CZCORS在像控测量中的应用

文章通过与现有方法的分析比较,阐述了利用CZCORS技术不仅可以满足像控点测量的精度要求,而且与常规方法具有较大的优越性,展示了CZCORS在航测中的广泛应用前景。     

The relations between modulus of elasticity and temperature in the context of the experimental simulation of rock weathering by fire

Fires occur frequently in many biomes and generate high temperatures on the ground surface. There are many field examples of fire causing rock disintegration. The simulation of fire in the laboratory (using a furnace) and the monitoring of changes in rock modulus of elasticity (with a Grindosonic apparatus), reveal that different rocks respond differently to heating. Significant decreases in elasticity occur at temperatures as low as 200°C and granites display particularly marked reductions. Extended periods of heating are not required for significant reductions to occur. It is postulated that the degree of change in elasticity as a result of simulated fire is such that rock outcrops subjected to real fires are likely to be sufficiently modified as to increase their susceptibility to erosion and weathering processes.     

卡尔曼滤波器在卫星遥感影像大地校正中的应用

在卫星遥感图像的预处理中,通过系统校正可以基本上消除影像内部的变形误差。然而,整幅影像的大地定位误差仍然是很大的,甚至几百米至1公里的数量级。本文介绍利用少量的地面控制点,采用递归算法而不是批处理算法,大大地提高大地定位精度。在单幅影像处理中,可以重新对卫星的轨道参数与姿态参数进行估值,从而提高影像的大地定位精度。在一条轨道上连续的多幅影像中,可以预报下一幅影像的大地位置,从而对那些不具有地面控制点的影像的大地校正中,实现外推计算。文中介绍并推导了卡尔曼滤波器方程中的转移矩阵和测量矩阵的系数,并推荐用数值回归的办法求得其数值解。文中最后介绍模拟试验及算法计算结果,并讨论其优缺点。     

Detecting forest damage after a low-severity fire using remote sensing at multiple scales

Remote sensing technologies are an ideal platform to examine the extent and impact of fire on the landscape. In this study we assess that capacity of the RapidEye constellation and Landsat (Thematic Mapper and Operational Land Imager to map fine-scale burn attributes for a small, low severity prescribed fire in a dry Western Canadian forest. Estimates of burn severity from field data were collated into a simple burn index and correlated with a selected suite of common spectral vegetation indices. Burn severity classes were then derived to map fire impacts and estimate consumed woody surface fuels (diameter ≥2.6 cm). All correlations between the simple burn index and vegetation indices produced significant results (p < 0.01), but varied substantially in their overall accuracy. Although the Landsat Soil Adjusted Vegetation Index provided the best regression fit (R² = 0.56), results suggested that RapidEye provided much more spatially detailed estimates of tree damage (Soil Adjusted Vegetation Index, R² = 0.51). Consumption estimates of woody surface fuels ranged from 3.38 ± 1.03 Mg ha⁻¹ to 11.73 ± 1.84 Mg ha⁻¹, across four derived severity classes with uncertainties likely a result of changing foliage moisture between the before and after fire images. While not containing spectral information in the short wave infrared, the spatial variability provided by the RapidEye imagery has potential for mapping and monitoring fine scale forest attributes, as well as the potential to resolve fire damage at the individual tree level.     

Data,interface, security: Assembling technologies that govern the future

Over the last decade, fire governance practices in the British Fire and Rescue Service (FRS) have undergone fundamental transformation. Rather than just being responded to as and when they occur, the FRS have adopted a range of anticipatory governing strategies to govern fires in anticipation of their occurence. This turn towards anticipatory governance has been facilitated in no small part by the digital infrastructure now embedded in the FRS. Composed of data, hardware, software, fibre-optic cables along with human analysts and organisational processes, this infrastructure operates to make risk projections on fire which shape and condition strategic decision making. This paper explores the operation of this digital infrastructure through the notion of interface. Drawing on empirical material relating to processes of data sourcing and risk calculation, interfaces account for the sites, moments and experiences in which human and non-human agents relate to one another in making fire risk projections. Showing relations to exist spatially, temporally and sensually, I argue that interfaces are crucial to the operation of an anticipatory security apparatus which relies on digital devices.     

