Information For Forest Management Research Articles

Permafrost melting enhances growth but leads to less intra-annual density fluctuations in boreal Larix gmelinii forests at its southernmost limit in northeast China

Permafrost melting due to climate warming in recent decades has produced significant effects on forest ecosystems, especially the boreal biome at its southernmost limit in Asia. How this warming affects wood formation of trees at intra-annual resolution is unclear, yet is crucial for assessing the impact of permafrost melting on boreal forest growth. In this study, we compared the radial growth and intra-annual wood density fluctuations (IADFs) of Dahurian larch (Larix gmelinii Rupr.) at a permafrost (PF) and a non-permafrost (NPF) site at the southernmost permafrost limit in northeast China and quantified their relationships with climate factors. Drought in early summer was the main factor limiting growth of Dahurian larch. The basal area increment (BAI) of trees at both sites increased initially and then decreased in the 1980s, probably in response to warm-dry climate conditions. Earlywood IADFs (IADF-E) occurred in 14.0% and 9.3% of dated rings at the NPF and PF sites, while the frequency of latewood IADFs (IADF-L) was 6.8% and 2.7% at these two sites. The frequency of IADF-E in trees at both sites was positively and negatively related to June temperatures (and vapor pressure deficit) and precipitation, respectively, suggesting drought stress in June triggered the formation of IADF-E. The IADF-Ls were probably formed in response to warm temperatures in the late growing season. A higher BAI and a lower frequency of IADF-Es of trees at the PF site than at the NPF site indicated that permafrost melting could alleviate drought stress in early summer and promote radial growth of Dahurian larch. This greatly improved forest carbon sequestration and wood quality of some northeastern Asian boreal forests may offset to some extent the adverse effects of warming-drying climates at some sites of northeast Asia. Larch IADF-Es recorded extreme droughts in early summer, giving us a new sight for reconstructing high-frequency extreme climate events. If climate warming continues, the benefits of permafrost melting will gradually disappear and even turn negative due to warmer-dryer climate conditions. Our findings provide valuable information for boreal forest management and conservation under future global warming.

Examining the Impacts of Pre-Fire Forest Conditions on Burn Severity Using Multiple Remote Sensing Platforms

Pre-fire environmental conditions play a critical role in wildfire severity. This study investigated the impact of pre-fire forest conditions on burn severity as a result of the 2020 Bighorn Fire in the Santa Catalina Mountains in Arizona. Using a stepwise regression model and remotely sensed data from Landsat 8 and LiDAR, we analyzed the effects of structural and functional vegetation traits and environmental factors on burn severity. This analysis revealed that the difference normalized burn ratio (dNBR) was a more reliable indicator of burn severity compared to the relative dNBR (RdNBR). Stepwise regression identified pre-fire normalized difference vegetation index (NDVI), canopy cover, and tree density as significant variables across all land cover types that explained burn severity, suggesting that denser areas with higher vegetation greenness experienced more severe burns. Interestingly, residuals between the actual and estimated dNBR were lower in herbaceous zones compared to denser forested areas at similar elevations, suggesting potentially more predictable burn severity in open areas. Spatial analysis using Geary’s C statistics further revealed a strong negative autocorrelation: areas with high burn severity tended to be clustered, with lower severity areas interspersed. Overall, this study demonstrates the potential of readily available remote sensing data to predict potential burn severity values before a fire event, providing valuable information for forest managers to develop strategies for mitigating future wildfire damage.

Soil organic carbon, pH, and ammonium nitrogen controlled changes in bacterial community structure and functional groups after forest conversion

IntroductionLand-use changes significantly impact soil properties in forests, which is an area of concern. Therefore, the effects of changing forest types on soil microbial communities and their functions in northern subtropical forest regions need to be further researched.MethodsWe used 16S rDNA sequencing and Functional Annotation of Prokaryotic Taxa (FAPROTAX) to assess the variation of soil bacterial communities and potential functions related to carbon (C) and nitrogen (N) cycling in two soil layers (0–10 and 10–30 cm) after the conversion of the secondary masson pine (Pinus massoniana, PM) forest to plantations of slash pine (Pinus elliottii, PE) and Chinese fir (Cunninghamia lanceolata, CL) located in Jingde County, Anhui Province, China.ResultsThe study found that converting coniferous secondary forests to coniferous plantations resulted in a notable increase in soil pH and a decrease in nitrate nitrogen and organic carbon contents. Additionally, soil microbial diversity increased significantly, and microbial community structure changed, particularly in the topsoil. These changes might affect the C- and N-cycling mediated by soil bacteria. The analysis revealed a significant decrease in the abundance of functional groups associated with C-cycling and a significant increase in the abundance of functional groups associated with N-cycling, particularly those associated with denitrification. Soil organic carbon, pH, and ammonium nitrogen were the most critical variables affecting changes in the soil microbial community.DiscussionThese findings provide valuable information for ecological restoration and future sustainable forest management.

