Field Inventory Data Research Articles

Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study

The Sudanian Savanna (SS) of West Africa is characterized by tropical savannas and woodlands. Accurate estimation of AGB and carbon stocks in this biome is important for addressing sustainable development goals as the information can aid natural resource management at varied spatial scales. Previous AGB mapping efforts focused on humid forests, with little attention on savannas. This study explored the use of annual monthly time-series of Senitinel-1 (S-1) and Sentinel-2 (S-2) data to map AGB in the SS. Backscatter, spectral reflectance, and derivatives (vegetation indices and biophysical parameters) were combined with field inventory data in a Random Forest regression to map AGB. Eight experiments were conducted with different data configurations to determine: (1) the potential of S-1 and S-2 for AGB mapping, (2) optimal image acquisition period for AGB mapping, and (3) contribution of image derivatives to improving the accuracy of AGB mapping. The predicted map was validated with 40% of the inventory data. Uncertainty in the AGB was assessed using mean absolute error, root mean squared error, coefficient of determination and symmetrical mean absolute percentage error. Results show that about 90% of the study area have low AGB stocks of less than 90 Mg/ha. Compared to S-1 (RMSE: 78.6; MAE: 25.6), S-2 achieved better prediction accuracy (RMSE: 60.6; MAE: 19.2), although combination of the two according to seasonality produced the best results (RMSE: 45.4; MAE: 16.3). Images acquired in the dry season were found to be more useful for predicting AGB than those of rainy season. Also, stress-related vegetation indices and a red-edge dependent normalized difference vegetation index not tested in previous AGB studies using Sentinels were found to be significant contributors to the superior performance of S-2. Since biomass is a finite resource, our results can provide valuable information on the sustainable use of biomass and energy security including studies on carbon cycling and ecosystem functions in the region. The demonstrated possibility of using open access earth observation data to map and monitor AGB in data scarce regions is useful and beneficial to attaining SDG indicators 15.2.1 (sustainable forest management) and 15.3.1 (proportion of land that is degraded over total land area). Further work on developing species-specific wood densities and allometric equations is required to improve AGB and carbon stock estimation in the SS.

Author Correction: Carbon stocks in central African forests enhanced by elephant disturbance

Large herbivores, such as elephants, can have important effects on ecosystems and biogeochemical cycles. Yet, the influence of elephants on the structure, productivity and carbon stocks in Africa’s rainforests remain largely unknown. Here, we quantify those effects by incorporating elephant disturbance in the Ecosystem Demography model, and verify the modelled effects by comparing them with forest inventory data from two lowland primary forests in Africa. We find that the reduction of forest stem density due to the presence of elephants leads to changes in the competition for light, water and space among trees. These changes favour the emergence of fewer and larger trees with higher wood density. Such a shift in African’s rainforest structure and species composition increases the long-term equilibrium of aboveground biomass. The shift also reduces the forest net primary productivity, given the trade-off between productivity and wood density. At a typical density of 0.5 to 1 animals per km2, elephant disturbances increase aboveground biomass by 26–60 t ha−1. Conversely, the extinction of forest elephants would result in a 7% decrease in the aboveground biomass in central African rainforests. These modelled results are confirmed by field inventory data. We speculate that the presence of forest elephants may have shaped the structure of Africa’s rainforests, which probably plays an important role in differentiating them from Amazonian rainforests. Elephant disturbance favours the emergence of larger trees with higher wood density, and thereby increases the aboveground biomass in central African forests by up to 60 t ha–1, according to simulations with the Ecosystem Demography model.

Toward Efficient Land Cover Mapping: An Overview of the National Land Representation System and Land Cover Map 2015 of Bangladesh

In response to prevailing classification inconsistency between land cover maps, developed by different organizations in different times at different scales, an object-based National Land Representation System (NLRS) for Bangladesh has been developed. The process, which began in 2013 and was completed in 2016, brought together several national organizations and involved an extensive process of consultation, data collection, translation, and analysis of existing land cover/use classification systems. The process focused on the interpretation of three legends from historic national land cover/use maps. Field inventory data were collected from over 1000 sites across the country to assist the process of land characterization and the development of a dynamic and representative overview of land cover and land use in Bangladesh. The system has been applied to the development of a wall-to-wall national land cover map for the year 2015. In this article, the methodological process and results of NLRS formulation and land cover map 2015 are presented. We also provide examples of how this interoperable system and the land cover dataset are being used for variety of applications including national forest resources assessment, estimation of REDD+ activity data, integration of biophysical and socioeconomic information, and semantic similarity assessment.

Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data

An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R 2). A forest AGB map of the study area was generated using the optimal model.

Forest biomass estimation using remote sensing and field inventory: a case study of Tripura, India.

Forests are the potential source for managing carbon sequestration, regulating climate variations and balancinguniversal carbon equilibrium between sources and sinks. Further, assessment of biomass, carbon stock, and its spatial distribution is prerequisite for monitoring the health of forest ecosystem. Moreover, vegetation field inventories are valuable source of data for estimating aboveground biomass (AGB), density, and the carbon stored in biomass of forest vegetation. In view of the importance of biomass, the present study makes an attempt to estimate temporal AGB of Tripura State, India, using Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), leaf area index (LAI) and the field inventory data through geospatial techniques. A model was developed for establishing the relationship between biomass, LAI, and NDVI in the selected study site. The study also aimed to improve method for quantifying and verifying inventory-based biomass stock estimation. The results demonstrate the correlation value obtained betweenLAI and NDVI were0.87 and 0.53 for the years 2011 and 2014, respectively. The correlation value between estimated AGB with LAI werefound as 0.66 and 0.69, while with NDVI, the values were obtained as0.64 and 0.94 for the years 2011 and 2014, respectively. The regression model of measured biomass with MODIS NDVI and LAI was developed for the data obtained during the period 2011-2014. The developed model was used to estimate the spatial distribution of biomass and its relationship between LAI and NDVI. The R2 values obtained were 0.832 for estimated and the measured AGB duringthe training and 0.826 for the validation. The results indicate that the methodology adopted in this study can help in selecting best fit model for analyzing relationship between AGB and NDVI/LAI and for estimating biomass using allometric equation at various spatialscales. The developed output thematic map showed an average biomass distribution of 32-94 Mg ha-1. The highest biomass values (72-95Mg ha-1) was confined to the dense region of the forest while the lowest biomass values (32-46 Mg ha-1) wasidentified in the outer regions of the study site.

Allometric equations to estimate the above-ground biomass of trees in the tropical secondary forests of different ages

Abstract. Karyati, Ipor IB, Jusoh I, Wasli ME. 2019. Allometric equations to estimate the above-ground biomass of trees in the tropical secondary forests of different ages. Biodiversitas 20: 2427-2436. The allometric equations for trees of secondary forests of different ages in abandoned lands after shifting cultivation are still rarely available. The objective of this study was to develop allometric equations to estimate the above-ground biomass (AGB) of trees (DBH of > 5 cm) in the tropical secondary forest of different ages, namely 5, 10, and 20 years after abandonment. The selected trees in this study represented the dominant and rare species and DBH classes in each study site. The trunk dry biomass and AGB showed strong correlations (adjusted R2= 0.59-0.95) with diameter at breast height (DBH) and height. The leaf and branch dry biomass had weak correlations with height (adjusted R2=0.36-0.50). The developed allometric equations were suitable for trees of secondary forests of different ages, because the selected samples used in the destructive method were based on a field inventory data of forest structure and floristic composition.

