Scaling smallholder tree cover restoration across the tropics

Restoring tree cover in tropical countries has the potential to benefit millions of smallholders through improvements in income and environmental services. However, despite their dominant landholding shares in many countries, smallholders’ role in restoration has not been addressed in prior global or pan-tropical restoration studies. We fill this lacuna by using global spatial data on trees and people, national indicators of enabling conditions, and micro-level expert information. We find that by 2050, low-cost restoration is feasible within 280, 200, and 60 million hectares of tropical croplands, pasturelands, and degraded forestlands, respectively. Such restoration could affect 210 million people in croplands, 59 million people in pasturelands and 22 million people in degraded forestlands. This predominance of low-cost restoration opportunity in populated agricultural lands has not been revealed by prior analyses of tree cover restoration potential. In countries with low-cost tropical restoration potential, smallholdings comprise a significant proportion of agricultural lands in Asia (∼76 %) and Africa (∼60 %) but not the Americas (∼3%). Thus, while the Americas account for approximately half of 21st century tropical deforestation, smallholder-based reforestation may play a larger role in efforts to reverse recent forest loss in Asia and Africa than in the Americas. Furthermore, our analyses show that countries with low-cost restoration potential largely lack policy commitments or smallholder supportive institutional and market conditions. Discussions among practitioners and researchers suggest that four principles – partnering with farmers and prioritizing their preferences, reducing uncertainty, strengthening markets, and mobilizing innovative financing – can help scale smallholder-driven restoration in the face of these challenges.     

Search for Solar Periodicities in Tree-ring Widths from Concórdia (S.C., Brazil)

Tree ring studies are usually used to determine or verify climatic factors prevailing at a given place or region that may cause tree-ring width variations. Few studies are dedicated to the solar phenomena which may underlie these tree-ring width variations. Furthermore, it is known that some terrestrial phenomena are influenced by short- and long-time scale solar variability. An optical and computational method was set up and applied to tree samples (Araucaria angustifolia from Santa Catarina State in Southern Brazil) in order to obtain a growth ring width mean chronology. Spectral analysis is used for the search of periodicities, by maximum entropy and iterative regression methods. The results evidenced several embedded signals at periods which may be related to solar activity variations. Cross-correlation analysis between sunspot number and tree-ring data was performed and a lag of zero year was obtained. From our work, it seems that the tropical conifer species Araucaria angustifolia may be a good choice for studies on Sun-Earth relationships and their regional effects.     

A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification

Tree species information is crucial for digital forestry, and efficient techniques for classifying tree species are extensively demanded. To this end, airborne light detection and ranging (LiDAR) has been introduced. However, the literature review suggests that most of the previous airborne LiDAR-based studies were only based on limited kinds of tree signatures. To address this gap, this study proposed developing a novel modular framework for LiDAR-based tree species classification, by deriving feature parameters in a systematic way. Specifically, feature parameters of point-distribution (PD), laser pulse intensity (IN), crown-internal (CI) and tree-external (TE) structures were proposed and derived. With a support-vector-machine (SVM) classifier used, the classifications were conducted in a leave-one-out-for-cross-validation (LOOCV) mode. Based on the samples of four typical boreal tree species, i.e., Picea abies, Pinus sylvestris, Populus tremula and Quercus robur, tests showed that the accuracies of the classifications based on the acquired PD-, IN-, CI- and TE-categorized feature parameters as well as the integration of their individual optimal parameters are 65.00%, 80.00%, 82.50%, 85.00% and 92.50%, respectively. These results indicate that the procedures proposed in this study can be used as a comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification.     