Interannual climate variation,land type and village livelihood effects on fires in Kalimantan,Indonesia

The increasing extent and frequency of fires globally requires nuanced understanding of the drivers of large-scale events for improved prevention and mitigation. Yet, the drivers of fires are often poorly understood by various stakeholders in spatially expansive and temporally dynamic landscapes. Further, perceptions about the main cause of fires vary amongst stakeholders, which amplify ongoing challenges from policies being implemented inconsistently across different governance levels. Here, we develop a spatially and temporally-explicit typology of fire prevalence across Kalimantan, Indonesia, a region with significant contribution to global greenhouse gas emissions. Based on livelihood information and data on climate, soil type and forest degradation status, we find that in intact forest the density of fires in villages that largely coincide with oil palm concessions was twice as high as in villages outside the concessions across all years. Fires occurring in degraded land on mineral soil across all years were also most prevalent in villages with industrial plantations (oil palm or timber). On the other hand, in degraded peatland, where fires are most intense during dry years induced by the El Niño episodes, occurrence rates were high regardless of village primary livelihoods. Based on these findings we recommend two key priorities for fire mitigation going forward for policy across different governance levels in Kalimantan: degraded peatland as the priority area and industrial plantations as the priority sector. Our study suggests a fire prevention and mitigation approach, which accounts for climate, land type and village livelihood, has the potential to deliver more effective means of management.     

Fire activity in Borneo driven by industrial land conversion and drought during El Niño periods, 1982–2010

Tropical rainforests, naturally resistant to fire when intact, are increasingly vulnerable to burning due to ongoing forest perturbation and, possibly, climatic changes. Industrial-scale forest degradation and conversion are increasing fire occurrence, and interactions with climate anomalies such as El Niño induced droughts can magnify the extent and severity of fire activity. The influences of these factors on fire frequency in tropical forests has not been widely studied at large spatio-temporal scales at which feedbacks between fire reoccurrence and forest degradation may develop. Linkages between fire activity, industrial land use, and El Niño rainfall deficits are acute in Borneo, where the greatest tropical fire events in recorded history have apparently occurred in recent decades. Here we investigate how fire frequency in Borneo has been influenced by industrial-scale agricultural development and logging during El Niño periods by integrating long-term satellite observations between 1982 and 2010 – a period encompassing the onset, development, and consolidation of its Borneo’s industrial forestry and agricultural operations as well as the full diversity of El Niño events. We record changes in fire frequency over this period by deriving the longest and most comprehensive spatio-temporal record of fire activity across Borneo using AVHRR Global Area Coverage (GAC) satellite data. Monthly fire frequency was derived from these data and modelled at 0.04° resolution via a random-forest model, which explained 56% of the monthly variation as a function of oil palm and timber plantation extent and proximity, logging intensity and proximity, human settlement, climate, forest and peatland condition, and time, observed using Landsat and similar satellite data. Oil-palm extent increased fire frequency until covering 20% of a grid cell, signalling the significant influence of early stages of plantation establishment. Heighted fire frequency was particularly acute within 10 km of oil palm, where both expanding plantation and smallholder agriculture are believed to be contributing factors. Fire frequency increased abruptly and dramatically when rainfall fell below 200 mm month⁻¹, especially as landscape perturbation increased (indicated by vegetation index data). Logging intensity had a negligible influence on fire frequency, including on peatlands, suggesting a more complex response of logged forest to burning than appreciated. Over time, the epicentres of high-frequency fires expanded from East Kalimantan (1980’s) to Central and West Kalimantan (1990’s), coincidentally but apparently slightly preceding oil-palm expansion, and high-frequency fires then waned in East Kalimantan and occurred only in Central and West Kalimantan (2000’s). After accounting for land-cover changes and climate, our model under-estimates observed fire frequency during ca. 1990–2002 and over-estimates it thereafter, suggesting that a multi-decadal shift to industrial forest conversion and forest landscapes may have diminished the propensity for high-frequency fires in much of this globally significant tropical region since ca. 2000.     

