Mixed Forest Area Research Articles

Soil pH enhancement and alterations in nutrient and Bacterial Community profiles following Pleioblastus amarus expansion in tea plantations

BackgroundThe expansion of bamboo forests increases environmental heterogeneity in tea plantation ecosystems, affecting soil properties and microbial communities. Understanding these impacts is essential for developing sustainable bamboo management and maintaining ecological balance in tea plantations.MethodsWe studied the effect of the continuous expansion of Pleioblastus amarus into tea plantations, by establishing five plot types: pure P. amarus forest area (BF), P. amarus forest interface area (BA), mixed forest interface area (MA), mixed forest center area (TB), and pure tea plantation area (TF). We conducted a comprehensive analysis of soil chemical properties and utilized Illumina sequencing to profile microbial community composition and diversity, emphasizing their responses to bamboo expansion.Results(1) Bamboo expansion significantly raised soil pH and enhanced levels of organic matter, nitrogen, and phosphorus, particularly noticeable in BA and MA sites. In the TB sites, improvements in soil nutrients were statistically indistinguishable from those in pure tea plantation areas. (2) Continuous bamboo expansion led to significant changes in soil bacterial diversity, especially noticeable between BA and TF sites, while fungal diversity was unaffected. (3) Bamboo expansion substantially altered the composition of less abundant bacterial and fungal communities, which proved more sensitive to changes in soil chemical properties.ConclusionThe expansion of bamboo forests causes significant alterations in soil pH and nutrient characteristics, impacting the diversity and composition of soil bacteria in tea plantations. However, as expansion progresses, its long-term beneficial impact on soil quality in tea plantations appears limited.

The sources of nitrate exported from a watershed containing mixed forest, paddy fields, and urban areas in Japan: differences between baseflow conditions and rainfall events

The sources of nitrate exported from a watershed containing mixed forest, paddy fields, and urban areas in Japan: differences between baseflow conditions and rainfall events

Dynamics of land use and land cover changes in Amibara and Awash-fentale districts, Ethiopia

The analysis of land-use and land-cover (LULC) changes is crucial for rural development planning, food security monitoring, and natural resource conservation. This study focuses on detecting LULC changes in Amibara and Awash-Fentale districts from 1985 to 2021. We utilized five sets of Landsat data (Landsat 5 TM for 1985, 1995, 2002, and Landsat 8 OLI for 2015 & 2020) and applied supervised maximum likelihood classification. Accuracy assessments revealed overall accuracies ranging from 88.9% to 95.3% for Amibara and 89.5%–93.2% for Awash-Fentale. Both districts exhibited six main LULC classes: agriculture, bareland, built-up, mixed forest, shrubland, and water bodies. In Amibara LULC changes from 1985 to 2021 revealed significant shifts, maintaining its primary bareland characteristic, concentrated agriculture, and expanding Prosopis-dominated shrubland due to livestock-mediated seed dispersal. Conversely, in Awash-Fentale bareland dominance decreased from 92.28% to 67.02%, while agriculture, built-up areas, and shrubland expanded. Water bodies emerged between 2015 and 2021 which is associated with the construction of Kesem Kebena dam for sugar cane farm production. The net gains were observed in shrubland (12.9%), agriculture (5.8%), mixed forest (4.1%), water bodies (1.5%), and built-up areas (0.9%), with bareland experiencing a loss of 25.3%. In conclusion, Amibara and Awash-Fentale underwent both comparable and distinct LULC shifts, featuring prevalent bareland and central agriculture, alongside Prosopis-driven shrubland expansion due to livestock dispersal. While mixed forest exhibited fluctuations, built-up areas and water bodies remained limited. Notably, Awash-Fentale showed higher LULC variability. Understanding these land cover changes helps assess vulnerability to climate impacts like droughts and floods, enhancing climate resilience. Insights from this study can inform sustainable land-use planning, conservation strategies, and policy interventions in the Afar region and similar areas. These observations highlight the need for integrated land management approaches that balance socioeconomic development with environmental sustainability.