Tree-Level Chinese Fir Detection Using UAV RGB Imagery and YOLO-DCAM

Achieving the accurate and efficient monitoring of forests at the tree level can provide detailed information for precise and scientific forest management. However, the detection of individual trees under planted forests characterized by dense distribution, serious overlap, and complicated background information is still a challenge. A new deep learning network, YOLO-DCAM, has been developed to effectively promote individual tree detection amidst complex scenes. The YOLO-DCAM is constructed by leveraging the YOLOv5 network as the basis and further enhancing the network’s capability of extracting features by reasonably incorporating deformable convolutional layers into the backbone. Additionally, an efficient multi-scale attention module is integrated into the neck to enable the network to prioritize the tree crown features and reduce the interference of background information. The combination of these two modules can greatly enhance detection performance. The YOLO-DCAM achieved an impressive performance for the detection of Chinese fir instances within a comprehensive dataset comprising 978 images across four typical planted forest scenes, with model evaluation metrics of precision (96.1%), recall (93.0%), F1-score (94.5%), and AP@0.5 (97.3%), respectively. The comparative test showed that YOLO-DCAM has a good balance between model accuracy and efficiency compared with YOLOv5 and advanced detection models. Specifically, the precision increased by 2.6%, recall increased by 1.6%, F1-score increased by 2.1%, and AP@0.5 increased by 1.4% compared to YOLOv5. Across three supplementary plots, YOLO-DCAM consistently demonstrates strong robustness. These results illustrate the effectiveness of YOLO-DCAM for detecting individual trees in complex plantation environments. This study can serve as a reference for utilizing UAV-based RGB imagery to precisely detect individual trees, offering valuable implications for forest practical applications.

How does urbanization affect the fauna of the largest urban forest in Mexico?

Animals in urban forests provide ecological and cultural services, but the quality of these services often depends on floral diversity and animal diversity. In turn, animal diversity in urban forests is influenced by various aspects of habitat quality, in spatial scale- and taxon-dependent ways. Understanding the effect of individual habitat factors on different animal groups can provide valuable information for urban forest management. We estimated the relationships of 25 environmental, vegetation, land cover and anthropogenic perturbation variables to the fauna of Chapultepec Forest within Mexico City, Mexico. We included flower-visiting flies (Diptera: Bombyliidae, Syrphidae), bees (Hymenoptera: Apoidea), butterflies (Lepidoptera: Papilionoidea), birds, and reptiles (Squamata and Testudines). Insect populations were more strongly affected by herb and flower variables, while variables related to trees and shrubs were more important to vertebrate taxa. In general, human density, buildings cover and pavement cover were the most relevant anthropogenic perturbation factors. Birds and butterflies were more tolerant of urbanization than the other groups. Effects for each taxon are discussed. Our results suggest that urban park management plans should focus on enhancing habitat heterogeneity and vegetation structure complexity. Specifically, maintaining herb and tree diversity would likely promote animal diversity and ecosystem services in the Chapultepec Forest.

