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Maps of burned area derived from Land Remote-Sensing Satellite (LANDSAT) system imagery may be more reliable than fire perimeter records based on ground observation or manual cartographic delineati...

Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (SIFT) and Random Sample Consensus (RANSAC) Alignment (SARA). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows.

Mapping plant growth forms is useful to classify and monitor chaparral shrubland community types in southern California. This study evaluates the utility of plant height information, derived from high spatial resolution aerial images and structure-from-motion photogrammetry, for the purpose of distinguishing tree, shrub and sub-shrub growth forms from herbs and bare ground. Canopy height models (CHMs) were derived for two chaparral sites on Santa Rosa Plateau which contain intermixed growth forms. A multi-criterion, knowledge-based thresholding approach was used to classify growth forms based on spectral data (Normalized Difference Vegetation Index, hue, intensity, and focal texture) alone, CHM data alone, and hybrid combinations of spectral and CHM data. Overall accuracies were 66.0–69.0% from spectral data, 72.0–75.5% from CHM data, and 80.5–82.5% from the hybrid data sets. This study highlights the utility in combining multi-spectral and canopy height data for characterizing Mediterranean-type plant communities and changes therein.

From a conservation perspective, quantifying potential refugial capacity has been predominantly focused on climate refugia, which is critical for maintaining the persistence of species and ecosystems. However, protection from other stressors, such as human-induced changes in fire and hydrology, that cause habitat loss, degradation, and fragmentation is also necessary to ensure that conservation efforts focused on climate are not undermined by other threats. Thus, conceptual and methodological advances for quantifying potential refugia from multiple anthropogenic stressors are important to support conservation efforts. We devised a new conceptual approach, the domains of refugia, for assessing refugial capacity that identifies areas where exposure to multiple stressors is low. In our framework, patterns of environmental variability (e.g., increased frequency of warm summers), thresholds of resilience, and extent and intensity of stressors are used to identify areas of potential refugia from a suite of ongoing anthropogenic stressors (e.g., changes in fire regime). To demonstrate its utility, we applied the framework to a Southern California landscape. Sites with high refugial capacity (super-refugia sites) had on average 30% fewer extremely warm summers, 20% fewer fire events, 10% less exposure to altered river channels and riparian areas, and 50% fewer recreational trails than the surrounding landscape. Our results suggest that super-refugia sites (∼8200 kmMarco Conceptual a para Identificar Refugios de Múltiples Amenazas a Escala de Paisaje Resumen Desde la perspectiva de la conservación, la cuantificación de la capacidad potencial de refugio se ha enfocado principalmente en los refugios climáticos, los cuales son críticos para mantener la persistencia de las especies y los ecosistemas. Sin embargo, la protección ante otros factores estresantes, como los cambios inducidos por los humanos en los incendios y la hidrología, que causan la pérdida, degradación y fragmentación del hábitat, también son necesarios para asegurar que los esfuerzos de conservación enfocados en el clima no sean afectados por otras amenazas. Por lo tanto, los avances conceptuales y metodológicos para cuantificar los refugios potenciales ante múltiples factores estresantes causados por el humano son importantes para asegurar que los esfuerzos de conservación logren sus objetivos. Diseñamos una nueva estrategia conceptual, los dominios de los refugios, para evaluar la capacidad de refugio de un paisaje donde la exposición a múltiples factores estresantes es baja. En nuestro marco conceptual usamos los patrones de variabilidad ambiental (p. ej.: incremento en la frecuencia de veranos cálidos), los umbrales de resiliencia y la extensión e intensidad de los factores estresantes para identificar las áreas de refugios potenciales a partir de un conjunto de factores antropogénicos persistentes (p. ej.: cambios en el régimen de incendios). Para demostrar su utilidad, aplicamos el marco conceptual a un paisaje del sur de California. Los sitios con una alta capacidad de refugio (sitios de súper-refugios) tuvieron en promedio un 30% menos veranos extremadamente cálidos, 20% menos eventos de incendios y 50% menos senderos recreativos que el paisaje circundante. Nuestros resultados sugieren que los sitios de súper-refugios (∼ 8,200 km从保护的角度来看, 目前对潜在避难能力的量化主要关注的是气候避难所, 它们对于维持物种和生态系统续存至关重要。然而, 为了确保关注气候的保护工作不受其它威胁因素破坏, 还需要考虑针对其它压力因素的保护, 包括人类引起的火灾和水文变化, 因为它们会导致栖息地丧失、退化和破碎化。因此, 在概念和方法上推动量化多种人为压力因素的潜在避难所的研究进展, 对支持保护工作十分重要。本研究设计了一个新的概念性方法 (即避难所领域) 用于评估避难能力, 以识别受多种压力因素影响较小的地区。我们的框架利用环境变异性格局 (如暖夏出现频率增加) 、恢复力阈值, 以及压力因素的范围和强度来识别一系列持续的人为压力因素 (如火灾变化) 影响下的潜在避难所。为了证明该框架的有效性, 我们将其应用于南加州的一处景观之中。结果表明, 与周围景观相比, 具有高避难能力的位点 (超级避难所) 极端暖夏出现的概率平均低 30%, 火灾出现概率平均低 20%, 河道和河岸地区被改变的风险低 10%, 且存在的休闲步道少 50%。我们的结果指出, 一些自然群落的超级避难所 (约 8,200 平方公里) 在现有的保护区网络中的代表性不足, 这一发现可以指导扩大保护区的工作。我们的案例研究还强调了考虑多种压力因素的暴露情况将如何为规划和实践提供信息, 从而在不断变化的世界中保护生物多样性。 【翻译: 胡怡思; 审校: 聂永刚】.