Carbon stocks in central African forests enhanced by elephant disturbance

Large herbivores, such as elephants, can have important effects on ecosystems and biogeochemical cycles. Yet, the influence of elephants on the structure, productivity and carbon stocks in Africa’s rainforests remain largely unknown. Here, we quantify those effects by incorporating elephant disturbance in the Ecosystem Demography model, and verify the modelled effects by comparing them with forest inventory data from two lowland primary forests in Africa. We find that the reduction of forest stem density due to the presence of elephants leads to changes in the competition for light, water and space among trees. These changes favour the emergence of fewer and larger trees with higher wood density. Such a shift in African’s rainforest structure and species composition increases the long-term equilibrium of aboveground biomass. The shift also reduces the forest net primary productivity, given the trade-off between productivity and wood density. At a typical density of 0.5 to 1 animals per km2, elephant disturbances increase aboveground biomass by 26–60 t ha−1. Conversely, the extinction of forest elephants would result in a 7% decrease in the aboveground biomass in central African rainforests. These modelled results are confirmed by field inventory data. We speculate that the presence of forest elephants may have shaped the structure of Africa’s rainforests, which probably plays an important role in differentiating them from Amazonian rainforests. Elephant disturbance favours the emergence of larger trees with higher wood density, and thereby increases the aboveground biomass in central African forests by up to 60 t ha–1, according to simulations with the Ecosystem Demography model.

Variables Selection for Aboveground Biomass Estimations Using Satellite Data: A Comparison between Relative Importance Approach and Stepwise Akaike’s Information Criterion

It is still a major challenge to select appropriate variables from remote sensing sensors, which implicates finding reliable selection methods that can maximize the performance of chosen variables in regression models. In this study, we compare the performance of stepwise variable selection based on Akaike information criterion and an approach that integrates relative importance techniques and the decomposition criteria of R 2 using two different remote sensing data: SPOT-5 and RapidEye images, with the purpose of selecting suitable variables in multiple linear regression models to estimate aboveground biomass. The obtained accuracy of the regression models was evaluated by triple cross-validation. We carried out this study in a mixed pine–oak forest of central Mexico where intensive wood extraction occurs and therefore different levels of degradation are found. We estimated aboveground biomass from field inventory data at the plot level (n = 52) and used well-established allometric equations. The results showed that a better fit was obtained with the explanatory variables selected from the RapidEye image ( R 2 = 0.437 with stepwise variable selection based on the Akaike information criterion approach and R 2 = 0.420 with relative importance techniques) and the approach that integrates the relative importance can generate better regression models to estimate forest biomass with a reduced number of variables and less error in the estimates.

A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests

Tropical forest canopies are comprised of tree crowns of multiple species varying in shape and height, and ground inventories do not usually reliably describe their structure. Airborne laser scanning data can be used to characterize these individual crowns, but analytical tools developed for boreal or temperate forests may require to be adjusted before they can be applied to tropical environments. Therefore, we compared results from six different segmentation methods applied to six plots (39 ha) from a study site in French Guiana. We measured the overlap of automatically segmented crowns projection with selected crowns manually delineated on high-resolution photography. We also evaluated the goodness of fit following automatic matching with field inventory data using a model linking tree diameter to tree crown width. The different methods tested in this benchmark segmented highly different numbers of crowns having different characteristics. Segmentation methods based on the point cloud (AMS3D and Graph-Cut) globally outperformed methods based on the Canopy Height Models, especially for small crowns; the AMS3D method outperformed the other methods tested for the overlap analysis, and AMS3D and Graph-Cut performed the best for the automatic matching validation. Nevertheless, other methods based on the Canopy Height Model performed better for very large emergent crowns. The dense foliage of tropical moist forests prevents sufficient point densities in the understory to segment subcanopy trees accurately, regardless of the segmentation method.

Monitoring the structure of forest restoration plantations with a drone-lidar system

We are in an unprecedented moment for promoting forest restoration globally, with international and regional pledges to restore at least 350 million hectares by 2030. To achieve these ambitious goals, it is necessary to go beyond traditional plot-scale assessments and develop cost-effective technologies that can monitor the structure and function of restored forests at much broader scales. Lidar remote sensing in unmanned aerial vehicle (UAV) platforms can be an agile and autonomous method for monitoring forest restoration projects, especially under conditions when information updates are frequently needed in relatively small areas or, when using an airplane-borne lidar system may be not financially viable. Here, we explored the potential of an UAV-borne lidar system to assess the outcomes of a mixed-species restoration plantation experiment, designed to maximize aboveground biomass (AGB) accumulation. The experiment was established in Brazil’s Atlantic Forest, with 20 native tree species, by combining two levels of planting density and two management levels, totaling four treatment combinations and one control (plots left over for natural regeneration). We analyzed three structural variables from lidar data (canopy height, gap fraction and leaf area index) and one from field inventory data (AGB). Structural differences between the treatments and the control plots were reliably distinguished by the UAV-borne lidar system. AGB was strongly correlated with canopy height, allowing us to elaborate a predictive equation to use the UAV-borne lidar system for monitoring structural features in other restoration plantations in the region. UAV-borne lidar systems showed enormous potential for monitoring relatively broad-scale (thousands of hectares) forest restoration projects, providing an important tool to aid decision making and accountability in forest landscape restoration.