The impact of tourist destinations on wildlife in northern Finland

ABSTRACT Increased tourism impact, recreational activities and structures in natural areas can affect wildlife by increasing urbanization, landscape fragmentation and disturbance. I examined the potential effects of tourist destinations on four forest grouse (Tetraoninae) and five mammalian species in northern Finland. I analyzed density data obtained from wildlife counts carried out by hunters around ten tourist destinations. The densities of mountain hare and mustelid species were negatively correlated with the distance to a tourist destination. The densities of adult grouse, juvenile grouse, mountain hare, and mustelids were positively correlated with the area of mixed forests surrounding the destinations. The densities of adult and juvenile grouse were positively correlated, while the densities of pine marten and mustelids were negatively correlated with the area of agricultural land surrounding the destinations. The densities of the studied wildlife species varied among destinations and years. It seems that current recreational activities have not caused significant changes in the occurrence and abundance of wildlife species in the surroundings of the tourist destinations studied. Location of a destination, predator densities, and landscape structure around the destination had the most impact on the density of wildlife species.

Can Landuse Landcover changes influence the success of India's national clean air plans ?

India implemented a range of multifarious strategies to address the issue of substandard air quality. One such flagship scheme of government of India is National Clean Air Programme (NCAP), which recommends sector specific reduction in emissions and increase in forest cover etc. To reduce particulate matter concentrations by 40% in 2026 compared to 2019. The present study aims to gauge the impact of Land Use Land Cover (LULC) changes alone on success of NCAP, using weather research forecasting model with chemistry (WRF-Chem) and integrated geographical information system and remote sensing software Terrset. The findings elucidate that, by the year 2026, the Ventilation Coefficient (VC) in India's eastern, central, northern, and north-eastern regions is anticipated to register a decline ranging from 18% to 50% compared to the baseline year of 2019. Conversely, an increase of 17% is expected in the southern region. The alterations in Fallow Land, Barren and sparsely vegetated land, Urban and Built-up Land, and Tundra, contribute to these shifts, displaying varying percentage changes across distinct zones. Simulations indicate that these LULC changes are impeding the planned reduction in PM2.5 levels. Projections suggest an increase in PM2.5 levels as high as 13% in the eastern, central, northern, and north-eastern regions, accompanied by a decrease of 33% in the Southern zone of the country. Significantly, non-attainment cities in Himachal Pradesh and Maharashtra are expected to witness a substantial rise in PM2.5-induced premature mortality, with Pune city projected to experience over 24,525 additional premature deaths by 2026. A comparable examination conducted for the year 2022, utilizing actual LULC data, suggests that if the NCAP fails to effectively implement LULC changes, it may reduce this anticipated trade-off. Addressing this concern, the study employed WRF-Chem to simulate 60 combinations, proposing LULC enhancements conducive to improving VC. The results underscore the critical importance of preserving at least 36% of the LULC category of mixed forest land, encompassing plantations, orchards, and areas under shifting agriculture. Additionally, a reduction in barren land and fallow land emerges as pivotal for enhancing the ventilation coefficient. The study accentuates the necessity of refraining from further expansion in densely populated areas to counter these anticipated VC trends. This study provides valuable insights, highlighting the need to prioritize LULC management to effectively combat the alarming air pollution.

Patterns and Dominant Driving Factors of Carbon Storage Changes in the Qinghai–Tibet Plateau under Multiple Land Use Change Scenarios

Revealing the spatial–temporal evolution of carbon storage and its driving mechanisms in the Qinghai–Tibet Plateau could provide support for decision making in the protection of regional ecosystems and the achievement of regional dual-carbon goals. In this study, the spatial–temporal evolution of carbon storage in the Qinghai–Tibet Plateau was analyzed under various scenarios using PLUS-InVEST and a gravity center model, and the driving mechanisms of carbon storage were clarified with Geodetector. The results are as follows: (1) During 2000–2020, the areas of coniferous forest, evergreen broad-leaved forest, closed shrub, temperate shrub desert, multi-tree grassland, and grassland showed an increasing trend, while the areas of deciduous broad-leaved forest and mixed forest showed a decreasing trend. (2) During 2030–2060, there was a decreasing trend in the total carbon storage of the Qinghai–Tibet Plateau under three different scenarios. (3) During 2030–2060, the area of the Qinghai–Tibet Plateau was mostly represented by carbon balance (56%), while the areas of carbon sources and carbon sinks showed a scattered distribution. (4) The precipitation and topographic factors with a q value of 0.888 played a dominant role in affecting the spatio-temporal variations in carbon storage in the Qinghai–Tibet Plateau. (5) In future ecological protection and restoration efforts, more high-quality farmlands should be protected and constructed, which could contribute to the achievement of dual-carbon goals. In addition, the hydrothermal conditions should be improved to aid the carbon cycle process in the Qinghai–Tibet Plateau.

