Diverse Land Research Articles

Assessing Carbon Sequestration and Biomass Distribution across Diverse Land Use Types in Ban Krang Subdistrict, Phitsanulok Province

This study investigates carbon dioxide (CO₂) sequestration and biomass distribution across various plant components and land use types in Ban Krang Subdistrict, Mueang District, Phitsanulok Province, with the goal of enhancing carbon management strategies. Field surveys were conducted using 14 plots of 40x40 meters to quantify biomass and estimate CO₂ sequestration across different vegetation types. The findings reveal an average CO₂ sequestration of 122.81 Ton ha⁻¹, with aboveground biomass, particularly stems, contributing the most to carbon storage. Notably, abandoned perennial crops and mixed perennial crops demonstrated the highest sequestration rates, at 657.94 Ton ha⁻¹ and 613.00 Ton ha⁻¹, respectively. In contrast, agricultural lands such as rice paddies and cassava plantations exhibited the lowest sequestration rates, though rice paddies contributed the highest total CO₂ sequestration, amounting to 61,119.71 Tons, due to their extensive area. The study highlights the critical role of diverse and dense vegetation, particularly perennial crops, in maximizing carbon sequestration. It also underscores the potential for improving carbon storage in agricultural lands through better land management practices. The results suggest that targeted strategies should prioritize high-sequestration land use types while also enhancing carbon storage in low-sequestration areas. By optimizing land use and management practices, the region can significantly increase its carbon storage capacity, contributing to climate change mitigation and promoting long-term ecological sustainability. These insights are crucial for formulating effective carbon management strategies in Ban Krang Subdistrict, as well as in other comparable regions.

Predicting soil properties for fertiliser recommendation in South Korea using MIR spectroscopy

The national fertiliser policies in South Korea aim to provide guidance to farmers for efficient fertiliser application and thus rely on the annual collection and analysis of soil samples. Providing timely soil analysis results remains a challenge, as wet laboratory analysis is time-consuming and expensive. This study represents a pioneering effort in South Korea, by investigating mid-infrared (MIR) spectroscopy for accurate soil properties prediction and its application in developing fertiliser recommendations for several crop types. Additionally, we examined the time efficiency of MIR spectroscopy compared to conventional analytical methods. A total of 567 soil samples from diverse soil and land use types (paddy, upland, orchard, and greenhouse fields) in South Korea (0–20 cm depth) were collected and scanned using an MIR spectrometer. Four machine learning algorithms (partial least squares regression, support vector machine, cubist, and random forest) were trialled and compared for their prediction accuracies using 15-fold cross-validation for eight essential soil properties: organic matter, total nitrogen (N), available phosphorus (P), pH, exchangeable calcium (Ca), potassium (K), magnesium (Mg), and available silica. Results demonstrated robust predictive performance (R2 > 0.70) across the selected soil properties, with organic matter and total nitrogen exhibiting excellent accuracy (R2 > 0.9). Compared with conventional analysis, the average difference in fertiliser application recommendation for seven crops using MIR prediction was 3.8 % for N, 13.9 % for P and 8.1 % for K. Based on the measurement of 11 soil properties, analysis using MIR spectroscopy was about 12 times faster than conventional methods. The study demonstrates the potential of this approach to revolutionise soil analysis protocols, offering a more efficient and cost-effective solution for sustainable agricultural practices in South Korea.

Liverwort (Marchantiophyta) and Hornwort (Anthocerotophyta) Flora of Yenice District (Çanakkale): New Floristic Findings from the Northern Part of Mount Ida (Kaz Dağı)

The Yenice district of Çanakkale stands out due to its diverse land features, being part of the Kaz Dağı (Mount Ida), its expansive coniferous and deciduous forests, and its potential for high biological diversity and endemism. It also provides highly suitable habitats for liverworts. However, the absence of legally protected areas highlights the need for a detailed assessment of the region's biodiversity. Liverworts are key components of floristic biological richness. Nevertheless, studies on the liverwort flora in both Çanakkale province and Yenice district are quite limited. Based on these considerations, this study aimed to investigate the liverwort flora of the Yenice district. During the study, a total of 295 liverwort specimens were collected from 74 different sites representative of the district’s various regions. The collected specimens were identified using appropriate methods and preserved as herbarium material. As a result of the study, 29 liverwort species from 17 families and 1 hornwort species were identified from the area. Of the identified species, 7 taxa are new records for the B6 grid square according to Henderson’s grid system (1961), and 13 taxa are new records for Çanakkale province.

An evaluation of intra-university campus temperature variability under variable synoptic weather conditions using mobile transects.