An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI

Yield forecasting is essential for management of the food and agriculture economic growth of a country. Artificial Neural Network (ANN) based models have been used widely to make precise and realistic forecasts, especially for the nonlinear and complicated problems like crop yield prediction, biomass change detection and crop evapo-transpiration examination. In the present study, various parameters viz. spectral bands of Landsat 8 OLI (Operational Land Imager) satellite data and derived spectral indices along with field inventory data were evaluated for Mentha crop biomass estimation using ANN technique of Multilayer Perceptron. The estimated biomass showed a good relationship (R2 = 0.762 and root mean square error (RMSE) = 2.74 t/ha) with field-measured biomass.

Accounting tropical forest carbon stock with synergistic use of space-borne ALOS PALSAR and COSMO-Skymed SAR sensors

The objective of this study is to precisely quantify the forest aboveground biomass (AGB) and carbon stock in a tropical deciduous forest with synergistic use of space-borne L-band ALOS PALSAR and X-band COSMO-Skymed SAR data, along with field inventory data. AGB serves as a decisive parameter for preparing global decision making policy targeting the impact of reducing emissions from deforestation and forest degradation and climate change. The study proposed optimal regression models for assessing above-ground bole biomass over tropical deciduous mixed forests of Munger (Bihar, India) with synergistic use of ALOS PALSAR and COSMO-Skymed sigma nought images. Coefficient of determination (r2) of 0.89 and RMSE of 15.12 Mg ha−1 were calculated for the best fit integrated model. On validation, the integrated model produced a model accuracy of 78%, r2 = 0.89, RMSE = 16.64 Mg ha−1 and Willmott’s index of agreement of 0.934. Resulting modeled AGB were converted to carbon and carbon dioxide equivalents using conversion factors. L-band showed higher accuracy in the estimation in comparison to X-band; but the estimation accuracy improved with the synergistic use of both X- and L-band SAR data. Hence, the study recommends the combined use of X- and L-band SAR with exceptional capabilities for improved assessment of tropical forest stand AGB and carbon with significant contribution towards operational forestry and policy making.

Quantifying Spatiotemporal Post‐Disturbance Recovery Using Field Inventory, Tree Growth, and Remote Sensing

AbstractForest recovery following a disturbance lasts decades to centuries, and the rate depends on pre‐ and post‐disturbance condition and local environmental factors. Existing approaches of field observations, remote sensing, statistical chronosequence, and ecological modeling have one or more drawbacks, including short time frames, generalized details, indirect indicators, hard parameterization, and defective assumptions. Using aboveground live biomass (AGLB) as an example, we developed an approach called “Disturbance and Recovery Assessment across Space and Time (DRAST).” For a specific post‐disturbance year, DRAST utilizes Field Inventory and Analysis data sets and the Forest Vegetation Simulator, as well as pre‐ and post‐disturbance remote sensing to create two rasters: (1) what the AGLB would look like over the disturbed area had the disturbance not occurred and (2) what the AGLB would look like over the disturbed area in the actual presence of the disturbance. These two rasters are compared annually to examine the spatiotemporal recovery pattern. We demonstrated DRAST with the 2013 Rim fire in California, United States, by creating two sets of AGLB for 100 years. Our results showed that (1) the AGLB consumed by Rim fire was 3.52 Tg and (2) 45.9% of the burned area needs <5 years to recover, followed by 6.4% (5–10 years), 6.1% (>95 years), 5.9% (10–15 years), 5.4% (15–20 years), 4.8% (20–25 years), and 4.3% (25–30 years). In conclusion, DRAST can provide spatially explicit and highly detailed ecological indicators for decades under the two scenarios of “no disturbance” and “actual disturbance occurrence” for recovery analysis.