Summer habitat for the female Tricolored Bat (Perimyotis subflavus) in Tennessee, United States

Abstract The Tricolored Bat is an imperiled species due to white-nose syndrome. There is limited information available on roosting and foraging area use of the species to support planning and management efforts to benefit recovery in the Southeastern United States. Female tricolored bats exit hibernation and allocate energy toward disease recovery, migration, and reproduction. Providing and managing for summer habitat is 1 strategy to promote recovery. We sought to: (1) determine local- and landscape-scale factors that influence female Tricolored Bat roost selection; (2) quantify land cover use in core and overall foraging areas; and (3) define foraging area size and distances traveled by female tricolored bats in Tennessee. Bats in this study roosted in trees of variable sizes, in multiple tree species with large canopy volumes, and almost always roosted in trees with dead leaf foliage suspended in the canopy. Forest plots used by bats had trees averaging 30 cm diameter at breast height, basal areas averaging 27 m2/ha, contained multiple tree species, and comprised around a 50:50 ratio of canopy and subcanopy trees. Bats did not roost in coniferous forest areas and were only located in deciduous and mixed forest areas. Bats foraged near and directly over water, in open areas, and along forest edges. This study increases our knowledge on habitat requirements of the species in a temperate region dominated by unfragmented forests and many large water bodies and serves a baseline for management and efforts to benefit survival, reproduction, and population recovery.

Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17

Abstract. A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.

Classification of Standing Types at PT. Work Locations. Sustainable Forest Tusam in Krakap Dusun, Umang Village, Linge District, Central Aceh District

PT. Tusam Hutani Lestari is a company with an area of ± 97,300 Ha which has an Industrial Plantation Forest Concession Rights permit number 452/Kpts-11/92 dated 14 May in Aceh Province which is in the districts of Central Aceh, North Aceh, Bener Meriah and Bireuen. With a large working area, the forest area is divided into 6 working blocks, namely the Mount Salak block, Blangkuyu block, Lampahan block, Burni Telong block, Bidin block and Jambu Aye block, making this area have varied biodiversity. This observation aims to determine the diversity of types of stand distribution found on mixed forest land and to determine the value of the important value index (INP) for a type of stand at the PT work site. Tusam Sustainable Forest. The results show that there are 8 types of stands or trees with a total of 17 individuals in the mixed forest area. The dominant important value index (INP) is found in Eucalyptus trees at 60.57% and Pine at 58.66%, while the INP for all plant types in mixed forest area cover is 300% and is included in the high value, and the diversity index for the growth rate of mixed forest land cover shows that the PT Tusam Hutani Lestari work area is included in the medium criteria.

Detection of Population Density, LULC Variation and Cross-Regional Similarities Using K-Means Clustering Algorithm in Istanbul Example

In this study, the effect of urban sprawl on land change in Istanbul was examined using Geographic Information System (GIS) technologies and the CORINE Land Cover (CLC) data set produced for the years 1990-2018 and population information. According to this; It has been determined that urban sprawl in the study area has increased due to population growth, especially industrial units, city structures, mines and construction sites have increased by approximately 9%, while maquis areas, arable, mixed agricultural areas and forest areas have decreased by 9%. According to the K-means application, similarities in the districts were revealed between 1990 and 2018. According to the results obtained, it was determined that the districts that were in clusters with similar characteristics in the 1990s changed over time and were located in different clusters. As a result, it is predicted in the study that urban sprawl will increase further due to population growth in Istanbul.

