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Community-based Forestry (CBF) is now a popular approach for landscape restoration, forest management, biodiversity conservation and support for rural livelihoods worldwide. The Himalayan country Nepal has been at the forefront of CBF for over four decades, with almost 40% of the total population directly involved in protecting and managing more than 32% of the country’s forested land. However, in the past, the focus of CBF in Nepal was the provision of goods for local subsistence, and there has been limited analysis of the role of CBF in providing ecosystem services (ES) from restored forest landscapes. Based on material drawn from a literature review and a stakeholders’ workshop, this paper analyses changes in Nepalese forest policies to provide a more holistic framework for CBF that provides a wider range of ES and to potentially underpin payments for ecosystem services in Nepal. The analysis indicates that Nepal’s forest policy and practices are still dominated by a narrowly conceived notion of forest management that does not accommodate the holistic concept of ES. The study illustrates that CBF provides many ES from local to global benefits as result of forest restoration. For example, timber, firewood, food, and water have local importance, while climate regulation, flood/erosion control, and habitat improvement have global importance. Many innovative cases are emerging in the long journey of CBF in Nepal that demonstrate more diverse management strategies, new forms of tenure rights and autonomy in institutional spaces. These can potentially provide a catalytic platform for the wider adoption of the ES framework in CBF regimes, in order to focus and reward forest management more directly for the provision of services such as water, biodiversity, climate regulation and recreation. Consequently, this study discusses the issues and challenges that are impeding the implementation of the ES concept in Nepal and suggests some ways forward. [ABSTRACT FROM AUTHOR]

Abstract: Analysis of uncertainty is often neglected in the evaluation of complex systems models, such as computational models used in hydrology or ecology. Prediction uncertainty arises from a variety of sources, such as input error, calibration accuracy, parameter sensitivity and parameter uncertainty. In this study, various computational approaches were investigated for analysing the impact of parameter uncertainty on predictions of streamflow for a water-balance hydrological model used in eastern Australia. The parameters and associated equations which had greatest impact on model output were determined by combining differential error analysis and Monte Carlo simulation with stochastic and deterministic sensitivity analysis. This integrated approach aids in the identification of insignificant or redundant parameters and provides support for further simplifications in the mathematical structure underlying the model. Parameter uncertainty was represented by a probability distribution and simulation experiments revealed that the shape (skewness) of the distribution had a significant effect on model output uncertainty. More specifically, increasing negative skewness of the parameter distribution correlated with decreasing width of the model output confidence interval (i.e. resulting in less uncertainty). For skewed distributions, characterisation of uncertainty is more accurate using the confidence interval from the cumulative distribution rather than using variance. The analytic approach also identified the key parameters and the non-linear flux equation most influential in affecting model output uncertainty. [Copyright &y& Elsevier]

In light of the growing volumes of remote sensing data generated by multiple space-borne platforms, integrated multi-sensor frameworks will continue to generate a significant interest in the frame of international forest monitoring initiatives. This work investigates the interoperability of synthetic aperture radar (SAR) and optical datasets for the purpose of large-scale and operational forest monitoring. Using a discriminant technique known as canonical variate analysis, we investigate the level of discrimination (between forest and non-forest training sites) achieved by different datasets, thereby providing an assessment of complementarity between Landsat data and SAR data acquired at C-band (RADARSAT-2) and L-band (ALOS PALSAR), as well as related texture measures. Spatio-temporal methods developed as part of Australia's Land Cover Change Program (an established forest mapping and carbon accounting scheme operating at continental scale) are subsequently used for the integration of Landsat and (segmented) PALSAR data. To highlight specific operational aspects of the multi-sensor framework, this approach is demonstrated over the Australian state of Tasmania (approximately 6.8 million ha), one of several national demonstrator sites defined by the Forest Carbon Tracking task of the Group on Earth Observations (GEO-FCT). In terms of complementarity, the combination of Landsat and L-band SAR data is found to provide most of the forest discrimination, while texture information and single-date C-band SAR data are found to provide only limited additional discrimination improvement in the frame of the considered monitoring system. The interoperability of optical and SAR data is assessed by comparison of forest maps resulting from the spatio-temporal processing under different scenarios, including: i) Landsat-only time series, ii) PALSAR-only time series, and iii) mixed Landsat–PALSAR time series. A comparison of the single-date optical and SAR-based forest classifications indicates a good agreement over Tasmania, with some bias towards forest in the PALSAR classifications. Significant differences are evident when considering the case of forest conversion (deforestation and afforestation) over large areas, thereby compromising the full interoperability of SAR and optical data within the framework of Australia's carbon accounting system. [ABSTRACT FROM AUTHOR]