Intensive observations were collected in a wide range of synoptic weather conditions to evaluate variability in the intra-urban heat island on the campus of the University of North Carolina at Charlotte between February 2023 and June 2023. An easily reproducible bicycle-based mobile transit route around the university was traversed during 20 afternoon and 20 evening periods. The magnitude of observed temperature range from an individual data collection period is defined as the campus urban heat island intensity, with areas having more anthropogenic modification also having higher temperatures. While other papers have examined the relationship between the city-scale urban heat island intensity and the present weather conditions, this paper aims to disentangle the relationship between present weather conditions and the magnitude of thermal variability across a small intra-urban campus with diverse land use and land cover characteristics. This will contribute to a better understanding of intra-urban heat islands, particularly identifying days where conditions will be highly dangerous in more developed areas, and not in more natural environments. When comparing the standardized mobile-transit observations to the regionally present weather conditions it is evident that clear and calm conditions often enhance both city-scale and campus-scale heat islands, increasing temperature disparities. While the spatial distribution of warm and cool areas across campus remains relatively constant, the campus-scale heat island is significantly modulated by the present weather conditions.

Land Classification Plugin for QGIS Using Pix2Pix

Abstract. Land cover classification is critical in various fields, including environmental monitoring, urban planning, and ecological assessment, facilitating informed decision-making processes. Traditional land cover classification methods often involve labor-intensive and time-consuming processes, relying on manual intervention and predefined algorithms. The emergence of deep learning techniques, particularly convolutional neural networks (CNNs), offers a promising solution to automate this process, albeit with complexities in implementation. This study addresses the limitations of existing Geographic Information System (GIS) software and plugins by proposing a novel approach utilizing the Pix2Pix architecture, a type of CNN, for automated land cover classification. The proposed Land Classification Plugin (LCP) integrates seamlessly with QGIS, offering an end-to-end solution for generating classified static maps. The methodology involves preprocessing data, utilizing the Pix2Pix model for image segmentation, and post-processing to produce georeferenced outputs. The development of the LCP involved extensive software and hardware configurations, including essential components like GDAL/OGR, PyTorch, and OpenCV. The plugin's architecture comprises a user-friendly interface for region selection, clipping, and classification aided by the Pix2Pix model. A layout manager feature also allows for the creation of composite maps for enhanced visualization. The accuracy assessment of the LCP demonstrated an overall accuracy of 83.40% across diverse land cover classes, indicating its efficacy in classification tasks. The plugin's capabilities offer significant potential for applications in land management, environmental surveillance, and urban planning, revolutionizing current practices in land cover classification within the realm of GIS software.

Occurrence, transformation, and transport of PFAS entering, leaving, and flowing past wastewater treatment plants with diverse land uses

Per- and polyfluoroalkyl substances (PFAS) have been detected ubiquitously throughout the environment. Wastewater treatment plants (WWTPs) have been identified as potential hotspots for the introduction of PFAS into the environment. Therefore, the occurrence, transformation, and transport of 18 PFAS in two WWTPs with varying treatment processes, prevailing land uses, and during two distinct time periods were investigated. Polar Organic Chemical Integrative Samplers (POCIS) were installed at two WWTPs in Central Kentucky during April and July of 2022. PFAS concentrations typically increased from influent to effluent at both WWTPs, regardless of wastewater treatment processes, but changes in surface water concentrations from upstream to downstream of the effluent mixing zones varied. Both WWTPs discharged the 18 PFAS at higher loads than received, indicating prevalent transformation of PFAS precursors and non-measured PFAS analytes into measurable PFAS. Nearly all measured PFAS persisted in aqueous (86–98%) compartments rather than sediment or biosolids (2–14%). All biosolids had low content of PFAS with the dominant compound being PFOS (1.59–2.60 ng/g). Based on recent US EPA proposed maximum contaminant levels, hazard indexes for drinking water were exceeded in effluent and downstream surface waters at both WWTPs. The WWTP located in a heavily developed area and downstream from a firefighting training facility, had significantly higher concentrations of most PFAS species at most monitoring sites and was less impacted by sampling period compared to the WWTP located in a moderately developed, pastured area. Findings support the importance of WWTPs and land use practices as contributing to PFAS impact to downstream ecosystems along with potentially increasing strains on downstream drinking water source waters in regions that are surface water dependent.

