Forest land in developing countries is increasingly being brought under community management under programs such as Joint Forest Management, Community-based Forest Management, Collaborative Management, etc., more generally called Community Forest Management (CFM). This movement has been stimulated by the recognition in many countries that Forest Departments (FD), which are nominally responsible for management of state-owned forest, do not have the resources to carry out this task effectively. Rural people, whose livelihoods are supplemented by, or even dependent on, a variety of forest products such as firewood and fodder, foods and medicines, have the potential knowledge and human resources to provide effective management capacity to take care of the forest resources when the FD cannot. These actors are not only forest peoples with indigenous entitlements or customary rights to the forest lands, but countless rural communities adjacent to forest areas with accumulated grounded knowledge of forest specificities.

Carbon assimilation defined as the overall rate of fixation of carbon through the process of photosynthesis is central to the climate change research. The present study compares the two well-known algorithms in satellite based carbon assimilation estimation, the Vegetation Photosynthesis Model (VPM) and the MOD 17A2 GPP Model, over the tropical forest types in India for a period of two years (September, 2006–August, 2008). The results indicate that the evergreen forest assimilate carbon at a higher rate while the rate is lower for montane grasslands. The comparison between the model results shows that there are large differences between these estimates, and that the spatial resolution of the input datasets plays a larger role than the algorithms of the models. The comparison exercise will be helpful for the refinement and development of the existing and future GPP models by incorporating the empirical environmental conditions.

A main limitation of pixel-based vegetation indices or reflectance values for estimating above-ground biomass is that they do not consider the mixed spectral components on the earth's surface covered by a pixel. In this research, we decomposed mixed reflectance in each pixel before developing models to achieve higher accuracy in above-ground biomass estimation. Spectral mixture analysis was applied to decompose the mixed spectral components of Landsat-7 ETM+ imagery into fractional images. Afterwards, regression models were developed by integrating training data and fraction images. The results showed that the spectral mixture analysis improved the accuracy of biomass estimation of Dipterocarp forests. When applied to the independent validation data set, the model based on the vegetation fraction reduced 5-16% the root mean square error compared to the models using a single band 4 or 5, multiple bands 4, 5, 7 and all non-thermal bands of Landsat ETM+.

Estimating the contribution of forests to carbon sequestration is commonly done by applying forest growth models. Such models inherently use field observations, such as leaf area index (LAI), whereas relevant information is also available from remotely sensed images. The purpose of this study is to improve the LAI estimated from the Physiological Principles Predicting Growth (3-PG) model by combining its output with LAI derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. A Bayesian network (BN) approach is proposed to take care of the different structure of the inaccuracies in the two data sources. It addresses the bias in the 3-PG model and the noise of the ASTER images. Moreover, the EM algorithm is introduced into BN to estimate missing the LAI ASTER data, since they are not available for long time series due to the atmospheric conditions. This paper shows that the outputs obtained with the BN were more accurate than the 3-PG estimate, as the root mean square error reduces to 0.46, and the relative error to 5.86 percent. We conclude that the Em-algorithm within a BN can adequately handle missing LAI ASTER values, and BNS can improve the estimation of LAI values. Ultimately, this method may be used as a predicting model of LAI values, and handling the missing data of ASTER images time series.

Measuring forest degradation and related forest carbon stock changes is more challenging than measuring deforestation since degradation implies changes in the structure of the forest and does not entail a change in land use, making it less easily detectable through remote sensing. Although we anticipate the use of the IPCC guidance under the United Framework Convention on Climate Change (UNFCCC), there is no one single method for monitoring forest degradation for the case of REDD+ policy. In this review paper we highlight that the choice depends upon a number of factors including the type of degradation, available historical data, capacities and resources, and the potentials and limitations of various measurement and monitoring approaches. Current degradation rates can be measured through field data (i.e. multi-date national forest inventories and permanent sample plot data, commercial forestry data sets, proxy data from domestic markets) and/or remote sensing data (i.e. direct mapping of canopy and forest structural changes or indirect mapping through modelling approaches), with the combination of techniques providing the best options. Developing countries frequently lack consistent historical field data for assessing past forest degradation, and so must rely more on remote sensing approaches mixed with current field assessments of carbon stock changes. Historical degradation estimates will have larger uncertainties as it will be difficult to determine their accuracy. However improving monitoring capacities for systematic forest degradation estimates today will help reduce uncertainties even for historical estimates.