Paleoclimate histories improve access and sustainability in index insurance programs

Proxy-based climate reconstructions can extend instrumental records by hundreds of years, providing a wealth of climate information at high temporal resolution. To date, however, their usefulness for informing climate risk and variability in policy and social applications has been understudied. Here, we apply tree-ring based reconstructions of drought for the last 700 years in a climate index insurance framework to show that additional information from long climate reconstructions significantly improves our understanding of the underlying climate distributions and variability. We further show that this added information can be used to better characterize risk to insurance providers, in many cases providing meaningful reductions in long-term contract costs to farmers in stand-alone policies. The impact of uncertainty on insurance premiums can also be reduced when insurers diversify portfolios, and the availability of long-term climate information from tree rings across a broad geographic range provides an opportunity to characterize spatial correlation in climate risk across geographic regions. Our results are robust to the range of climate variability experienced over the last 400 years and in model simulations of the twenty-first century, even within the context of changing baselines due to low frequency variability and secular climate trends. These results demonstrate the utility of longer-term climate histories in index insurance applications. Furthermore, they make the case from a climate-variability perspective for the continued importance of such approaches to improving the instrumental climate record, even into a non-stationary climate future.     

微波消解-直角加速飞行时间电感耦合等离子体质谱法测定烟叶中砷铅镉汞

对某省5个产地21种烟叶样品用微波密闭消解,电感耦合等离子体直角加速时间飞行质谱(ICP-oaTOF-MS)测定砷、镉、汞、铅的含量。对称样量、微波消解及质谱检测参数、同位素及内标、方法准确性及可行性进行了研究,确定了最佳的实验条件。标准曲线的相关系数>0.9998,方法回收率为91.49%～106.25%,精密度(RSD)<2%,检出限为0.093～0.168μg/L。对在同一品种、同一部位、不同地区的烟叶样品进行测定,结果显示各地区样品中被测元素的含量存在较大差异,As为0.503～1.737 mg/kg,Pb为1.826～4.969 mg/kg,Cd为0.656～2.641 mg/kg,Hg为0.042～0.584 mg/kg;As、Hg含量差异较小,Pb、Cd含量差异较大。相比于其他分析方法,ICP-oaTOF-MS适合对浓度低至ng/L级超痕量组分的分析,具有简便快速、准确、灵敏度高、检出限低等优点,可用于烟叶重金属的检测及质量控制,对于判断烟株的丰缺情况、鉴别烟叶制品的品质具有一定的意义。     

Sensitivity to climate change for two reptiles at the Mojave-Sonoran Desert interface

The high temperatures and extended droughts that characterize habitat for desert-living reptiles may already approach their physiological tolerances and so could put them at risk due to climate change. Here I examined climate change sensitivity for desert tortoises, Gopherus agassizii, and common chuckwallas, Sauromalus ater, two large-bodied reptiles that occur across the Mojave-Sonoran Desert interface. I employed the Mahalanobis D² statistic to model their niche spaces and then assessed climate-change sensitivity by altering climate variables along a gradient of increasing temperature and decreasing precipitation. While shifting climate variables, I held terrain and soils variables that otherwise define these species’ preferred habitat constant, providing a more realistic prediction of available niche space. Both reptiles’ modeled niches responded to climate change by shifting to higher elevations and increasingly away from their Sonoran Desert distribution. At moderate predictions of climate change (+2 °C, −50 mm precipitation) desert tortoises’ suitable habitat was reduced by nearly 88% in the Sonoran Desert portions my study area, and nearly 66% in the Mojave Desert regions. Under the same scenario chuckwallas lost nearly 92%, but increased 120% respectively. Within the context of climate change potential increases in drought frequency appear to present the greater challenge for these species.     

Exploring full-waveform LiDAR parameters for tree species classification

Precise tree species classification with high density full-waveform LiDAR data is a key research topic for automated forest inventory. Most approaches constrain to geometric features and only a few consider intensity values. Since full-waveform data offers a much larger amount of deducible information this study explores a high number of parameter and feature combinations. Those variables having the highest impact on species differentiation are determined. To handle the large amount of airborne full-waveform data and to extract a comprehensive number of variable combinations an improved algorithm was developed. The full-waveform point parameters amplitude, width, range corrected intensity and total number of targets within a beam are transferred into raster covering a test site of 10 km². It was possible to isolate the three most important variables based on the intensity, the width and the total number of targets. Up to six tree species were classified with an overall accuracy of 57%, limiting to the four main species accuracy was improved to 78% and constraining just to conifers and broadleaved trees even 91% could be classified correctly.     