Groundwater potential zone mapping using AHP and geospatial techniques in the upper Narmada basin, central India

Water scarcity occurs in the agriculturally dominated Upper catchment area of Narmada River, Central India because of the overexploitation of underground water for residential, industrial, and other uses. Delineating the Ground Water Potential Zone (GWPZ) is critical to meeting the area’s water demand. Finding the Upper catchment Narmada River groundwater potential zone is the primary goal of this study. The study uses geographical methodologies based on the Analytical Hierarchical Process (AHP). To create a GWPZ map, ArcGIS 10.4 software compiles eight thematic layers, including elevation, slope, drainage density, geology, rainfall, soil texture, modified normalized difference water index, topographic wetness index, and land use/cover. There are five classifications for land use land cover map: Very low, low, moderate, high, and Very high. Each theme map in this study was given a weight based on its unique attributes and contribution to the GWP capacity. The AHP method, which takes into account each layer’s relative relevance regarding the others, was used to establish the weights. Four groups were created from the resulting groundwater potential map: excellent, good, moderate, and poor. According to the study, 26.05% of the basin was categorized as excellent, 34.59% as good, 23.97% as moderate, and 15.4% as poor groundwater potential.The results of this study further indicate that a sizable section of the Narmada River Basin has well to moderate groundwater potential, pointing to encouraging prospects for the area’s sustainable groundwater use. The study offers crucial insights for planners and policymakers to conscientiously harness groundwater resources, fostering sustainable development across diverse land uses in the fragile zone of the upper catchment of the Narmada, and it serves as a model for simulation in other sensitive river basins. The implications of the study are geared towards enhancing groundwater prospects, revitalizing fragile riverine ecosystems, and achieving the target outlined in Goal 6 of the Sustainable Development Goals (SDGs) of 2030.

Healthy urban blue space design: Exploring the associations of blue space quality with recreational running and cycling using crowdsourced data

Urban blue space offers substantial health benefits by encouraging population physical activity. Despite much evidence on the nature-health nexus, the relationship between blue space and recreational exercises remains under-studied, limiting the realisation of health benefits in blue space design. Using crowdsourced data, including volunteered geographic information and street view image data, this study investigates the associations of blue space quality with recreational running and cycling in Rotterdam, the Netherlands. Results show that recreational exercise levels on street segments vary based on the blue space type and design. Compared to inland canals and rivers, small-scale recreational waterbodies are more conducive to running but not cycling, while both activities tend to cluster around the Nieuwe Maas River. Interestingly, the Water View Index shows a general negative association with both activities after adjusting for the blue space type. Besides the waterbody characteristics, eye-level environmental factors, including higher Green View Index, lower building density, more diverse land use, greater connected street network and fewer traffic elements, are associated with more running and cycling exercises. Results for visual complexity and neighbourhood population composition are mixed depending on the exercise type. These findings are further translated into spatial design patterns for developing exercise-supportive and health-promoting blue spaces.

Soil carbon sequestration potential of different land use systems: evidence from sub-humid southern plains and Aravalli hills of Rajasthan, India.

This study has assessed total soil organic carbon (TOC) fractions, carbon management index (CMI), and carbon sequestration economic value under diverse land use systems (LUSs) of sub-humid Southern plains of Rajasthan, India. The study dealt with six LUSs: barren land (BL), agricultural land (AL), agri-horticulture (AH), horticultural land (HL), grassland (GL), and natural forest land (FL) were selected for the study. FL contained the highest TOC (13.04 ± 0.74gkg-1), particulate organic carbon (POC) (2.2 ± 0.19gkg-1), mineral-associated organic carbon (MAC) (10.84 ± 0.54gkg-1), and CMI (230.36 ± 4.13), whereas BL had the lowest amount of TOC (3.53 ± 0.4gkg-1), POC (0.47 ± 0.06gkg-1), MAC (3.06 ± 0.32gkg-1), and CMI (54.60 ± 4.2). The impact of LUSs on soil carbon lability index (LI) was minimal in all LUSs except FL exhibited statistically insignificant variations in LI. TOC stock showed the highest decline in BL (71.04%), AL (55.29%), AH (44.38%), and HL (25.92%) uses compared with the FL system. Different LUSs result in varying amounts of carbon stocks, representing the relative carbon credit gain. The maximum carbon credit was achieved by FL, which was roughly US $49,303 and 245% higher than BL. These results indicate the reinstatement of BL and AL towards HL and AH systems, and effective recycling of residues could improve the TOC storage in the study region.

