MODIS-based vegetation index has sufficient sensitivity to indicate stand-level intra-seasonal climatic stress in oak and beech forests

Context: Variation in photosynthetic activity of trees induced by climatic stress can be effectively evaluated using remote sensing data. Although adverse effects of climate on temperate forests have been subjected to increased scrutiny, the suitability of remote sensing imagery for identification of drought stress in such forests has not been explored fully. Aim: To evaluate the sensitivity of MODIS-based vegetation index to heat and drought stress in temperate forests, and explore the differences in stress response of oaks and beech. Methods: We identified 8 oak and 13 beech pure and mature stands, each covering between 4 and 13 MODIS pixels. For each pixel, we extracted a time series of MODIS NDVI from 2000 to 2010. We identified all sequences of continuous unseasonal NDVI decline to be used as the response variable indicative of environmental stress. Neural Networks-based regression modelling was then applied to identify the climatic variables that best explain observed NDVI declines. Results: Tested variables explained 84–97% of the variation in NDVI, whilst air temperature-related climate extremes were found to be the most influential. Beech showed a linear response to the most influential climatic predictors, while oak responded in a unimodal pattern suggesting a better coping mechanism. Conclusions: MODIS NDVI has proved sufficiently sensitive as a stand-level indicator of climatic stress acting upon temperate broadleaf forests, leading to its potential use in predicting drought stress from meteorological observations and improving parameterisation of forest stress indices.

Palaeovegetation in China during the late Quaternary: Biome reconstructions based on a global scheme of plant functional types

Two previous reconstructions of palaeovegetation across the whole of China were performed using a simple classification of plant functional types (PFTs). Now a more explicit, global PFT classification scheme has been developed, and a substantial number of additional pollen records have become available. Here we apply the global scheme of PFTs to a comprehensive set of pollen records available from China to test the applicability of the global scheme of PFTs in China, and to obtain a well-founded reconstruction of changing palaeovegetation patterns. A total of 806 pollen surface samples, 188 mid-Holocene (MH, 6000 14C yr BP) and 50 last glacial maximum (LGM, 18,000 14C yr BP) pollen records were used to reconstruct vegetation patterns in China, based on a new global classification system of PFTs and a standard numerical technique for biome assignment (biomization). The biome reconstruction based on pollen surface samples showed convincing agreement with present potential natural vegetation. Coherent patterns of change in biome distribution between MH, LGM and present are observed. In the MH, cold and cool-temperate evergreen needleleaf forests and mixed forests, temperate deciduous broadleaf forest, and warm-temperate evergreen broadleaf and mixed forest in eastern China were shifted northward by 200–500 km. Cold-deciduous forest in northeastern China was replaced by cold evergreen needleleaf forest while in central northern China, cold-deciduous forest was present at some sites now occupied by temperate grassland and desert. The forest–grassland boundary was 200–300 km west of its present position. Temperate xerophytic shrubland, temperate grassland and desert covered a large area on the Tibetan Plateau, but the area of tundra was reduced. Treeline was 300–500 m higher than present in Tibet. These changes imply generally warmer winters, longer growing seasons and more precipitation during the MH. Westward shifts of the forest–shrubland–grassland and grassland–desert boundaries imply greater moisture availability in the MH, consistent with a stronger summer monsoon. During the LGM, in contrast, cold-deciduous forest, cool-temperate evergreen needleleaf forest, cool mixed forests, warm-temperate evergreen broadleaf and mixed forest in eastern China were displaced to the south by 300–1000 km, while temperate deciduous broadleaf forest, pure warm-temperate evergreen forest, tropical semi-evergreen and evergreen broadleaf forests were restricted or absent from the mainland of southern China, implying colder winters than present. Strong shifts of temperate xerophytic shrubland, temperate grassland and desert to the south and east in northern and western China and on the Tibetan Plateau imply drier conditions than present.

Impact of biomass burning on aerosol properties over tropical wet evergreen forests of Arunachal Pradesh, India

Trees outside forests and rural livelihoods: a study of Chitwan District, Nepal

Trees outside forests (TOF) in Nepal’s Terai have significantly increased over the past decade. The Chitwan District was one of the focus districts in the Terai Community Forestry Development Project that promoted a tree seedling distribution program. This paper examines the current position of tree integration on farmland and its contribution to livelihoods of rural households in this district. Interviews with local key informants, government and non-government agencies and woodbased industries, as well as an in-depth study of 32 households were used to describe the constraints faced by the households in management of trees on farmland. Most households cited disease, poor growth, lack of preferred tree species, lack of technical support, an uncertain tree market, and lack of financial support as constraints. Despite the important role of trees in subsistence and marketbased rural livelihood diversification, and the consequent reduction in pressure on national forests from on-farm trees, current government policies and practices fail to recognise the value of these trees. It is argued that there is substantial potential for improving on-farm trees to enhance rural livelihoods. A responsive service mechanism centred on tree growing households would help the management of tree resources on the farmland.