Assessing the performance of random forest regression for estimating canopy height in tropical dry forests

<span lang="EN-US">Accurate estimation of forest canopy height is essential for monitoring forest ecosystems and assessing their carbon storage potential. This study evaluates the effectiveness of different remote sensing techniques for estimating forest canopy height in tropical dry forests. Using field data and remote sensing data from airborne lidar and polarimetric synthetic aperture radar (SAR), a random forest (RF) model was developed to estimate canopy height based on different indices. Results show that the normalize difference build-up index (NDBI) has the highest correlation with canopy height, outperforming other indices such as relative vigor index (RVI) and polarimetric vertical and horizontal variables. The RF model with NDBI as input showed a good fit and predictive ability, with low concentration of errors around 0. These findings suggest that NDBI can be a useful tool for accurately estimating forest canopy height in tropical dry forests using remote sensing techniques, providing valuable information for forest management and conservation efforts.</span>

Hydrological Modelling to Inform Forest Management: Moving Beyond Equivalent Clearcut Area

Forest disturbance can alter the hydrologic conditions of a watershed, including the frequency, magnitude, and timing of peak and low flows. Equivalent Clearcut Area (ECA) has been routinely used in watershed assessments to estimate hydrologic alteration due to forest disturbance. ECA analyses typically rely on broad regional assumptions, qualitative observations, and/or expert judgement, making it difficult to provide accurate quantitative estimates of hydrologic change. Process-based hydrological models offer an improved approach since they replicate watershed processes, can simulate land cover and climate change scenarios, and provide quantitative estimates of hydrologic change, including at ungauged points of interest. A workflow using a regionally calibrated hydrological model to investigate forest disturbance and future climate change scenarios is demonstrated. Results are contrasted with ECA-based outputs and emphasize that in addition to the amount of forest disturbance, watershed physical characteristics and the location of disturbance within a watershed influence the hydrologic response. This approach provides forest managers with quantitative outputs that support risk-based forest management decisions and presents a substantial improvement over ECA-based methods. Keywords: hydrological modelling, ECA-based analysis, watershed assessment, forest disturbance, cumulative effects, risk-based forest management

Community forestry dominates the recent land greening amid climate change in Nepal

The Himalaya Plateau including Nepal is ‘greening up’ that has important implications to ecosystem services such as water supply, carbon sequestration, and local livelihoods. Understanding the combined causes behind greening is critical for effective policy makings in forest management and climate change adaptation towards achieving sustainable development goals. This national scale study comprehensively examined the natural and anthropogenic drivers of the long-term trend of vegetation dynamics across Nepal by correlation analysis and multiple linear regression analysis. We integrated multiple sources of data including global satellite-based leaf area index (LAI), climate data, landcover data, and forest land management information. Our study reveals a remarkable annual mean LAI increase of 22% (0.009 m2 m−2 yr−1) (p < 0.05) from 1982 to 2020, with an acceleration in the rate of increase to 0.016 m2 m−2 yr−1 (p < 0.05) after 2004. The community forestry (CF) program, forest area changes, and soil moisture availability accounted for 40%, 12%, and 10% of LAI temporal variability, respectively. Our analysis found soil moisture and forest area changes to be the primary drivers of the greening trend before 2004, while CF and forest expansion were the dominant factors thereafter. Additionally, interannual vegetation dynamics were significantly influenced by winter precipitation, incoming solar radiation, and pre-monsoon soil moisture. The projections based on four Earth System Models from Coupled Model Intercomparison Project Phase 6 suggest that Nepal’s greening trend is expected to continue at a rate of 0.009 m2 m−2 yr−1 (p < 0.05) throughout the 21st century. We conclude that forest management program (CF) amid climate change that alters water and energy conditions have enhanced land greening, posing both opportunities and risks to ecosystem services in Nepal. This study provides much needed national-level information for developing forest management policies and designing Nature-based Solutions to respond to climate change and increasing demands for ecosystem services in Nepal.

Drought Severity and Trends in a Mediterranean Oak Forest

Drought is a significant natural hazard with widespread socioeconomic and environmental impacts. This study investigated the long-term drought characteristics in a Mediterranean oak forest ecosystem using the Standardized Precipitation Evapotranspiration Index (SPEI) at various time scales and seasons. The analysis was based on a long-term time series dataset obtained from a meteorological station located at the University Forest of Taxiarchis in Greece. The dataset encompassed a substantial time span of 47 years of continuous monitoring, from 1974 to 2020. To accomplish the goals of the current research, the SPEI was calculated for 3, 6, 12, and 24-month periods, and drought events were identified. The Mann-Kendall (M-K) test was used to analyze the trends in drought severity and evaluate the trends significance. The results showed that shorter time scales (SPEI3 and SPEI6) were more efficient for identifying short-term droughts, while longer time scales (SPEI12 and SPEI24) were better for identifying less frequent but longer-lasting drought episodes. The analysis consistently revealed positive trends across all seasons and time scales, indicating an overall transition towards wetter conditions. Nearly all the data series for SPEI12 and SPEI24 exhibited statistically significant upward trends (wetter conditions) at a 95% confidence level. However, more intense events were detected during the recent decade using the seasonal analysis. Additionally, as the time scale expanded, the magnitude of these trends increased. The findings contributed to a better understanding of drought dynamics in Mediterranean oak forests and provided valuable information for forest management and climate change adaptation planning.