Unveiling Anomalies in Terrain Elevation Products from Spaceborne Full-Waveform LiDAR over Forested Areas

Anomalies displaying significant deviations between terrain elevation products acquired from spaceborne full-waveform LiDAR and reference elevations are frequently observed in assessment studies. While the predominant focus is on “normal” data, recognizing anomalies within datasets obtained from the Geoscience Laser Altimeter System (GLAS) and the Global Ecosystem Dynamics Investigation (GEDI) is essential for a comprehensive understanding of widely used spaceborne full-waveform data, which not only facilitates optimal data utilization but also enhances the exploration of potential applications. Nevertheless, our comprehension of anomalies remains limited as they have received scant specific attention. Diverging from prevalent practices of directly eliminating outliers, we conducted a targeted exploration of anomalies in forested areas using both transmitted and return waveforms from the GLAS and the GEDI in conjunction with airborne LiDAR point cloud data. We unveiled that elevation anomalies stem not from the transmitted pulses or product algorithms, but rather from scattering sources. We further observed similarities between the GLAS and the GEDI despite their considerable disparities in sensor parameters, with the waveforms characterized by a low signal-to-noise ratio and a near exponential decay in return energy; specifically, return signals of anomalies originated from clouds rather than the land surface. This discovery underscores the potential of deriving cloud-top height from spaceborne full-waveform LiDAR missions, particularly the GEDI, suggesting promising prospects for applying GEDI data in atmospheric science—an area that has received scant attention thus far. To mitigate the impact of abnormal return waveforms on diverse land surface studies, we strongly recommend incorporating spaceborne LiDAR-offered terrain elevation in data filtering by establishing an elevation-difference threshold against a reference elevation. This is especially vital for studies concerning forest parameters due to potential cloud interference, yet a consensus has not been reached within the community.

Research on Global Off-Road Path Planning Based on Improved A* Algorithm

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments.

Liming acidic soils creates profits, land use options but often more emissions

Context Soil acidity constrains crop production in Australia. The practice of liming can reduce soil acidity but produces greenhouse gas emissions. Aims By examining land use sequences over three decades at a range of locations in Western Australia, this study aims to identify firstly where and when liming might boost farm profits and secondly, what emissions and land use management flexibilities are generated by liming. Methods Bioeconomic simulation modelling is used to identify the gross margins and emissions associated with liming in land use sequences at 14 locations in Western Australia. Three intensities of cropping and three different rotational sequences are considered. The simulations account for price and weather–year variations across a 30-year period of analysis. Key results Liming is profitable at almost all locations and across all rotation sequences examined. Where problematic soil acidity is a feature or is poised to soon become a problem at a location, liming is a profitable ameliorative practice that enables greater diversity in land use. For most situations assessed, liming increases emissions. The exceptions are at locations where liming prevents a switch away from a crop-dominant system, due to soil acidity reducing crop yields, into additional sheep production that increases emissions. Conclusions Liming is profitable in most acidic soil situations and preserves land use flexibility, although additional greenhouse gas emissions are often generated. Implications Liming acidic soils bolsters land use profitability and helps sustain biologically diverse land use sequences, despite often increasing greenhouse gas emissions.

Differences in Urban Vibrancy Enhancement among Different Mixed Land Use Types: Evidence from Shenzhen, China

Mixed land use has the advantages of promoting the economic and intensive utilization of land and improving the efficiency of land use, which can help alleviate the current urban problems and promote the sustainable development of cities. Existing studies have usually used quantitative indicators to reflect complex and diverse mixed land use situations, and the conclusions obtained usually cannot provide a basis for functional selection in mixed land use practices. Therefore, this study took Shenzhen as the study area to explore whether there are differences in the urban vibrancy enhancement among different mixed land use types. First, the block-scale mixed land use dataset of the study area was constructed. Second, the spatial distribution characteristics of the main functional types and urban vibrancy in the study area were explored. Finally, the impact of mixed land use types on urban vibrancy was explored by using a multiple linear regression model and setting land use type as the dummy variable. The results show that the number of mixed-function blocks in Shenzhen is relatively small, and the mixed land use degree still needs to be improved. Among the 12 main land use types in the study area, those containing industrial land are usually clustered in the northern industrial area of Shenzhen, those containing public or commercial service land are usually clustered in the city center, and those containing residential land are widely distributed in the study area. From the perspective of urban vibrancy, there is a phenomenon of “jobs–housing mismatch” in Shenzhen, as well as a problem of low urban vibrancy in the peripheral areas of the city. In addition, the urban vibrancy intensity of mixed land use types including residential or commercial land is higher, such as “administration+residential”, “residential+commercial”, “industrial+residential+commercial”, and “administration+residential+commercial” land, which includes residential or commercial land, is stronger, while. However, the urban vibrancy stability of mixed land use types including industrial land is higher, such as “industrial+residential” and “industrial+administration” land. The results of this study can provide a basis for future mixed land use practices in terms of land use type selection. For the urban central areas and subcenters in urban peripheral areas, mixed land use types such as “administration+residential”, “residential+commercial”, and “administration+residential+commercial” can be selected to enhance the urban vibrancy stability of the area. For industrial parks in urban peripheral areas, mixed land use types such as “industrial+residential”, “industrial+commercial”, “industrial+administration+residential”, and “administration+residential+commercial” can be selected to enhance the urban vibrancy intensity of the area.