Growth determinants of timber species Triplochiton scleroxylon and implications for forest management in central Africa

The sustainability of the polycyclic logging system in tropical forests has been increasingly questioned for a variety of reasons, and particularly in central Africa as commercial species, mostly light-demanding long-lived pioneer species, usually fail to recover a stable number of large trees after exploitation. Several factors are known to affect tropical tree demographic processes, like tree growth, survival and recruitment. Tree growth has particularly been showed to depend on ecological conditions, tree genetics, and competition with surrounding vegetation, as well as tree size or ontogeny. Yet, due to the paucity of available data, the importance of such factors is unclear and usually ignored when estimating future timber yields. To fill this gap, we chose to evaluate the variability in growth of one African long-lived pioneer and commercially very important species: Triplochiton scleroxylon K. Schum, gathering a broad dataset composed of tree ring data recorded in one site in Cameroon and periodic field inventory data recorded in seven sites across central Africa. In total, we analyzed 13,225 records of annual tree diameter increments recorded over 920 trees from seven sites in Cameroon, Republic of the Congo and Central African Republic. We evaluated (i) to what extent the average growth of trees that reach harvestable dimensions differs from population average and (ii) to what extent past perturbations influence the growth of remaining trees. We found the diameter growth of T. scleroxylon to be remarkably variable and this study provided an unprecedented quantification of the magnitude of some key growth determinants. In unlogged forests, the diameter increment of T. scleroxylon ranged between 0.40 cm year−1 in Southern Cameroon and 0.83 cm year−1 in South-Eastern Cameroon. The diameter increment was weakly related to tree size but increased twofold from unlogged to logged forests. Perturbation caused by logging stimulates growth of T. scleroxylon for at least 10–15 years. Finally, harvestable timber stock of large-sized T. scleroxylon was found to be constituted by trees that grew in average twice faster than trees of the entire extant population. As more and more inventory data become available, quantifying these effects could be replicated for other timber species and in other sites, to improve the accuracy of future timber resource estimates and improve forest management guidelines.

Spatial distribution of mangrove forest species and biomass assessment using field inventory and earth observation hyperspectral data

The objective of this research is to identify species, provide spatial distribution of the species and estimate the biomass in the mangrove Forest, Bhitarkanika India. Mangrove ecosystems play an important role in regulating carbon cycling, thus having a significant impact on global environmental change. Extensive studies have been conducted for the estimation of mangrove species identification and biomass estimation. However, estimation at a regional level with species-wise biomass distribution has been insufficiently investigated in the past because either research focuses on the species distribution or biomass assessment. Study shows that good relationship has been achieved between stem volume (field measured data) and Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from satellite image and further these two indices are employed to estimate the biomass in the study site. Three models- linear, logarithmic and polynomial (second degree) are used to estimate biomass derived from EVI and NDVI. The hyperspectral data (spatial resolution ~ 30 m) is utilised to identify ten mangrove plant species. We have prepared the spatial distribution map of these species using spectral angle mapper. We have also generated mangrove species-wise biomass distribution map of the study site along with areal coverage of each species. The results indicate that the Sonneratia apetala Buch.-Ham. and Cynometra iripa Kostel has the highest biomass among all ten identified species, 643.12 Mg ha−1 and 652.14 Mg ha−1. Our study provided a positive relationship between NDVI, EVI, and the estimated biomass of Bhitarkanika Forest Reserve Odisha India. The study finds a similar results for both NDVI and EVI derived biomass, while linear regression has more significant results than the polynomial (second degree) and logarithmic regression derived biomass. The polynomial is found slightly better than the logarithmic when using the EVI as compared to NDVI derived biomass. The spatial distribution of species-wise biomass map obtained in this study using both, EVI and NDVI could be used to provide useful information for biodiversity assessment along with the sustainable solutions to different problems associated with the mangrove forest biodiversity. Thus, biomass assessment of larger regions can be achieved by utilization of remote sensing based indices as concluded in the present study.