Assessing Site Productivity via Remote Sensing—Age-Independent Site Index Estimation in Even-Aged Forests

Forest productivity is a key driver of forest growth and yield and a critical information need for forest management and planning. Traditionally, this information has come from field plots, but these are expensive to measure and have limited coverage. Remote sensing, on the other hand, can provide forest inventory attributes on landscape scales and with a relatively low cost. A common predictor of forest productivity is site index (SI), traditionally estimated from age and height. In plantations, age can often be treated as a known quantity, but in natural-origin forests (of which Canada has vast swaths), age is often unknown and must be estimated, requiring expensive field work and resulting in a high level of error which, in turn, introduces error into SI estimates. The objective of this study is to generate estimates of SI from two successive LiDAR captures. The 99th percentiles (p99) of LiDAR returns from two successive captures 13 years apart were used along with species-specific SI curves to estimate SI. The results were compared to field-based estimates of SI for two major boreal species, jack pine and black spruce in managed and unmanaged conditions. Overall, the difference between the LiDAR-based SI and the field estimate was 2% with a relative mean squared error of 18%. For the few situations in which the height change was small or negative (less than 0.5%/year), SI was estimated from the average p99 and an assumed age of 100. The advantage of this method is that it does not require field sampling or estimates of age. Using two successive LiDAR captures, wall to wall estimates of SI can be generated at the grid cell level (e.g., 20 × 20 m), a level of detail generally not found in inventories. Overall, our results demonstrate the excellent potential for estimating SI from LiDAR alone, without age, to provide detailed productivity information for forest management and inventory that has been lacking in most large-scale inventories until now.

Quantifying the Profiles of Heartwood, Sapwood, and Bark Using a Seemingly Unrelated Mixed-Effect Model for Larix Olgensis in Northeast China

Heartwood, sapwood, and bark constitute the main components of the tree stem. The stem is the main component of the tree and plays an important role in supporting the tree and transporting nutrients and water. Therefore, quantifying the profiles of heartwood, sapwood, and bark is fundamental to understanding the different components of the tree stem. A seemingly unrelated mixed-effect model system was developed based on 179 destructively sampled trees for 31 permanent sample plots in Korean larch plantation in Northeast China. The heartwood radius and sapwood width were estimated and calibrated only by the observed bark thickness or by all response variables considering the correlations of submodel random effects. The results indicated that the model system achieved good fitting performance and prediction. In addition, after including one to ten bark thickness points and all response variables of sampling below the 2 m height of the tree, the estimated best linear predictor (EBLUP) for local calibration improved the prediction performance, indicating that the heartwood radius and sapwood width could be effectively calibrated by bark thickness while keeping intact the complete inner structure inside the stem. The results provided important information for forest managers and ecologists when selecting appropriate approaches for quantifying the profiles of heartwood, sapwood, and bark.

Silvicultural Treatments Affect Growth and Foliar Nutrients in a Young Ponderosa Pine Stand