P2P空间数据索引方法研究综述

本文针对基于对等网络的空间数据索引方法展开综述,将当前的P2P空间数据索引方法分为4大类:基于分布式哈希表、树结构、Delaunay三角网结构和基于距离的P2P空间索引方法。分别对每类的研究内容和相关技术进行详细论述,然后对各类查询方法的效率、维护代价和负载均衡情况等相关指标进行对比,最后进行了总结和展望。     

Tree-ring dates on two pre-Little Ice Age advances in Glacier Bay National Park and Preserve, Alaska, USA

Two interstadial tree ring-width chronologies from Geikie Inlet, Glacier Bay Southeast, Alaska were built from 40 logs. One of these chronologies has been calendar dated to AD 224–999 (775 yr) crossdating with a living ring-width chronology from Prince William Sound, Alaska. Trees in this chronology were likely killed through inundation by sediments and meltwater from the advancing Geikie Glacier and its tributaries ca. AD 850. The earlier tree-ring chronology spans 545 yr and is a floating ring-width series tied to radiocarbon ages of about 3000 cal yr BP. This tree-ring work indicates two intervals of glacial expansion by the Geikie Glacier system toward the main trunk glacier in Glacier Bay between 3400 and 3000 cal yr BP and again about AD 850. The timing of both expansions is consistent with patterns of ice advance at tidewater glaciers in other parts of Alaska and British Columbia about the same time, and with a relative sea-level history from just outside Glacier Bay in Icy Strait. This emerging tree-ring dated history builds on previous radiocarbon-based glacial histories and is the first study to use tree-ring dating to assign calendar dates to glacial activity for Glacier Bay.     

Using multi-date high spectral resolution data to assess the physiological status of macroscopically undamaged foliage on a regional scale

Forests play an important role in regulation of the global climate; moreover, they provide human beings with a whole range of ecosystem services. Forest health and ecosystem functioning have been influenced by anthropogenic activities and their consequences, such as air pollution, surface mining, heavy metal contamination, and other biotic and abiotic stress factors, which had an especially serious effect on central Europe. Many aspects of the physiological state of trees are more or less related to the concentrations of two main groups of leaf photosynthetic pigments: chlorophylls and carotenoids. Therefore, their contents can be used as non-specific indicators of the actual tree physiological status, stress and the pre-visible tree damage. Variations in leaf biochemical composition affect foliar optical properties and can be assessed remotely using high spectral resolution data (hyperspectral data). These data were successfully used in earlier studies to detect vegetation stress and damage. However, only a few approaches have dealt with the use of hyperspectral remote sensing to assess vegetation physiological status on a regional scale. Moreover, little or no research has been done on assessing vegetation health while utilizing multi-date hyperspectral images.In this study, the method for assessing forest health conditions using optical indices retrieved from hyperspectral data was applied to the two temporal HyMap date sets acquired in 07/2009 and 08/2010 to detect stress for the Norway spruce forests in Sokolov, NW Bohemia, a region affected by long-term extensive mining. The classification results were validated by ground truth data (total chlorophyll – Cab, carotenoids – Car and carotenoid to chlorophyll ratio – Car/Cab) and were associated with the geochemical conditions of the forest stands. Both biochemical analysis of the sampled foliage and classification of 2009 and 2010 hyperspectral image identified the same sites affected by vegetation stress. In addition to higher Car/Cab, which enabled detection of the stressed trees using hyperspectral image data, these sites showed critically low pH and lower values for the macronutrient parameters in both organic horizons and, in addition, both sites exhibit critically low base cation to aluminum ratios (Bc/Al) for lower organic and top mineral (0–20 cm) soil horizons.The results of this study demonstrate (i) the potential application of hyperspectral remote sensing as a rapid method of identifying tree stress prior to symptom expression, and (ii) the added value of multi-temporal approaches for hyperspectral data and its further potential for monitoring forest ecosystems.