Forest edge effect on biomass carbon along altitudinal gradients in Chinese Fir (Cunninghamia lanceolata): A study from Southeastern China

Monitoring and mapping forest carbon is critical for informing climate change mitigation measures. Evidence indicates that forest edges hold less carbon than the forest interior. In this study, using field inventory data from a Chinese fir forest ecosystem in southeast China, the carbon at the forest edge and forest interior at different elevations was measured. The results showed a significant increasing trend in carbon with elevation. The results also revealed that forest edges had significantly lower carbon than the forest interior. Carbon density values varied between 265 Mg C ha−1 at forest edges and 320 Mg C ha−1 at forest interior. The study highlights that ignoring edge effects overestimates carbon density by 9.46%. These findings suggest that edge effects should be given proper consideration during national forest carbon inventory. Furthermore, the authors recommend the establishment of permanent sampling plots along forest edges for precise and accurate carbon accounting.

Mapping forest disturbance intensity in North and South Carolina using annual Landsat observations and field inventory data

Disturbance and regrowth are vital processes in determining the roles of forest ecosystem in the carbon and biogeochemical cycles. Using time series observations, the vegetation change tracker (VCT) algorithm was designed to map the location, timing, and spectral magnitudes of forest disturbance events. While these spectral disturbance magnitudes are indicative of physical changes in tree cover or biomass, their quantitative relationships have yet to be established. This study focuses on estimating disturbance intensity as measured by percent basal area removal using spectral indices from the VCT algorithm over North and South Carolina. Repeat measurements on Forest Service Forest Inventory Analysis (FIA) ground plots, which provide changes in basal area between multiple dates at precise locations, are used for training and validation of the model. The overall R2 between predicted disturbance intensity and reference data is 0.66, and cross-validation prediction uncertainty is 14% in North Carolina. Possible causes of this uncertainty could be site heterogeneity and the temporal offset between ground measurements and satellite observations. Results show the area of stand clearing disturbances remains relatively stable around 1143 km2 yr−1 in North and South Carolina throughout the period of observations (1985–2015). The average amount of forest area affected by partial disturbance is much higher at 3287 km2 yr−1. The area of partial disturbances has strong inter-annual variability with a high value of 6000 km2 in 2007 and a low value of 1919 km2 in 2013.

Mitigating the Effects of Climate Change through Harvesting and Planting in Boreal Forests of Northeastern China

The ecological resilience of boreal forests is an important element of measuring forest ecosystem capacity recovered from a disturbance, and is sensitive to broad-scale factors (e.g., climate change, fire disturbance and human related impacts). Therefore, quantifying the effects of these factors is increasingly important for forest ecosystem management. In this study, we investigated the impacts of climate change, climate-induced fire regimes, and forest management schemes on forest ecological resilience using a forest landscape model in the boreal forests of the Great Xing’an Mountains, Northeastern China. First, we simulated the effects of the three studied variables on forest aboveground biomass, growing space occupied, age cohort structure, and the proportion of mid and late-seral species indicators by using the LANDIS PRO model. Second, we calculated ecological resilience based on these four selected indicators. We designed five simulated scenarios: Current fire only scenario, increased fire occurrence only scenario, climate change only scenario, climate-induced fire regime scenario, and climate-fire-management scenario. We analyzed ecological resilience over the five scenarios from 2000 to 2300. The results indicated that the initialized stand density and basal area information from the year 2000 adequately represented the real forest landscape of that year, and no significant difference was found between the simulated landscape of year 2010 and the forest inventory data of that year at the landscape scale. The simulated fire disturbance results were consistent with field inventory data in burned areas. Compared to the current fire regime scenario, forests where fire occurrence increased by 30% had an increase in ecological resilience of 12.4–43.2% at the landscape scale, whereas increasing fire occurrence by 200% would decrease the ecological resilience by 2.5–34.3% in all simulated periods. Under the low climate-induced fire regime scenario, the ecological resilience was 12.3–26.7% higher than that in the reference scenario across all simulated periods. Under the high climate-induced fire regime scenario, the ecological resilience decreased significantly by 30.3% and 53.1% in the short- and medium-terms at landscape scale, while increasing slightly by 3.8% in the long-term period compared to the reference scenario. Compared to no forest management scenario, ecological resilience was decreased by 5.8–32.4% under all harvesting and planting strategies for the low climate-induced fire regime scenario, and only the medium and high planting intensity scenarios visibly increased the ecological resilience (1.7–15.8%) under the high climate-induced fire regime scenario at the landscape scale. Results from our research provided insight into the future forest management and have implications for improving boreal forest sustainability.