Abstract Foliar nutrient status, diameter, and height growth were investigated for young ponderosa pine (Pinus ponderosa) stands grown under six fertilizer and herbicide treatments (including control) in southeast Washington to diagnose foliar nutrient status and evaluate the effects of silvicultural treatments on growth rates. Two application methods of fertilizer (one “best guess” multi-nutrient fertilizer blend) and herbicide applications (broadcast or spot) were used in the study. Broadcast herbicide application produced higher tree growth responses than spot application. The two broadcast application methods improved diameter increments by 43.3%, whereas the average response of the three spot application methods during the third growing season was only 14.5%. Height growth response for these same two treatments was much less than the diameter response. Fertilizer application in addition to herbicide did not significantly increase tree growth over herbicide alone. Foliar nutrient response to fertilization based on foliar vector analysis was inconsistent, possibly due to nutrient imbalances resulting from the one multi-nutrient blend used in our experiment. Control of grass completion produced a rapid posttreatment early growth trajectory and suggested that stand establishment was successful in this plantation. Our results should also be useful in naturally regenerated young ponderosa pine forests subject to heavy grass competition. Study Implications: Given the increasing need for reforestation in the western United States, results from our study should provide useful information for forest managers. Stand establishment is a critical phase for ponderosa pine stands. Broadcast herbicide application in a recently established plantation produced a rapid early growth trajectory. In this experiment, reducing severe sod grass competition was the most important factor that increased tree growth. Our results may also apply to naturally regenerated ponderosa pine stands. Fertilization with a multi-nutrient fertilizer blend did not significantly increase tree growth as a separate treatment or when combined with herbicide. Foliar nutrient response to fertilization was inconsistent, possibly due to nutrient imbalances resulting from the multi-nutrient fertilizer blend used in our experiment. Fertilization rate and nutrient composition trials are needed to develop effective fertilization treatments in young ponderosa pine stands. Based on our results, fertilizing without vegetation control is not recommended when there is heavy grass competition. The grasses proliferated following only fertilization to the detriment of the trees. For the conditions in our study, we cannot recommend adding fertilizer in addition to the herbicide as a treatment because fertilization did not significantly increase tree growth over herbicide alone. We feel fertilization rate and nutrient composition trials are needed to develop effective fertilization treatments in young ponderosa pine stands. The establishment of ponderosa pine forests is vital for the future timber they may produce and the ecological benefits they provide. We recommend broadcast herbicide as an effective silvicultural treatment to control competing vegetation (from grasses in our experiment) during the stand establishment of ponderosa pine. As demonstrated in our study, the rapid posttreatment early growth trajectory from vegetation control suggests that stand establishment was successful.

Machine learning-based prediction and assessment of recent dynamics of forest net primary productivity in Romania

While the analysis of spatio-temporal changes in the net primary productivity (NPP) of forests can provide critical information on carbon cycle and climate change, these ecological trends have remained unclear in many countries worldwide, including Romania. By using complex (satellite, forest and climate) data, many sophisticated (machine learning) algorithms and some widely applied (the Mann-Kendall test and Sen's slope estimator) statistical procedures, this study investigates, for the first time, recent forest NPP trends (1987–2018) that occurred in Romania, in relation to climate change that affected the country over the past decades. Following the modelling, mapping and assessment of NPP dynamics, results showed almost exclusively positive trends for this ecological parameter, which accounts for ∼99% of all forest NPP changes that occurred throughout the country, after 1987. Interestingly, almost three quarters (∼73%) of all NPP increasing trends are statistically significant, which indicates that Romania's forests have recently experienced a large-scale improvement in carbon fluxes and stocks. Investigations of eco-climatic relationships suggest that climate change has partially contributed to these surprising NPP dynamics observed in recent decades. All these findings can provide valuable information for forest management and for many stakeholders and policymakers who operate in the forestry and climate fields in Romania.

Development of a site form equation for predicting and mapping site quality. A case study of unmanaged beech forests in the Cantabrian range (NW Spain)

Site form (SF), expressed as the dominant height of a stand at a reference dominant diameter, is used less often than site index (SI) to estimate forest site quality. However, it has the advantage that it is age-independent and can therefore be applied in a wider set of situations in forestry practice. Like SI, elaboration of SF has traditionally required stem analysis or repeated measurements in permanent plots; however, Airborne Laser Scanning (ALS) can nowadays be used to generate site quality practical maps, thereby simplifying the method. The aims of this study were to fit a dynamic equation to stem analysis data in order estimate SF in natural beech forests in NW Spain, as well as to compare the performance of SF for site quality estimation and to analyze the possibility of using ALS data for site quality prediction in these forests. The Algebraic Difference Approach formulation of the Bertalanffy-Richards model provided the best results and defined four curves for dominant heights of 5, 10, 15 and 20 m at a reference dominant diameter of 20 cm. A significant relationship between SF and SI was observed, and we therefore believe that SF is a good approach for site quality estimation. These results can be used directly at inventory plot level for establishing site quality classes without the need to know the stand age, either in terms of SF or translated into SI. On the other hand, ALS data allowed estimation of both dominant height and dominant diameter, although better results were obtained with the former. Applying the SF dynamic model to both data sets enabled prediction of SF at 25x25 m/pixel (and SI using the SI-SF relationship). The overall accuracy of the relationship between the observed SI and that predicted from ALS metrics yielded a coefficient of determination of 0.456 without bias, heteroscedasticity or absence of normality. The results of this unbiased raster model were considered rather good, as predictions were obtained for a pixel size of 25x25 m (0.0625 ha). Prediction of mean SI value for one hectare would thus be necessary to average the values obtained in 16 pixels with an expected error compensation. Use of the raster model based on ALS metrics will enable site quality estimation for current beech stands at high spatial resolution without the need for fieldwork, providing very valuable information for forest managers and researchers.