A new policy, Reduced Emissions from Deforestation and Degradation in Developing Countries, is currently being discussed under the UNFCCC. This policy will necessarily involve multi-level governance and involve international, national and local level bodies in both North and South. The paper considers a number of unresolved issues relating to Reducing Emissions from Deforestation in Developing Countries and how the interests of the different levels in this governance structure will be reflected in them. It demonstrates that on several issues there is more difference in interest between different South Parties than between South and North. It also shows that recognition of traditional and local level forest governance capacities by national Parties may be an essential step not only to control deforestation and degradation, but also to provide the data that the state will need to claim carbon credits.

The primary productivity of a plant community can be modeled as the product of the amount of photosynthetically active radiation (PAR) absorbed by the canopy and a light use efficiency factor, where the amount of absorbed PAR (APAR) is the product of the fractional absorption and the amount of incident PAR. By implementing a method, PARcalc, using atmospheric data from the Moderate Resolution Imaging Spectroradiometer (MODIS), incident PAR is estimated in this study. In addition, since many PAR datasets are generated by converting shortwave radiation into PAR, the ratio of PAR to shortwave radiation was also investigated. PARcalc models the photosynthetic photon flux density (PPFD) as a product of atmospheric transmittance, the cosine of the Sun zenith angle, and the solar constant. The atmospheric transmittance includes the attenuation of radiation by Rayleigh and aerosol scattering, and absorption by water and ozone. A cloud transmittance factor which is primarily a function of the cloud optical thickness is added in order to cope with cloudy conditions. The model was implemented at two sites in Sweden, Asa and Norunda, where in situ measurements of PPFD were made during the spring and summer of 2004. Modeled time-series were evaluated against the measurements, and daily sums of PPFD were calculated by fitting of a sine function in combination with linear interpolation of the instantaneous estimates from sunrise to sunset. This gave correlation coefficients at Norunda and Asa of 0.80 and 0.77, respectively, when comparing modeled and measured daily insolation. The average relative errors were 24% and 25%. Corresponding figures for five day averages were 0.91 and 0.86; and 9.3% and 11.9%. Instantaneous estimates of PPFD were modeled with correlation coefficients of 0 88–0 93 and average relative errors from 17.0%. These numbers were acquired when using measured values for determining cloudiness; the corresponding figures when the method is fully implemented using satellite data are 0.84 to 0.71 and 24.9%, respectively. The ratio of PAR to shortwave radiation was measured at Norunda 1 Jan to 31 Oct 2004 and was found to vary between 0.27 and 0.48 on a daily basis with an average of 0.43 for the whole period.

Instantaneous PAR (Photosynthetically Active Radiation), computed from atmospheric parameters from individual images from the MODIS sensors aboard the Terra and Aqua satellite platforms, is combined to derive daily integrated PAR and mapped to a local coordinate system. Compared to field observations, the daily integrated PAR values were shown to have average errors in the order of 5–8%, with individual estimation errors as high as 21%, but monthly averages showed much better correspondence with observations yielding averaged absolute errors of around 5%. The error appears to be mostly related to uncertainties in the MODIS aerosol retrieval accuracy. This accuracy and the medium spatial resolution of the PAR map compare very favourably to other sources of PAR data and make this a useful product in the improved assessment of vegetation dynamics.

Most estimates of deforestation are derived for areas that are not intrinsically related to the causes of deforestation, making it hard to link such estimates with strategies for improved land management. In this paper a method is presented to identify local areas of high rates of deforestation within larger areas, using remote sensing and geographic information systems (GIS). These deforestation hot spots are identified using a local analysis approach, considering for every location only land cover changes taking place in the immediate vicinity, and they thus reflect local deforestation. This method has potential application in improved land policy development, land management and it could supplement land use change modeling at the landscape level. Specifically, the method could be used to undertake a risk assessment of the effectiveness of ecological corridors. The method was applied to the province of Limón in Costa Rica, where commercial agriculture has caused large scale deforestation. The deforestation hot spots in the period 1986–1997 that were identified with this method represent a large proportion of the total deforestation having taken place in the province, and for each of the hot spots specific drivers of deforestation could be identified.