Patterns of Biomass and Carbon Allocation across Chronosequence of Chir Pine (Pinus roxburghii) Forest in Pakistan: Inventory-Based Estimate

The quantitative relationship between carbon sequestration potential and stand ages of Pine (Pinus roxburghii) forest is not documented in Pakistan. Using field inventory data, this study underlines the patterns of biomass and carbon allocation across a chronosequence of Chir Pine forest. Based on the uniform shelterwood silvicultural management system, the forest was classified into three stand age classes representing the young stand (<50 years), mature stand (50–75 years), and overmature stand (> 75 years). The results showed an increasing trend in living tree biomass carbon with stand age. However, soil carbon showed gradually decreasing trend from young to overmature stand. Similarly, deadwood, litter, and understory biomass carbon showed an increase pattern of changes. Altogether, the results highlighted that the mean carbon values of all components varied between 90.3 t·C·ha−1 in the young stand and 309.5 t·C·ha−1 in the overmature stand. Furthermore, our results confirm that the current management operations affect the forest floor and soil carbon. Therefore, we suggest that different protection measures should be considered during management operations to enhance soil and forest floor carbon.

Evaluating the Potential of ALS Data to Increase the Efficiency of Aboveground Biomass Estimates in Tropical Peat–Swamp Forests

Estimates of aboveground biomass (AGB) in forests are critically required by many actors including forest managers, forest services and policy makers. Because the AGB of a forest cannot be observed directly, models need to be employed. Allometric models that predict the AGB of a single tree as a function of diameter at breast height (DBH) are commonly used in forest inventories that use a probability selection scheme to estimate total AGB. However, for forest areas with limited accessibility, implementing such a field-based survey can be challenging. In such cases, models that use remotely sensed information may support the biomass assessment if useful predictor variables are available and statistically sound estimators can be derived. Airborne laser scanning (ALS) has become a prominent auxiliary data source for forest biomass assessments and is even considered to be one of the most promising technologies for AGB assessments in forests. In this study, we combined ALS and forest inventory data from a logged-over tropical peat swamp forest in Central Kalimantan, Indonesia to estimate total AGB. Our objective was to compare the precision of AGB estimates from two approaches: (i) from a field-based inventory only and, (ii) from an ALS-assisted approach where ALS and field inventory data were combined. We were particularly interested in analyzing whether the precision of AGB estimates can be improved by integrating ALS data under the particular conditions. For the inventory, we used a standard approach based on a systematic square sample grid. For building a biomass-link model that relates the field based AGB estimates to ALS derived metrics, we used a parametric nonlinear model. From the field-based approach, the estimated mean AGB was 241.38 Mgha − 1 with a standard error of 11.17 Mgha − 1 (SE% = 4.63%). Using the ALS-assisted approach, we estimated a similar mean AGB of 245.08 Mgha − 1 with a slightly smaller standard error of 10.57 Mgha − 1 (SE% = 4.30%). Altogether, this is an improvement of precision of estimation, even though the biomass-link model we found showed a large Root Mean Square Error (RMSE) of 47.43 Mgha − 1 . We conclude that ALS data can support the estimation of AGB in logged-over tropical peat swamp forests even if the model quality is relatively low. A modest increase in precision of estimation (from 4.6% to 4.3%), as we found it in our study area, will be welcomed by all forest inventory planners as long as ALS data and analysis expertise are available at low or no cost. Otherwise, it gives rise to a challenging economic question, namely whether the cost of the acquisition of ALS data is reasonable in light of the actual increase in precision.