Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data

Information regarding the extent, timing and magnitude of forest disturbance are key inputs required for accurate estimation of the terrestrial carbon balance. Equally important for studying carbon dynamics is the ability to distinguish the cause or type of forest disturbance occurring on the landscape. Wildfire and timber harvesting are common disturbances occurring in boreal forests, with each having differing carbon consequences (i.e., biomass removed, recovery rates). Development of methods to not only map, but distinguish these types of disturbance with satellite data will depend upon an improved understanding of their distinctive spectral properties. In this study, we mapped wildfires and clearcut harvests occurring in a Landsat time series (LTS) acquired in the boreal plains of Saskatchewan, Canada. This highly accurate reference map (kappa = 0.91) depicting the year and cause of historical disturbances was used to determine the spectral and temporal properties needed to accurately classify fire and clearcut disturbances. The results showed that spectral data from the short-wave infrared (SWIR; e.g., Landsat band 5) portion of the electromagnetic spectrum was most effective at separating fires and clearcut harvests possibly due to differences in structure, shadowing, and amounts of exposed soil left behind by the two disturbance types. Although SWIR data acquired 1 year after disturbance enabled the most accurate discrimination of fires and clearcut harvests, good separation (e.g., kappa ≥ 0.80) could still be achieved with Landsat band 5 and other SWIR-based indices 3 to 4 years after disturbance. Conversely, minimal disturbance responses in near infrared-based indices associated with green leaf area (e.g., NDVI) led to unreliably low classification accuracies regardless of time since disturbance. In addition to exploring the spectral and temporal manifestation of forest disturbance types, we also demonstrate how Landsat change maps which attribute cause of disturbance can be used to help elucidate the social, ecological and carbon consequences associated with wildfire and clearcut harvesting in Canadian boreal forests.     

基于TM影像的衡阳市城区扩展趋势研究

城镇化是我国经济发展必然产物,城市扩展是城镇化进程的一个重要指标。遥感技术具有快捷、便利、大面积同步观测等特征,已经广泛应用于对城市扩展的监测。以衡阳市为研究对象,选取该区域1989年、2006年和2010年的Landsat TM遥感影像作为数据源,利用植被、水体和建筑物指数特征和监督分类方法提取3 a （年）中衡阳市城区边界信息,最后采用GIS的空间分析技术,从扩展强度、扩展方向2个方面来讨论衡阳市城区的扩展空间特征。     

TM热红外波段等效比辐射率估算

地表比辐射率是热红外遥感获取地表温度必不可少的参数。目前,实验室或野外实际测量的都是8~14um热红外波段范围内的地表比辐射率,这与Landsat 5 TM热红外波段10.4~12.5um范围内的地表比辐射率还存在着一定的差异。本文将着重探讨TM热红外范围内地表比辐射率的估算方法,然后根据估算出的地表比辐射率,利用覃志豪等提出的单窗算法[1~2],对北京城八区进行地表温度反演。结果表明,该方法能获得较为合理的地表温度反演结果。     

Comparing MODIS and ETM+ data for regional and global land classification

Nearly simultaneous reflectance data sets from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), at 30-m resolution, and the Terra satellite instrument MODIS, at 500-m resolution, are compared for their ability to map fractional coverage of surface types over large areas. Lower spatial resolution MODIS classification results are generally comparable those of ETM+, with discrepancies for some regions with mixed surface types. Analysis of laboratory and field spectra suggests an ambiguity, the “brightness ambiguity”, which can prevent accurate area estimation of pixels having two or more surface types. This ambiguity, plus general mathematical inversion issues, can account for the discrepancy. Thus, occasional high-resolution measurements, as from Landsat 7, are necessary to refine estimations of large area surface types from MODIS and similar lower spatial resolution instruments.     

Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAI_e) (r²=0.55, p<0.001) and with the lidar-derived estimate of tree occurrence (r²=0.79, p<0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response (r²=0.30, r=−0.55 and r²=0.41, r=−0.64, respectively; p<0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction (r²=0.99, p<0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures (r²=0.77, p<0.001) than at 4 m resolution (r²=0.58, p<0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data (r²=0.66, p<0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem.     