An important property of wildfire behaviour is rate of spread (ROS). The objectives of this study are to evaluate the uncertainty of landscape-scale ROS estimates derived from repetitive airborne thermal infrared (ATIR) georeferenced imagery and the utility of such estimates for understanding fire behaviour and controls on spread rates. Time-sequential ATIR image data were collected for the Cedar, Detwiler, and Rey Fires, which burned in California during summers of 2016 and 2017. We analyse error, uncertainty, and precision of ROS estimates associated with co-location accuracy, delineation of active fire front positions, and generation of fire spread vectors. The major sources of uncertainty influencing accuracy of ROS estimates are co-registration accuracy of sequential image pairs and procedures for delineating active fire front locations and spread vectors between them; none of these were found to be substantial. Median ROS estimates are 11 m min−1 for the Cedar Fire and 8 m min−1 for the Detwiler Fire, both of which burned through mixed shrub and tree areas of the Sierra Nevada foothills and were estimated for downslope spread events. Of the three study fires, the fastest spread rates (average spread of 25 m min−1 with maximum of 39 m min−1) are estimated for the Rey Fire, which burned on variable directional slopes through chaparral shrubland vegetation.

Satellite based flood detection can enhance understanding of risk to humans and infrastructures, geomorphic processes, and ecological effects. Such application of optical satellite imagery has been mostly limited to the detection of water exposed to sky, as plant canopies tend to obstruct water visibility in short electromagnetic wavelengths. This case study evaluates the utility in multi-temporal thermal infrared observations from Landsat 8 as a basis for detecting sub-canopy fluvial inundation resulting in ambient temperature change.We selected three flood events of 2016 and 2019 along sections of the Mississippi, Cedar, and Wapsipinicon Rivers located in Iowa, Minnesota, and Wisconsin, United States. Classification of sub-canopy water involved logical, threshold-exceedance criteria to capture thermal decline within channel-adjacent vegetated zones. Open water extent in the floods was mapped based on short-wave infrared thresholds determined parametrically from baseline (non-flooded) observations. Map accuracy was evaluated using higher-resolution (0.5–5.0 m) synchronic optical imagery.Results demonstrate improved ability to detect sub-canopy inundation when thermal infrared change is incorporated: sub-canopy flood class accuracy was comparable to that of open water in previous studies. The multi-temporal open-water mapping technique yielded high accuracy as compared to similar studies. This research highlights the utility of Landsat thermal infrared data for monitoring riparian inundation and for validating other remotely sensed and simulated flood maps.

Chaparral shrubs in southern California may be vulnerable to frequent fire and severe drought. Drought may diminish postfire recovery or worsen impact of short-interval fires. Field-based studies have not shown the extent and magnitude of drought effects on recovery, which may vary among chaparral types and climatic zones. We tracked regional patterns of shrub cover based on June-solstice Landsat Normalized Difference Vegetation Index series, compared between the periods 1984–1989 and 2014–2018. High spatial resolution ortho-imagery was used to map shrub cover in distributed sample plots, to empirically constrain the Landsat-based estimates of mature-stage lateral canopy recovery. We evaluated precipitation, climatic water deficit (CWD), and Palmer Drought Severity Index in summer and wet seasons preceding and following fire, as regional predictors of recovery in 982 locations between the Pacific Coast and inland deserts. Wet-season CWD was the strongest drought-metric predictor of recovery, contributing 34–43 % of explanatory power in multivariate regressions (R(2) =0.16–0.42). Limited recovery linked to drought was most prevalent in transmontane chamise chaparral; impacts were minor in montane areas, and in mixed and montane chaparral types. Elevation was correlated negatively to recovery of transmontane chamise; this may imply acute drought sensitivity in resprouts which predominate seedlings at higher elevations. Landsat Visible Atmospherically Resistant Index (sensitive to live-fuel moisture) was evaluated as a landscape-scale predictor of recovery and explained the greatest amount of variance in a multivariate regression (R(2) = 0.53). We find that drought severity was more closely related to recovery differences among twice-burned sites than was fire-return interval. Summarily, drought has a major role in long-term shrub cover reduction within xeric chaparral ecotones bounding the Mojave Desert and Colorado Desert, likely in tandem with other global change stressors.