Orchidaceae in Iguaçu National Park, Paraná, Brazil

Abstract This study presents a synopsis of the Orchidaceae species in Iguaçu National Park (ParNa Iguaçu), one of the largest Atlantic Forest remnants in the state of Paraná. Orchidaceae is represented in the area by 65 species, distributed in 41 genera, the most representative being Gomesa (7 spp.) and Acianthera (6 spp.). Representatives of three subfamilies are present: Vanilloideae (2 spp.), Orchidoideae (12 spp.) and Epidendroideae (51 spp.) which, as expected, presents the greatest richness (78% of the total). Among the species found, five are considered endemic to Brazil, 23 are endemic to the Atlantic Forest (36%) and one is endemic to Paraná. Thirty-one new records were found for the area. The areas of Mixed Ombrophilous Forest (MOF) had 29 exclusive species, the Semideciduous Seasonal Forest (SSF) had 20, while 16 species occur in both phytophysiognomies. A new occurrence was recorded for MOF. Regarding habit, exclusively epiphytic was the most representative (39 spp.), followed by exclusively terricolous (15 spp.), two vines and one mycoheterotrophic species. Among the families already inventoried in ParNa Iguaçu, Orchidaceae is among the richest and the findings of the present study reinforce the importance of floristic studies for cataloging the local flora.

Spatio-Temporal Change of Land Use/Land Cover and Vegetation Using Multi-MODIS Satellite Data, Western Ethiopia.

Land use and land cover (LULC) change and variability are some of the challenges to present-day water resource management. The purpose of this study was to determine LULC and Normalized Difference Vegetation Index (NDVI) fluctuations in western Ethiopia during the last 20 years. The first part of the study used MODIS LULC data for the change analysis, change detection, and spatial and temporal coverage in the study region. In the second part, the study analyzes the NDVI change and its spatial and temporal coverage. In this study, The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were applied to determine LULC and NDVI changes over four different periods. Evergreen broadleaf forests, deciduous broadleaf forests, mixed forests, woody savannas, savannas, grasslands, permanent wetlands, croplands, urban and built-up lands, and water bodies are the LULC in the period of analysis. The overall classification accuracy for the classified image from 2001 to 2020 was 85.4% and the overall kappa statistic was 81.2%. The results indicate a substantial increase in woody savannas, deciduous broadleaf, grasslands, permanent wetlands, and mixed forest areas by 119.6%, 57.7% 45.2%, 37%, and 21.3%, respectively, followed by reductions in croplands, water bodies, savannas, and evergreen broadleaf forest by 90.1%, 19.8%, 13.2%, and 4.8%, respectively, for the catchment between 2001 and 2020. The result also showed that the area's vegetation cover increased by 64% from 2001 to 2022. This study could provide valuable information for water resource and environmental management as well as policy and decision-making.

High uncertainty of evapotranspiration products under extreme climatic conditions

Terrestrial Evapotranspiration (ET) is an important process for understanding regional or global water, energy, and carbon cycles, and with the development of satellite observations and increased research investment, grid ET products covering a broad spatial extent are becoming more readily available. However, as global warming becomes a reality and extreme climate events occur worldwide, existing studies do not go far enough to verify that these grid products can still be used under extreme climate conditions. This study evaluates nine global ET products, including land surface reanalysis products (GLDAS_CLSM, GLDAS_NOAH, ERA5, and FLDAS), remote sensing (RS) products (GLEAM_v3.6b, MOD16A2, and PMLv2), and multi-source data fusion products (REA and Synthesized), using observations from 153 flux towers worldwide. The objective is to evaluate their performance in estimating ET under extreme climatic conditions (high temperature, high vapor pressure deficit (VPD), and drought). The results indicate that the estimation accuracy of all ET products is significantly reduced under extreme climatic conditions, showing high uncertainty, and this impact is most severe in the Americas (AM) region. Overall, multi-source data fusion products showed the best estimation performance and were less affected by extreme climatic conditions. Among the remote sensing modeling products, GLEAM_v3.6b showed the best performance, while MOD16A2 has the lowest estimation accuracy. Land surface reanalysis products were most affected by extreme conditions, with CLSM and NOAH showing similar performance and ERA5 having the largest errors (RMSE_ERA5 = 1.699 mm/d, MAE_ERA5 = 1.294 mm/d). The ET products show significant error fluctuations and overestimation (PBias > 0.5) in most of North America, and there is a decline in simulation accuracy in arid and semi-arid regions near 30°N, with most ET products showing overestimation. The most significant errors were observed in cropland areas (CRO) and deciduous broadleaf forest areas (DBF), with significant overestimation in mixed forest areas (MF). The results of this study provide valuable insights for researchers in selecting ET products under extreme climatic conditions and encourage product developers to consider uncertainty under such conditions, thereby improving product accuracy.

Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions

Abstract. Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions of up to a few centimeters. Here, we study the spatiotemporal variability in snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability in snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS–SfM surveys in the winter of 2018/2019 and a snow-free bare-ground survey after snowmelt. Due to poor subcanopy penetration with the UAS–SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS–SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to −16 cm in coniferous forests and −32 cm in peatland during the melt period. This highlights the poor representation of point measurements in selected locations even on the subcatchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth range (5th–95th percentiles) within different land cover types increased from 17 to 42 cm in peatlands and from 33 to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its range were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high-spatial-resolution data, we found a systematic increase (2–20 cm) and then a decline in snow depth near the canopy with increasing distance (from 1 to 2.5 m) of the peak value through the snow season. This study highlights the applicability of the UAS–SfM in high-resolution monitoring of snow depth in multiple land cover types and snow–vegetation interactions in subarctic and remote areas where field data are not available or where the available data are collected using classic point measurements or snow courses.

Coupling of Forest Carbon Densities with Landscape Patterns and Climate Change in the Lesser Khingan Mountains, Northeast China

This research investigated the effects of the forest landscape composition and spatial distribution and local climate change’s lag effects on the carbon density of stands and provides a reference for optimizing the stand structure and sustainable management of forest resources in Xinqing District, Yichun City, Heilongjiang Province, China. Using second-class forest resource survey data of the Xinqing Forestry Bureau for 2007 and 2017, the forest carbon density, landscape pattern index and local forest climate were quantified by ArcGIS10.7, Fragstats4.2 and SPSS25, and a coupling coordination degree model was constructed to reflect their correlations. The overall broadleaved mixed forest area was larger in the new green area, and the overall forest productivity had improved in the past ten years. Forest management gradually improved from nonforest to forest land, resulting in a high degree of fragmentation in the surrounding landscape. The coupling research on the forest carbon density and the forest landscape pattern index and local climate index showed that, overall, the landscape pattern and the impact of climate change on the forest carbon density had a positive interaction; adjustments and improvements can be made to the forest carbon density in the poor-condition area by combining specific situations of the landscape pattern and climate change.

Mapping tree species diversity of temperate forests using multi-temporal Sentinel-1 and -2 imagery

Accurate information on tree species diversity is critical for forest biodiversity, conservation and management, but mapping forest diversity over large and mixed forest areas using satellite remote sensing data remains a challenge because of scale- and ecosystem-dependent relationships between spectral heterogeneity and tree species diversity. In this study, three different diversity indices (Simpson (λ), Shannon (H’), and Pielou (J’)), were tested to characterize forest tree species diversity using individual monthly and multi-temporal Sentinel-1 and -2 images during 2021. The performance of three different machine learning models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Deep Neural Network (DNN) were tested. A collection of 1,020 plot measurements (comprising 47 tree species and 28,122 trees), randomly collected in a mixed broadleaf-conifer forest area in northeast China, was used to train (n = 816) and validate (n = 204) the models. The models dependent on multi-temporal Sentinel-1/2 imagery were found to outperform the models based on individual monthly data, in predicting forest tree species diversity, with average accuracies of 78% for H’, 77% for λ and 77% for J’. The use of DNN performed marginally better than the XGB and RF models, with accuracies of 81% for H’, 80% for λ and 79% for J’, respectively. Finally, a boosted regression model, involving environmental variable predictors and the DNN-based estimated tree species diversity, showed that on average 63 ± 4% of the spatial variations of tree species diversity was explained by environmental variables, including annual temperature (29.30%), followed by soil fertility (27.03%), snow cover (13.63%) and a digital elevation model (12.33%). Our results highlight that an empirical approach based on machine learning and multi-temporal Sentinel-1/2 data can accurately predict forest tree species diversity and we further show the important roles of air temperature and soil fertility in governing the spatial variability of tree species diversity in a mixed broadleaf-conifer forest setting.

Assessing the Performance of a Handheld Laser Scanning System for Individual Tree Mapping—A Mixed Forests Showcase in Spain

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements.