An innovative method for determining hydrological calibration parameters for the WRF-Hydro model in arid regions

The techniques presented herein allow to directly determine certain crucial calibration parameters for the WRF-Hydro flood forecasting model. Typically, calibrations are chosen by an iterative, empirical, trial and error procedure. We suggest a more systematic methodology to arrive at a usable calibration. Our method is based on physical soil properties and does not depend on observed runoff from certain basins during specific storm events. Three specific calibration variables that most strongly affect the runoff predictions are addressed: topographic slope, saturated hydraulic conductivity, and infiltration. We outline a procedure for creating spatially distributed values for each of the three variables. Simulation runs are performed covering several storm events with calculated calibrations, with default values, and with an expert calibration. We show that our calibration, derived solely from soil physical properties, achieves forecast skill better than the default calibration and at least as good as an expert based calibration.     

Evaluating Land Surface Changes of Makassar City Using DInSAR and Landsat Thematic Mapper Images

Urban growth has been a major issue in environmental monitoring and changes occurred on land surfaces have been monitored by applying remote sensing as well as ground measurement. Most major cities in the world have experienced land subsidence phenomena on some parts of them due to the load of development and modernization. Excessive extraction of groundwater for the needs of industry has led to the condition where the water table drops, and this can possibly trigger subsidence, as observed in Indonesian cities. In this study the authors have shown that the application of DInSAR （differential interferometric synthetic aperture radar） technique using Japanese Earth Resources Satellite-I Synthetic Aperture Radar JERS-I SAR data can reveal subsidence conditions in the studied Makassar city area. Landsat TM （thematic mapper） images were used to evaluate the change of land cover during the observation period of 1994-1999. Makassar is fiat, covered mainly by alluvium deposit that is vulnerable to the load of constructions, and volcanic formations which is porous and will easily be degraded by groundwater extraction. It is found that mostly the subsidence has occurred in the western part of the city, including the industrial district, reclamation area, trading center area and the seaport area. The ground survey has indicated that high human activity exists in every point of subsidence. It is likely that various human activities such as ground water pumping and construction work should have affected the local subsidence phenomena in Makassar, as in the case of other large-scale cities in Indonesia.     

ESTIMATION OF JUVENILE SALMON HABITAT IN PACIFIC RIM RIVERS USING MULTISCALAR REMOTE SENSING AND GEOSPATIAL ANALYSIS

We conducted a regional classification and analysis of riverine floodplain physical features that represent key attributes of salmon rearing habitats. Riverine habitat classifications, including floodplain area and river channel complexity, were derived at moderate (30 m) spatial resolution using multispectral Landsat imagery and global terrain data (90 m) encompassing over 3 400 000 km² and most North Pacific Rim (NPR) salmon rivers. Similar classifications were derived using finer scale (i.e. ≤ 2.4‐m resolution) remote sensing data over a smaller set of 31 regionally representative flood plains. A suite of physical habitat metrics (e.g. channel sinuosity, nodes, floodplain width) were derived from each dataset and used to assess the congruence between similar habitat features at the different spatial scales and to evaluate the utility of moderate scale geospatial data for determining abundance of selected juvenile salmon habitats relative to fine scale remote sensing measurements. The resulting habitat metrics corresponded favorably (p < 0.0001) between the moderate scale and the fine scale floodplain classifications; a subset of these metrics (channel nodes and maximum floodplain width) also were strong indicators (R² > 0.5, p < 0.0001) of floodplain habitats defined from the finer scale analysis. These relationships were used to estimate the abundance and distribution of three critical shallow water floodplain habitats for juvenile salmon (parafluvial and orthofluvial springs, and shallow shore) across the entire NPR domain. The resulting database provides a potential tool to evaluate and prioritize salmon conservation efforts both within individual river systems and across major catchments on the basis of physical habitat distribution and abundance. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis

Both moderate and high spatial resolution imagery can be used to quantify abundance and distribution of urban vegetation for urban landscape management and to provide inputs to physical process models. Estimation of vegetation fraction from Landsat ETM+ and Quickbird allows for operational monitoring and reconnaissance at moderate resolution with calibration and vicarious validation at higher resolution. Establishing a linear correspondence between ETM-derived vegetation fraction and Quickbird-derived vegetation fraction facilitates the validation task by extending the spatial scale from 30 × 30 m to a more manageable 2.8 × 2.8 m. A comparative analysis indicates that urban reflectance can be accurately represented with a three component linear mixture model for both Landsat ETM+ and Quickbird imagery in the New York metro area. The strong linearity of the Substrate Vegetation Dark surface (SVD) mixture model provides consistent estimates of illuminated vegetation fraction that can be used to constrain physical process models that require biophysical inputs related to vegetation abundance. When Quickbird-derived 2.8 m estimates of vegetation fraction are integrated to 30 m scales and coregistered to Landsat-derived 30 m estimates, median estimates agree with the integrated fractions to within 5% for fractions > 0.2. The resulting Quickbird-ETM+ scatter distribution cannot be explained with estimate error alone but is consistent with a 3% to 6% estimation error combined with a 17 m subpixel registration ambiguity. The 3D endmember fraction space obtained from ETM+ imagery forms a ternary distribution of reflectance properties corresponding to distinct biophysical surface types. The SVD model is a reflectance analog to Ridd's V–I–S land cover model but acknowledges the fact that permeable and impermeable surfaces cannot generally be distinguished on the basis of broadband reflectance alone. We therefore propose that vegetation fraction be used as a proxy for permeable surface distribution to avoid the common erroneous assumption that all nonvegetated surfaces along the gray axis are completely impermeable. Comparison of mean vegetation fractions to street tree counts in New York City shows a consistent relationship between minimum fraction and tree count. However, moderate and high resolution areal estimates of vegetation fraction provide complementary information because they image all illuminated vegetation, including that not counted by the in situ street tree inventory.     

Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia

In Queensland, Australia, forest areas are discriminated from non-forest by applying a threshold (∼ 12%) to Landsat-derived Foliage Projected Cover (FPC) layers (equating to ∼ 20% canopy cover), which are produced routinely for the State. However, separation of woody regrowth following agricultural clearing cannot be undertaken with confidence, and is therefore not mapped routinely by State Agencies. Using fully polarimetric C-, L- and P-band NASA AIRSAR and Landsat FPC data for forests and agricultural land near Injune, central Queensland, we corroborate that woody regrowth dominated by Brigalow (Acacia harpophylla) cannot be discriminated using either FPC or indeed C-band data alone, because the rapid attainment of a canopy cover leads to similarities in both reflectance and backscatter with remnant forest. We also show that regrowth cannot be discriminated from non-forest areas using either L-band or P-band data alone. However, mapping can be achieved by thresholding and intersecting these layers, as regrowth is unique in supporting both a high FPC (> ∼ 12%) and C-band SAR backscatter (> ~ − 18 dB at HV polarisation) and low L-band and P-band SAR backscatter (e.g. < =∼ 14 dB at L-band HH polarisation). To provide a theoretical explanation, a wave scattering model based on that of Durden et al. [Durden, S.L., Van Zyl, J.J. & Zebker, H.A. (1989). Modelling and observation of radar polarization signature of forested areas. IEEE Trans. Geoscience and Remote Sensing, 27, 290-301.] was used to demonstrate that volume scattering from leaves and small branches in the upper canopy leads to increases in C-band backscattering (particularly HV polarisations) from regrowth, which increases proportionally with FPC. By contrast, low L-band and P-band backscatter occurs because of the lack of double bounce interactions at co-polarisations (particularly HH) and volume scattering at HV polarisation from the stems and branches, respectively, when their dimensions are smaller than the wavelength. Regrowth maps generated by applying simple thresholds to both FPC and AIRSAR L-band data showed a very close correspondence with those mapped using same-date 2.5 m Hymap data and an average 73.7% overlap with those mapped through time-series comparison of Landsat-derived land cover classifications. Regrowth mapped using Landsat-derived FPC from 1995 and JER-1 SAR data from 1994-1995 also corresponded with areas identified within the time-series classification and true colour stereo photographs for the same period. The integration of Landsat FPC and L-band SAR data is therefore expected to facilitate regrowth mapping across Queensland and other regions of Australia, particularly as Japan's Advanced Land Observing System (ALOS) Phase Arrayed L-band SAR (PALSAR), to be launched in 2006, will observe at both L-band HH and HV polarisations.