Optimizing the processing of large amounts of spatial data related to the ecosystem water service provided by forest territories

Abstract Nowadays, an increased interest towards the integration of the concept of ecosystem services has been observed, both in regards to processes related to forest and water resources management, as well as in the context of sustainable development of the economy on a local, regional and national level. In the most common case, what is referred to as forest ecosystem services are possible benefits, which man can reap directly or indirectly from ecosystem functions of forest territories. Therefore, it is of great importance to analyse and assess the interactions between the human factor with the conditions and processes through which forest ecosystems create and sustain ecosystem services. The water ecosystem service provided by forest territory could cause the need to process large amounts of spatial data in the form of forest management and hydrological information, as well as information about sanitary protection zones around water sources, in the context of the administrative territorial division of the country at different levels, creates considerable challenges. The difficulties are due to both the various electronic formats in which the necessary information is maintained, and the need to perform different operations on the input data for the purpose of analysis and decision making. The present paper investigates the possibilities of automating the processing of large volumes of spatial data in the context of the ecosystem water service, provided by forest territories by developing a set of tools operating into the environment of open source GIS. The main purpose is to develop a unified approach based on the use of GIS and suitable for the creation and implementation of methods for analysis and management of processes related to the ecosystem water service provided by forest territories. The approach can be applied in different sectors of the economy, public relations and the environment.

Biomass Estimation and Carbon Storage of Taxodium Hybrid Zhongshanshan Plantations in the Yangtze River Basin

As a pivotal wetland tree, Taxodium hybrid Zhongshanshan has been widely planted in the region of Yangtze River for multipurpose of ecological restoration, field shelter, landscape aesthetics as well as carbon sequestration. However, the carbon allocation patterns across distinct stages of stand development of T. Zhongshanshan are poorly documented. Using a sample of 30 trees which were destructively harvested, this study compared 3 models for assessing aboveground biomass. Furthermore, a linear seemingly unrelated regression (SUR) approach was introduced to fit the system of the best selected model that ensured the additivity property. On this basis, biomass and carbon storage of T. Zhongshanshan stands in the Yangtze River Basin (YRB) were fairly estimated. Specifically, the study developed height-diameter at breast (H-DBH) function. The results showed that the selected 3-parameter polynomial model performed better, and the SUR approach provided more accurate estimates of leaf and stem fractions. The total tree biomass was 53.43, 84.87, 140.67, 192.71 and 156.65 t ha−1 in the 9-, 11-, 13-, 15-, and 22-year-old T. Zhongshanshan stands, and contributed averagely 94.40% of the ecosystem biomass accumulation. The current T. Zhongshanshan stands in the YRB area can store 124.76 to 217.64 t ha−1 carbon, of which total tree ranges from 25.32 to 90.89 t ha−1, with 55.19% to 77.66% storing in the soil. The T. Zhongshanshan had continuous potential for carbon storage during its growth, particularly in the incipient stages. The findings of this research are firsthand information for forest managers for the sustainable management of T. Zhongshanshan in the YRB and similar subtropical areas.