Abstract: Using the airborne Polarimetric L-band Imaging Synthetic aperture radar (PLIS) the impact of high revisit cycle and full polarimetric acquisitions on biomass retrieval was investigated by means of backscatter-based multi-temporal methods. Parametric and non-parametric models were used to relate reference biomass levels obtained from field plot measurements and high point density lidar data to backscatter intensities or polarimetric target decomposition components. Single-date retrieval using multiple independent variables provided lower estimation errors when compared to models using one independent variable with errors decreasing by 2% to 15%. The multi-temporal aggregation of daily biomass estimates did not improve the overall retrieval accuracy but provided more reliable estimates with respect to single-date methods. Backscatter intensities improved estimation accuracies up to 10% compared to polarimetric target decomposition components. Using all four polarizations increased the estimation accuracy marginally (2%) when compared to a dual-polarized system. The biomass estimation error was considerably reduced (up to 30%) only by decreasing the spatial resolution and was related to decreasing forest variability with increasing pixel size. These results indicate that, at least in semi-arid areas, future L-band missions would not significantly improve biomass estimation accuracy using backscatter-based modeling approaches despite their better spatial resolution, higher revisit cycles and the availability of fully polarimetric information. [Copyright &y& Elsevier]

This paper addresses the shortfall in L-band SAR data availability for the purpose of extracting spatially explicit information on forest cover, and the capacity to fill the gap using shorter wavelength C-band data. Specifically, comparisons are made of forest/non-forest (F/NF) extent derived from independent classification of ALOS PALSAR and RADARSAT-2 data acquired over Tasmania. Focussing on a temperate forest landscape, it was demonstrated that C-band SAR data can be used interchangeably with L-band data to produce a comparable estimate of forest/non-forest cover. Only partial interoperability is achieved however, given the limitations of dual polarisation C-band SAR in discerning forests of different growth stage and biomass. Ambiguities in F/NF status were more prevalent in the C-band classification, despite inclusion of topographic (surface elevation and slope) and textural features in the training dataset, with the key observations as follows: (i) Reduced dynamic range and greater overlap amongst F/NF classes; (ii) Similarities in volume scattering from harvested/regrowth eucalyptus areas and background native forest; (iii) Confusion between young pine plantation and harvested/regrowth areas due to comparable roughness; (iv) Less variation in backscatter and poorer separation of intact and managed eucalyptus forest; and (v) Reduced capacity for discrimination of forest types. In almost every case, the use of L-band data was preferable. The one exception was the limited separation of young pine plantation and harvested/regrowth areas in both C- and L-band data. A similar level of performance was achieved in the discrimination of mature pine plantation, and between young eucalyptus plantation and harvested/regrowth. The findings were restricted to single-date classification of C- and L- band data. The potential to extend a time-series of L-band observations over forest using dense time-series (i.e., intra-annual) C-band observations acquired in dual or quad polarisation mode, warrants further investigation. Where a positive trade-off exists between the benefits and costs of integrating these data, multi-frequency (e.g., C- and L-band) and multi-sensor approaches (e.g., SAR and optical) are a viable way forward for operational forest monitoring and carbon accounting. [ABSTRACT FROM AUTHOR]

Abstract: Soil Surface Roughness (SR) provides a representation of surface variability which can be an important factor in a range of modelling applications such as surface water flow and sediment/nutrient transport. Moreover, it is a crucial parameter for interpreting backscatter characteristics of Synthetic Aperture Radar (SAR) for agricultural application such as near-surface soil moisture retrieval. SR is typically estimated using manual profiles of height variation along short transects (1 to 3m). However, this approach can be very time consuming and often only a small number of transects can be measured in this way, which may not be adequate to characterize the spatial variability of SR across the landscape. This study investigated the feasibility of utilising airborne Light Detection and Ranging (LiDAR) observations as an alternative for mapping SR attributes across an agricultural environment in New South Wales, Australia. To that end, SR attributes were extracted from airborne LiDAR observations and compared with those extracted from an extensive ground survey of SR making use of manual pin-profilers. Results show that LiDAR-estimates of soil profile surface heights Root Mean Square (RMS) are both accurate (compared to manual profiles) and precise (repeatable stable estimates) for fields presenting bare or fallow conditions and either presenting no row structure or as long as the orientation of the LiDAR scan line is perpendicular to the row structure. In such cases results indicated a strong correlation between LiDAR-estimated and ground-measured RMS estimates (R2 >0.68, p<0.05), with an RMSE better than 0.81cm and bias smaller than 0.48cm from a 400m flight altitude. Moreover, estimates produced from repeat pass LiDAR datasets were consistent and highly correlated (R2 0.98) suggesting that the approach is precise and robust, provided that key tillage parameters (i.e. presence of vegetative material and row direction) can be pre-classified. LiDAR estimates of surface height RMS were shown to be accurate enough to allow the tracking of temporal changes in surface roughness due to farming activities. In contrast, LiDAR-derived surface Correlation Length (CL) estimates were not found to be a reliable proxy of the ground-measured CL. [Copyright &y& Elsevier]