Ecosystem services of boreal forests - Carbon budget mapping at high resolution

Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data

BACKGROUND

Under the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is "invisible" to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data.

RESULTS

ALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area (50 km²) which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on (1) approximating the initial soil carbon stocks using simulations; (2) predicting the annual litter input based on the predicted growing stocks in each cell; (3) predicting the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated total carbon change (standard errors in parenthesis) for the entire area was 0.741 (0.14) Mg ha^-1 yr^-1. The biomass carbon change was 0.405 (0.13) Mg ha^-1 yr^-1, the litter carbon change (e.g., deadwood and leaves) was 0.346 (0.027) Mg ha^-1 yr^-1, and the change in SO carbon was - 0.01 (0.003) Mg ha^-1 yr^-1.

CONCLUSIONS

Our results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, the stand-level uncertainty can be estimated under a model-based inferential approach.

Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data

Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled the fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-sensor data will have a specific strength in the further classification of nemoral forest landscapes owing to the distinct seasonal patterns in the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and a random forest classifier trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90%, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34–74%. Species groups with coniferous species were the least confused, whereas the broadleaf groups, especially Quercus species, had higher error rates. The results are applied in Danish national accounting of greenhouse gas emissions from forests, resource assessment, and assessment of forest biodiversity potentials.

Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of vegetation dynamics were analyzed at the pixel scale in the ARB for the period of 1982–2013 using remotely sensed data of long-term leaf area index (LAI), fractional vegetation cover (FVC), and terrestrial gross primary productivity (GPP). The spatial autocorrelation characteristics of the vegetation indexes were further explored with global and local Moran’s I techniques. The spatial–temporal relationships between vegetation and climatic factors, land use/cover types and hydrological variables in the ARB were determined using a geographical and temporal weighted regression (GTWR) model based on the observed meteorological data, remotely sensed vegetation information, while the simulated hydrological variables were determined with the soil and water assessment tool (SWAT) model. The results suggest that the variation in area-average annual FVC was significant with an increase rate of 0.0004/year, and LAI, FVC, and GPP all exhibited strong spatial heterogeneity trends in the ARB. For LAI and FVC, the most significant changes in local spatial autocorrelation were recognized over the Sanjiang Plain, and the low–low agglomeration in the Sanjiang Plain decreased continuously. The GTWR model results indicate that natural and anthropogenic factors jointly took effect and interacted with each other to affect the vegetated regime of the region. The decrease in the impact of precipitation to vegetation growth over the Songnen Plain was determined as having started around 1991, which was most likely attributed to dramatic changes in water use styles induced by local land use changes, and corresponded to the negative correlation between pasture areas and vegetation indexes during the same period. The analysis results presented in this paper can provide vital information to decision-makers for use in managing vegetation resources.

Carbon dynamics in three subtropical forest ecosystems in China

The carbon sequestration capacity of the forest ecosystem normally increases overage due to the carbon dynamic in below canopy and soil. The carbon dynamic is reflective of the forest characteristics and their interactions with climate, topographic, and soil conditions. In this study, we measured the carbon content and carbon density of canopy, shrub, understory vegetation, litter, and soil, and assessed carbon dynamics in three forest ecosystems (Cunninghamia lanceolate, Pinus massoniana, and Evergreen broad-leaved forests) with a combination of data from Fujian Provincial forest resource inventory. This study showed that the carbon content of the canopy layers increased over time, and the carbon content of the topsoil (0-30 cm) in the young forests was significantly higher than that in other age groups in Cunninghamia lanceolata forest and Pinus massoniana forest. Due to the carbon differences in the soil layer, the carbon stocks of the C. lanceolata forest and the P. massoniana forest declined from 1996 to 2007, but the carbon stocks of Evergreen broad-leaved forest increased. Besides, using the traditional carbon content coefficient (0.5) might underestimate the carbon sequestration potential of these forest ecosystems, especially for the mature forests. The coniferous forests displayed a short-term reduction in the carbon stocks of ecosystems between 10 and 20 years after afforestation, and the decline cannot be ignored in the carbon budget.

Local Users and Other Stakeholders’ Perceptions of the Identification and Prioritization of Ecosystem Services in Fragile Mountains: A Case Study of Chure Region of Nepal

Forest-based ecosystem services (ES) play a vital role in improving people’s livelihoods, the environment, and the economy. Prior studies have focused on technical aspects of economic valuation such as biophysical quantification through modeling and mapping, or monetary valuation, while little attention has been paid to the social dimensions. Taking case studies of two dominant community-based forest management systems (community forestry—CF and collaborative forestry—CFM) in the Chure region of Nepal, we investigate how local users and other stakeholders perceive the valuation of forest-based ecosystem services based on proximity (nearby vs. distant users), socio-economic class (rich vs. poor users), and forest management modalities (CF vs. CFM). We found that local users and other stakeholders in the Chure region identified a total of 42 forest-based ecosystem services: 16 provisioning, 15 regulating, and 11 cultural services. While all local users prioritised firewood, water quality improvement, and bequest values as the top three services, genetic resources, hazard protection, and hunting services were valued as having the lowest priority. The priorities placed on other services varied in many respects. For instance, rich users living near a CF showed a strong preference for fodder, grasses, and soil conservation services whereas users living far from forests prioritised timber, fresh water, and flood control services. In the case of CFM, rich users adjacent to forests preferred timber, soil conservation, and carbon sequestration services but those living far from forests chose timber, poles, and flood control as their top priorities. Differences in rankings also occurred among the regional managers, national experts, and forest users. The reasons for these differences and their policy implications are discussed, and ways of reaching consensus between the users are suggested.