Search Results (6)

Human appropriation of net primary production (HANPP) is a substantial improvement upon 20th century attempts at developing an ecological footprint indicator because of its measurability in relation to net primary production, its close relationship to other key footprint measures, such as carbon and water, and its spatial specificity. This paper explores HANPP across four geographical scales: through literature review, the planet; through reanalysis of existing data, variations among the world’s countries; and through novel analyses, U.S. counties and the 30 m pixel scale for one U.S. county. Results show that HANPP informs different sustainability narratives at different scales. At the planetary scale, HANPP is a critical planetary limit that improves upon areal land use indicators. At the country macroscale, HANPP indicates the degree to which meeting the needs of the domestic population for provisioning ecosystem services (food, feed, biofiber, biofuel) presses against the domestic ecological endowment of net primary production. At the county mesoscale, HANPP reveals the dependency of metropolitan areas upon regional specialized rural forestry and agroecosystems to which they are teleconnected through trade and transport infrastructures. At the pixel microscale, HANPP provides the basis for deriving spatial patterns of remaining net primary production upon which biodiversity and regulatory and cultural ecosystem services are dependent. HANPP is thus a sustainability indicator that can fulfill similar needs as carbon, water and other footprints. Full article

Since 2010, the Reducing Emissions from Deforestation and Degradation (REDD+) mechanism has been implemented in Mexico’s Yucatan Peninsula, a biodiversity hotspot with persistent deforestation problems. We apply the before-after-control-intervention approach and quasi-experimental methods to evaluate the effectiveness of REDD+ interventions in reducing deforestation at municipal (meso) and community (micro) scales. Difference-in-differences regression and propensity score matching did not show an overall reduction in forest cover loss from REDD+ projects at both scales. However, Synthetic Control Method (SCM) analyses demonstrated mixed REDD+ effectiveness among intervened municipalities and communities. Funding agencies and number of REDD+ projects intervening in a municipality or community did not appear to affect REDD+ outcomes. However, cattle production and commercial agriculture land uses tended to impede REDD+ effectiveness. Cases of communities with important forestry enterprises exemplified reduced forest cover loss but not when cattle production was present. Communities and municipalities with negative REDD+ outcomes were notable along the southern region bordering Guatemala and Belize, a remote forest frontier fraught with illegal activities and socio-environmental conflicts. We hypothesize that strengthening community governance and organizational capacity results in REDD+ effectiveness. The observed successes and problems in intervened communities deserve closer examination for REDD+ future planning and development of strategies on the Yucatan Peninsula. Full article

Biochar can be an effective sorbent material for removal of nutrients from water due to its high specific surface area, porous structure, and high cation and anion exchange capacity. The aim of this study was to test a biochar reactor and to evaluate its efficiency in runoff water purification and consecutive nutrient recycling in clear-cut peatland forests. The goodness of the method was tested in a meso-scale (water volume thousands of liters) reactor experiment by circulating runoff water through wood biochar-filled columns and by determining water nutrient concentrations in the column inlet and outlet. The pseudo-first and second order kinetic models were fitted to the experimental data and the adsorption rate (K_ad) and maximum adsorption capacity (Q_max) of the biochar reactor were quantified. The concentration of total nitrogen (TN) decreased by 58% during the 8-week experiment; the majority of TN adsorption occurred within the first 3 days. In addition, NO₃-N and NH₄-N concentrations decreased below the detection limit in 5 days after the beginning of the experiment. The maximum adsorption capacity of the biochar reactor varied between 0.03–0.04 mg g⁻¹ biochar for NH₄-N, and was equal to 0.02 mg g⁻¹ biochar for TN. The results demonstrated that the biochar reactor was not able to adsorb TN when the water TN concentration was below 0.4 mg L⁻¹. These results suggest that a biochar reactor can be a useful and effective method for runoff water purification in clear-cut forests and further development and testing is warranted. Unlike traditional water protection methods in peatland forestry, the biochar reactor can effectively remove NO₃-N from water. This makes the biochar reactor a promising water protection tool to be tested in sites where there is the risk of a high rate of nutrient export after forest harvesting or drainage. Full article

Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha⁻¹ and ranged from 19.1381–195.66 Mg ha⁻¹. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high $R^2$ and a low root mean square error (RMSE). The $R^2$ values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha⁻¹, The $R^2$ values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha⁻¹. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments. Full article

In the next decades, climate change will put forests in the Hungarian Great Plain in the Carpathian Basin to the test, e.g., changing seasonal patterns, more intense storms, longer dry periods, and pests are expected to occur. To aid in the decision-making process for the conservation of ecosystems depending on forestry, how woods could adapt to changing meso- and microclimatic conditions in the near future needs to be defined. In addition to trendlike warming processes, calculations show an increase in climate extremes, which need to be monitored in accordance with spatial planning, at least for medium-scale mappings. We can use the MODIS sensor dataset if up-to-date terrestrial conditions and multi-decadal geographical processes are of interest. For geographic evaluations of changes, we used vegetation spectral indices; Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), based on the summer half year, 16-day MODIS data composites between 2000 and 2017 in an intensively forested study area in the Hungarian Great Plain. We delineated forest areas on the Danube–Tisza Interfluve using Corine Land Cover maps (2000, 2006, and 2012). Mid-year changes over the nearly two-decade-long period are currently in balance; however, based on their reactions, forests are highly sensitive to abrupt changes caused by extreme climatic events. The higher occurrence of years or periods with extreme water shortages marks an observable decrease in biomass production, even in shorter index time series, such as that between 2004 and 2012. In the drought-stricken July-August periods, the effect of a dry year, subsequent to years with more precipitation, immediately pushes back the green mass and the reduction in the biomass production could become persistent, according to climatology predictions. The changes of specific sub-periods in the vegetation period can be evaluated even in a relatively short, 18-year data series, including the change in the growing values of the vegetative growth in spring or the increase in the summertime biomass production. Standardized differences highlight spatial differences in the biomass production; in response to years with the highest (negative) biomass difference; typically, the northern and southwestern parts of the Danube–Tisza Interfluve in the study area have longer lasting losses in biomass production. A comparison of NDVI and EVI values with the PaDI drought index and the vegetation indices of LANDSAT Operational Land Imager sensor respectively confirms our results. Full article

The ecosite unit in Ontario’s boreal forest ecological land classification system is a polygon of common vegetation type and soil conditions intended to provide a standardized provincial framework to inform meso-scale forestry and planning applications. To determine whether the physical factors used for ecosite classification relate to patterns in ecological function over finer spatial scales, we examined 14 soil properties in replicate boreal forest plots representing eight mineral soil ecosite classes and three organic soil ecosite classes in the Hearst Forest. Despite large differences in vegetation composition, we found few statistically significant differences in properties when compared for individual classes or for more general groupings based on vegetation type and soil texture or expected fertility status. However, some properties (soil organic carbon, total nitrogen, and C:N ratio) were approaching significance in the 0–10 cm depth increment, and there were distinct differences between organic soil and mineral soil sites. Overall, these results suggest few explicit links between ecosystem function and ecosite class at this scale of measurement, highlighting the potential importance of non-steady-state relationships between vegetation species and soil properties in disturbed forests and the potential need for finer-scale characterization to capture patterns in ecosystem function. Full article