Assessment of SMAP and AMSR2 freeze/thaw products over Russia using in situ measurements

ABSTRACT Recently, many freeze/thaw (FT) products have been developed using spaceborne microwave measurements. In this letter, we compared the newest FT products, SMAP36 (36 km resolution) and SMAP09 (9 km resolution) provided by the Soil Moisture Active Passive (SMAP) mission and one product, AMSR2 FT, generated by the Advanced Microwave Scanning Radiometer 2 (AMSR2) against surface soil temperature measurements across Russia using a total of 494 stations. The results indicate that AMSR2 FT has a better performance in Russia than the SMAP products, which is more pronounced in the AM (descending orbit) value of overall accuracy (OA) than in the PM (ascending orbit). It is challenging to distinguish FT states in the transition periods between warm and cold seasons, but AMSR2 FT performs better than SMAP products. AMSR2 FT has a high OA in inland areas but a low OA along coastal zones because pixels may contain a water body or lake ice. The poorest SMAP results are mainly distributed in mixed forest areas, indicating that the algorithm should be improved to solve the wet snow problem in these areas. The results of this study could help improve FT products in Russia and provide application instructions for FT product users.

The Spatiotemporal Variability of Snowpack and Snowmelt Water 18O and 2H Isotopes in a Subarctic Catchment

AbstractThis study provides a detailed characterization of spatiotemporal variations of stable water 18O and 2H isotopes in both snowpack and meltwater in a subarctic catchment. We performed extensive sampling and analysis of snowpack and meltwater isotopic compositions at 11 locations in 2019 and 2020 across three different landscape features: (a) forest hillslope, (b) mixed forest, and (c) open mires. The vertical isotope profiles in the snowpack's layered stratigraphy presented a consistent pattern in all locations before snowmelt, and isotope profiles homogenized during the peak melt period; represented by a 1–2‰ higher 18O value than prior to melting. Our data indicated that the liquid‐ice fractionation was the prime reason that caused the depletion of heavy isotopes in initial meltwater samples prior to the peak melt period. The liquid‐ice fractionation was influenced by snowmelt rate, with higher fractionation during slow melt. The kinetic liquid‐ice fractionation was evident only in close examination of meltwater lc‐excess values, not 18O values alone. Meltwater was isotopically heavier and more variable than the depth‐integrated snowpack; the weighted mean of meltwater isotope values was higher by 0.62–1.33‰ 18O than the weighted mean of snowpack isotope values in forest hillslope and mixed forest areas, and 1.51–6.37‰ 18O in open mires. Our results reveal close to 3.1‰ 18O disparity between the meltwater and depth‐integrated snowpack isotope values prior to the peak melt period, suggesting that proper characterization of meltwater 18O and 2H values is vital for tracer‐based ecohydrological studies and models.

N-mixture model-based estimate of relative abundance of sloth bear (Melursus ursinus) in response to biotic and abiotic factors in a human-dominated landscape of central India.

Reliable estimation of abundance is a prerequisite for a species' conservation planning in human-dominated landscapes, especially if the species is elusive and involved in conflicts. As a means of population estimation, the importance of camera traps has been recognized globally, although estimating the abundance of unmarked, cryptic species has always been a challenge to conservation biologists. This study explores the use of the N-mixture model with three probability distributions, i.e., Poisson, negative binomial (NB) and zero-inflated Poisson (ZIP), to estimate the relative abundance of sloth bears (Melursus ursinus) based on a camera trapping exercise in Sanjay Tiger Reserve, Madhya Pradesh from December 2016 to April 2017. We used environmental and anthropogenic covariates to model the variation in the abundance of sloth bears. We also compared null model estimates (mean site abundance) obtained from the N-mixture model to those of the Royle-Nichols abundance-induced heterogeneity model (RN model) to assess the application of similar site-structured models. Models with Poisson distributions produced ecologically realistic and more precise estimates of mean site abundance (λ = 2.60 ± 0.64) compared with other distributions, despite the relatively high Akaike Information Criterion value. Area of mixed and sal forest, the photographic capture rate of humans and distance to the nearest village predicted a higher relative abundance of sloth bears. Mean site abundance estimates of sloth bears obtained from the N-mixture model (Poisson distribution) and the RN model were comparable, indicating the overall utility of these models in this field. However, density estimates of sloth bears based on spatially explicit methods are essential for evaluating the efficacy of the relatively more cost-effective N-mixture model. Compared to commonly used index/encounter-based methods, the N-mixture model equipped with knowledge on governing biotic and abiotic factors provides better relative abundance estimates for a species like the sloth bear. In the absence of absolute abundance estimates, the present study could be insightful for the long-term conservation and management of sloth bears.