A Deep Learning-Based System for Monitoring the Number and Height Growth Rates of Moso Bamboo Shoots

The number and growth of new shoots are very important information for bamboo forest cultivation and management. At present, there is no real-time, efficient and accurate monitoring method. In this study, a fixed webcam was applied for image capture, optimized YOLOv4 was used to model the detection of moso bamboo shoots, and a strategy of sorting and screening was proposed to track each moso bamboo shoot. The change in the number and height of moso bamboo shoots was obtained according to the number and height of detection boxes. The experimental results show that the system can remotely and automatically obtain the number of moso bamboo shoots and the pixel height of each bamboo shoot at any given time. The average relative error and variance in the number of moso bamboo shoots were 1.28% and 0.016%, respectively, and those for the corresponding pixel height results were −0.39% and 0.02%. This system can be applied to a series of monitoring purposes, such as the daily or weekly growth rate of moso bamboo shoots at monitoring stations and trends in the height of selected bamboo shoots.

Characterizing the spatial patterns of on- and off-year Moso bamboo forests with multisource data in Southeast China

Moso bamboo forests (MBFs) exhibit unique phenological phenomena, such as on-/off-years, which play an important role in regulating the sustainable management of MBF ecosystems. With human disturbance, on- and off-year effects occur at both spatial and temporal scales. Quantifying the spatial pattern of MBF performance remains a challenge because of physiological and ecological complexity, regional policy differences and human management intensity. In this paper, three zones (Anji, Guangde and Yixing) with similar administrative areas were selected for study. An on-/off-year MBF map was constructed by Sentinel-2 time series data and the multitemporal bamboo index. The spatial distribution patterns of the on-/off-year MBFs were analyzed from different perspectives, such as administrative zones and physical conditions. A new Moso bamboo on-/off-year differentiation index (MODI) was proposed, and MODI spatial pattern maps for different administrative zones were completed and comparatively analyzed. The results showed that (1) the total administrative area of each study zone was nearly 2000 km2, but the MBF distribution areas were quite different, and the MBF spatial areas in Anji, Guangde and Yixing were 637.6 km2, 334.5 km2 and 112.0 km2, respectively. (2) The MBFs were mainly distributed in mountainous areas, especially at elevations ranging from 100 to 400 m and slopes ranging from 5 to 35°. (3) On- and off-year MBFs occurred in different provinces; generally, the area of the off-year MBFs was larger than that of the on-year MBFs in each zone in 2019. (4) The newly proposed MODI can effectively detect spatial distribution differences between the on- and off-year MBFs in a region, providing valuable information for government decision-making and bamboo forest management.

Pengembangan dan Implementasi Sistem Informasi Tiga Tema Pada Kemen-LHK

The integrated information system solution is one example of a solution from information technology which is currently very helpful for humans in processing data and presenting a report that is precise, fast and accurate. One example of a much-needed information system is the forest area management information system managed by the Ministry of Environment and Forestry. The information system is managed by the Directorate General, there are a number of systems that can be used to build a system used in management, supervision and control, namely an information system related to areas, deforestation and licensing, including the management of environmental permits through Amdal.net. This research was conducted with a two-method approach, namely a field study conducted by direct analysis of the system that is currently running where the system will later be integrated into a three-theme information system and literature study. which is done by collecting informationrelated to the forest area management system including Amdal.net. The result of the research is the application of the Three Themes Information System which is an integration of: Forest Area, Licensing and Deforestation Themes. The Ministry of Environment and Forestry can benefit from this three-themed system, namely the integration of mapping of several forest areas that is integrated with permits and deforestation. This web-basedsystem was built using the PHP 7.4 programming language with the Code Igniter framework and the MariaDB database.

Micro-Data Efficiency Evaluation of Forest Companies

The forestry sector is facing critical challenges due to climate change. Decision-making support based on efficiency evaluation using non-parametric methods could provide important information for both forest managers and policymakers. However, such advanced technical analysis is scarce in forestry science. When applied, its application has been primarily based on aggregated, macro-level data, and efficiency was analysed for the forestry sector as a whole. There is a lack of studies from the company-level perspective, which are needed to provide sound decision support.In this paper, we focus on the micro-data level and offer the data envelopment analysis model settings and interpretations for an efficiency evaluation based on the financial data of individual forestry companies. The aim is to provide an original analysis of the company-level driving forces of forestry sector efficiency. The results for central European countries show that efficiency is driven by company size and country of operation. The study also confirms that, generally, German companies are the »efficiency leaders« in the region, while Czech companies may serve as an efficiency reference for east-central European forestry companies.