一种工业热源提取方法:利用温度特征模板的BP神经网络

针对年尺度热异常数据提取工业热源的方法存在数量和空间精细化程度不足的问题,使用VIIRS Active Fire数据,提出了一种基于温度特征模板的BP神经网络工业热源提取方法。该方法以京津冀及周边地区为试验区,首先,根据工业热源空间聚集性特征,使用OPTICS算法划分热源对象;其次,根据热源的热辐射特征,构建工业热源与非工业热源温度特征模板;最后,以温度特征模板、热源统计特征等作为参数,使用BP神经网络提取工业热源对象。结果表明：① 本文提出的基于温度特征模板的BP神经网络算法的工业热源提取精度达到了96.31%,与时间滤波、逻辑回归方法相比较,工业热源提取精度分别提高了8.45%、7.53%;② 2015—2020年京津冀及周边地区6省市工业热源数量整体减少了27.46%;河北省工业热源对象数量和热异常点数量年均减少了8.06%和7.44%,相对于其他省市减少幅度最大;山东、天津的工业热源集中度分别提高了25.72%、86.64%,说明两地工业转型升级政策取得较显著成效;③ 唐山、邯郸、吕梁和长治4个城市工业热源对象数量占试验区全部的31.37%,为京津冀及周边地区工业热源主要分布城市;临汾、太原等7个城市工业热源聚集程度和能源消耗程度高于其他城市;北京、周口等11个城市工业热源聚集程度和能源消耗程度低于其他城市;④ 2020年1—5月,京津冀及周边地区工业热异常点数量相对于2019、2021年同期保持不变或增加,新冠疫情对试验区工业热源无显著影响;2020年1、2月武汉工业热异常点数量与2019、2021年同期相比数量减少了66.67%以上,2020年3—5月工业热异常点数量低于2019年同期,2020年1—5月新冠疫情对武汉市工业热源影响显著。该研究反映了京津冀及周边地区工业热源发展的现状及趋势,能够为降低能耗和提高第二产业集中度等相关政策的制定与调整提供有价值的参考。     

Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

The influence of illumination effects on the optimality of the dNBR (differenced Normalized Burn Ratio) was evaluated for the case of the 2007 Peloponnese (Greece) wildfires using a pre/post-fire Landsat TM (Thematic Mapper) image couple. Well-illuminated pixels (south and south-east facing slopes) exhibited more optimal displacements in the bi-spectral feature space than more shaded pixels (north and north-west exposed slopes). Moreover, pixels experiencing a small image-to-image difference in illumination obtained a higher optimality than pixels with a relatively large difference in illumination. To correct for illumination effects, the c-correction method and a modified c-correction technique were applied. The resulting median dNBR optimality of uncorrected, c-corrected and modified c-correction data was respectively 0.58, 0.60 and 0.71 (differences significant for p < 0.001). The original c-correction method improved the optimality of badly illuminated pixels while deteriorating the optimality of well-illuminated pixels. In contrast, the modified c-correction technique improved the optimality of all the pixels while retaining the prime characteristic of topographic correction techniques, i.e. detrending the illumination–reflectance relationship. For a minority of the data, for shaded pixels and/or pixels with a high image-to-image difference in illumination, the original c-correction outperformed the modified c-correction technique. In this study conducted in rugged terrain and with a bi-temporal image acquisition scheme that deviated up to two months from the ideal anniversary date scheme the modified c-correction technique resulted in a more reliable change detection.     

Normalized algorithm for mapping and dating forest disturbances and regrowth for the United States

Forest disturbances such as harvesting, wildfire and insect infestation are critical ecosystem processes affecting the carbon cycle. Because carbon dynamics are related to time since disturbance, forest stand age that can be used as a surrogate for major clear-cut/fire disturbance information has recently been recognized as an important input to forest carbon cycle models for improving prediction accuracy. In this study, forest disturbances in the USA for the period of ∼1990–2000 were mapped using 400+ pairs of re-sampled Landsat TM/ETM scenes in 500m resolution, which were provided by the Landsat Ecosystem Disturbance Adaptive Processing System project. The detected disturbances were then separated into two five-year age groups, facilitated by Forest Inventory and Analysis (FIA) data, which was used to calculate the area of forest regeneration for each county in the USA.