Challenges in elevated CO2 experiments on forests

Current forest Free Air CO2 Enrichment (FACE) experiments are reaching completion. Therefore, it is time to define the scientific goals and priorities of future experimental facilities. In this opinion article, we discuss the following three overarching issues (i) What are the most urgent scientific questions and how can they be addressed? (ii) What forest ecosystems should be investigated? (iii) Which other climate change factors should be coupled with elevated CO2 concentrations in future experiments to better predict the effects of climate change? Plantations and natural forests can have conflicting purposes for high productivity and environmental protection. However, in both cases the assessment of carbon balance and how this will be affected by elevated CO2 concentrations and the interacting climate change factors is the most pressing priority for future experiments.

The trees and the bees: using enforcement and income projects to protect forests and rural livelihoods through spatial joint production

Forest managers in developing countries enforce extraction restrictions to limit forest degradation. In response, villagers may displace some of their extraction to other forests, which generates “leakage” of degradation. Managers also implement poverty alleviation projects to compensate for lost resource access or to induce conservation. We develop a model of spatial joint production of bees and fuelwood that is based on forest-compatible projects such as beekeeping in Thailand, Tanzania, and Mexico. We demonstrate that managers can better determine the amount and pattern of degradation by choosing the location of both enforcement and the forest-based activity.

Protecting developing countries' forests: enforcement in theory and practice

This paper relates the key findings of the optimal economic enforcement literature to practical issues of enforcing forest and wildlife management access restrictions in developing countries. Our experiences, particularly from Tanzania and eastern India, provide detail of the key pragmatic issues facing those responsible for protecting natural resources. We identify large gaps in the theoretical literature that limit its ability to inform practical management, including issues of limited funding and cost recovery, multiple tiers of enforcement and the incentives facing enforcement officers, and conflict between protected area managers and rural people's needs.

Fine-root turnover rates of European forests revisited: an analysis of data from sequential coring and ingrowth cores

Background and Aims Forest trees directly contribute to carbon cycling in forest soils through the turnover of their fine roots. In this study we aimed to calculate root turnover rates of common European forest tree species and to compare them with most frequently published values. Methods We compiled available European data and applied various turnover rate calculation methods to the resulting database. We used Decision Matrix and Maximum-Minimum formula as suggested in the literature. Results Mean turnover rates obtained by the combination of sequential coring and Decision Matrix were 0.86 yr−1 for Fagus sylvatica and 0.88 yr−1 for Picea abies when maximum biomass data were used for the calculation, and 1.11 yr−1 for both species when mean biomass data were used. Using mean biomass rather than maximum resulted in about 30 % higher values of root turnover. Using the Decision Matrix to calculate turnover rate doubled the rates when compared to the Maximum-Minimum formula. The Decision Matrix, however, makes use of more input information than the Maximum-Minimum formula. Conclusions We propose that calculations using the Decision Matrix with mean biomass give the most reliable estimates of root turnover rates in European forests and should preferentially be used in models and C reporting.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.

REDD+ and community-controlled forests in low-income countries: any hope for a linkage?

Deforestation and forest degradation are estimated to account for between 12% and 20% of annual greenhouse gas emissions and in the 1990s (largely in the developing world) released about 5.8 Gt per year, which was bigger than all forms of transport combined. The idea behind REDD + is that payments for sequestering carbon can tip the economic balance away from loss of forests and in the process yield climate benefits. Recent analysis has suggested that developing country carbon sequestration can effectively compete with other climate investments as part of a cost effective climate policy. This paper focuses on opportunities and complications associated with bringing community-controlled forests into REDD +. About 25% of developing country forests are community controlled and therefore it is difficult to envision a successful REDD + without coming to terms with community controlled forests. It is widely agreed that REDD + offers opportunities to bring value to developing country forests, but there are also concerns driven by worries related to insecure and poorly defined community forest tenure, informed by often long histories of government unwillingness to meaningfully devolve to communities. Further, communities are complicated systems and it is therefore also of concern that REDD + could destabilize existing well-functioning community forestry systems.

Persistent reduced ecosystem respiration after insect disturbance in high elevation forests

Amid a worldwide increase in tree mortality, mountain pine beetles (Dendroctonus ponderosae Hopkins) have led to the death of billions of trees from Mexico to Alaska since 2000. This is predicted to have important carbon, water and energy balance feedbacks on the Earth system. Counter to current projections, we show that on a decadal scale, tree mortality causes no increase in ecosystem respiration from scales of several square metres up to an 84 km2 valley. Rather, we found comparable declines in both gross primary productivity and respiration suggesting little change in net flux, with a transitory recovery of respiration 6–7 years after mortality associated with increased incorporation of leaf litter C into soil organic matter, followed by further decline in years 8–10. The mechanism of the impact of tree mortality caused by these biotic disturbances is consistent with reduced input rather than increased output of carbon.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.