unburned widths. 相似文献
Simulating land use/cover change (LUCC) and determining its transition rules have been a focus of research for several decades. Previous studies used ordinary logistic regression (OLR) to determine transition rules in cellular automata (CA) modeling of LUCC, which often neglected the spatially non-stationary relationships between driving factors and land use/cover categories. We use an integrated geographically weighted logistic regression (GWLR) CA-Markov method to simulate LUCC from 2001–2011 over 29 towns in the Connecticut River Basin. Results are compared with those obtained from the OLR-CA-Markov method, and the sensitivity of LUCC simulated by the GWLR-CA-Markov method to the spatial non-stationarity-based suitability map is investigated. Analysis of residuals indicates better goodness of fit in model calibration for geographically weighted regression (GWR) than OLR. Coefficients of driving factors indicate that GWLR outperforms OLR in depicting the local suitability of land use/cover categories. Kappa statistics of the simulated maps indicate high agreement with observed land use/cover for both OLR-CA-Markov and GWLR-CA-Markov methods. Similarity in simulation accuracy between the methods suggests that the sensitivity of simulated LUCC to suitability inputs is low with respect to spatial non-stationarity. Therefore, this study provides critical insight on the role of spatial non-stationarity throughout the process of LUCC simulation. 相似文献
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.