Temporal trajectories of apparent vegetation abundance based on the multi-decadal Landsat image series provide valuable information on the postfire recovery of chaparral shrublands, which tend to mature within one decade. Signals of change in fractional shrub cover (FSC) extracted from time-sequential Normalized Difference Vegetation Index (NDVI) data can be systematically biased due to spatial variation in shrub type, soil substrate, or illumination differences associated with topography. We evaluate the effects of these variables in Landsat-derived metrics of FSC and postfire recovery, based upon three chaparral sites in southern California which contain shrub community ecotones, complex terrain, and soil variations. Detailed validations of prefire and postfire FSC are based on high spatial resolution ortho-imagery; cross-stratified random sampling is used for variable control. We find that differences in the composition and structure of shrubs (inferred from ortho-imagery) can substantially influence FSC-NDVI relations and impact recovery metrics. Differences in soil type have a moderate effect on the FSC-NDVI relation in one of the study sites, while no substantial effects were observed due to variation of terrain illumination among the study sites. Arithmetic difference recovery metrics – based on NDVI values that were not normalized with unburned control plots – correlate in a moderate but significant manner with a change in FSC (R(2) values range 0.47–0.59 at two sites). Similar regression coefficients resulted from using Landsat visible reflectance data alone. The lowest correlations to FSC resulted from Soil-Adjusted Vegetation Index (SAVI) and are attributed to the effects of the soil-adjustment factor in sparsely vegetated areas. The Normalized Burn Ratio and Normalized Burn Ratio 2 showed a moderate correlation to FSC. This study confirms the utility of Landsat NDVI data for postfire recovery evaluation and implies a need for stratified analysis of postfire recovery in some chaparral landscapes.

Cartographic visualization of past changes in ownership and management of real estate provides a basis for historical research, decision-making, and political discourse. Surveys associated ...

This study addresses the problem of shadows in multi-temporal imagery, which is a key issue with change detection approaches based on image comparison. We apply image-to-image radiometric normaliza...

Land managers and ecologists working in Mediterranean-type ecosystems require information on postfire recovery of shrublands as a means of identifying long-term changes in these sensitive systems. This study evaluates the utility of postfire regrowth trajectories, derived from multi-temporal Landsat satellite surface reflectance imagery, as a basis for estimating postfire recovery of chamise chaparral in southern California. Postfire recovery metrics are applied to time series trajectories of spectral vegetation indices (SVIs) including NDVI, EVI, SAVI, MSAVI, NBR, and NBR2. Shrub fractional cover changes following postfire recovery are estimated at 74 plots using multi-date high spatial resolution orthoimagery, enabling stratification of the recovery outcomes. The sensitivity of Landsat-based regrowth trajectories to postfire shrub recovery is assessed by ANOVA statistical testing based on the stratified recovery outcomes. We evaluate several combinations of postfire recovery metrics and SVIs as postfire recovery indicators. We compare SVI trends extending 19 to 29 years postfire with field-collected data on postfire recovery trends reported in previous studies, in order to assess the sensitivity of the SVIs to short- and long-term phases of regrowth. The utility and necessity of normalizing SVI time series based on unburned control plots to reduce moisture effects is also evaluated. A primary finding is that the Scaled Recovery Metric (SRM), a variant of the Regeneration Index which incorporates the pixel-specific SVI value immediately after fire, is a particularly useful indicator of postfire recovery based on time-sequential SVI trajectories and facilitates inter-site comparisons. Time series of several SVIs (especially NDVI and NBR2) provide statistically significant (p

The causes and consequences of severe fires must be understood to strategically manage forest resources, including timber and fuelwood. Floods and resulting infrastructure damages are intensified due to soil erosion and debris exhumed by recently-burned catchments. A mixed Ponderosa and Pinon-Juniper forest managed by a rural New Mexico community burned in 2016, destroying harvestable fuelwood and damaging access roads. The objectives of this study were to: (1) estimate lost fuelwood value; (2) identify burned catchments that contributed to road damages; (3) develop criteria to prioritize a restoration area; and 4) recommend possible forest management actions based on assessment of fire regime, natural resource usage, ecological dynamics, and feasibility. Field reconnaissance facilitated image-based mapping of forest structure, burn severity, and early-successional recovery by 2018. High spatial resolution aerial images collected before fire were used to map tree density, as well as height based on shadow lengths and sun angle, and to infer stem volumes from empirical diameter-height relationships. Results suggest that 1.5–2.5 million USD of fuelwood was lost from a 1053-ha area. A remediation priority area (112 ha) was identified within a severely-burned, erosive catchment with limited successional plant cover. Econometrics and landscape-scale optimizations for restoration are proposed. Potential fire management strategies are outlined in order to maximize ecosystem products and services while enhancing safety. Techniques developed in this study – involving open-source data and minimal field work – may be useful to conifer forest managers with limited scientific funding.