Predictive Understanding of Stream Salinization in a Developed Watershed Using Machine Learning.

Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed. We compare the predictive performance of space- and time-unaware Random Forest models and space- and time-aware Recurrent Graph Convolution Neural Network models (KGE: 0.67 and 0.64, respectively) and use explainable artificial intelligence methods to interpret model predictions and understand salinization drivers. These models are applied to the Delaware River Basin, a developed watershed with diverse land uses that experiences anthropogenic salinization from winter deicer applications. These models capture seasonality for the winter first flush of deicers, and the streams with elevated predictions correspond well with indicators of deicer application. This result suggests that these models can be used to identify potential salinity-impaired streams for winter best management practices. Daily salinity predictions are driven primarily by land cover (urbanization) trends that may represent anthropogenic salinization processes and weather at time scales up to three months. Such modeling approaches are likely transferable to other watersheds and can be applied to further understand salinization risks and drivers.

The dung beetles of Venezuela (Coleoptera: Scarabaeidae: Scarabaeinae): catalogue and updated distribution

Venezuela’s diverse land ecosystems are grouped into four major regions (coast-islands, low plains, hills and mountains), ranging from sea level up to 4978 m. The Scarabaeinae (Scarabaeidae, Coleoptera) currently encompass 278 genera and 6837 species worldwide, but are poorly inventoried in Venezuela. We reviewed the literature and the material housed at the entomological collection of the Universidade Federal de Mato Grosso (CEMT), Cuiabá, Brazil, and found 32 genera and 149 species of dung beetles as certainly present in Venezuela. Twenty-four of these species are, as far as current knowledge goes, endemic to the country, while another 34 are restricted to Venezuela and the neighbouring countries of Colombia, Brazil, Trinidad and Tobago, and Guyana. Additionally, 36 species are deemed potential inhabitants of the country, whilst 14 others previously recorded in the literature as part of the Venezuelan fauna are here concluded not to be actually present there. Complete literature is listed for each genus and species, and information on type material, material examined, worldwide distribution, and Venezuelan records is also presented.

Field-scale variability and dynamics of soil moisture in Southwestern Nigeria

This research leverages field-based sensors and Geographic Information Systems (GIS) to elucidate soil moisture dynamics across diverse land uses in Southwestern Nigeria, including Cocoa, Cassava, Oil Palm, and Riparian ecosystems. The study synthesizes soil moisture data and geospatial coordinates from strategically selected land covers with satellite-derived soil moisture records from NASA's Prediction of Worldwide Energy Resources (POWER) and the US Geological Survey. Through a robust analytical framework combining cross-correlation functions, wavelet analysis, and inverse distance weighting interpolation, we revealed distinct soil moisture patterns ranging from 0 to 47.9% in arable farms, 0 to 46.8% in built-up areas, 0 to 49.9% in cocoa farms, 0.2 to 49.7% in oil palm farms, and 5.6 to 94.1% in riparian vegetation. The coefficient of determination values between 0.7 and 0.97 (p ≤ 0.05) underscore the significant influence of periodic land use on soil moisture during the study period. A comparative analysis of ground-based and satellite data revealed a potential overestimation of minimum soil moisture values by 66% and an underestimation of maximum values by 4%. This investigation provides critical insights into the interplay between hydrological variables and agro-meteorological factors, informing the management and assessment of agricultural resources.

A “Region-Specific Model Adaptation (RSMA)”-Based Training Data Method for Large-Scale Land Cover Mapping

An accurate and historical land cover monitoring dataset for Alaska could provide fundamental information for a range of studies, such as conservation habitats, biogeochemical cycles, and climate systems, in this distinctive region. This research addresses challenges associated with the extraction of training data for timely and accurate land cover classifications in Alaska over longer time periods (e.g., greater than 10 years). Specifically, we designed the “Region-Specific Model Adaptation (RSMA)” method for training data. The method integrates land cover information from the National Land Cover Database (NLCD), LANDFIRE’s Existing Vegetation Type (EVT), and the National Wetlands Inventory (NWI) and machine learning techniques to generate robust training samples based on the Anderson Level II classification legend. The assumption of the method is that spectral signatures vary across regions because of diverse land surface compositions; however, despite these variations, there are consistent, collective land cover characteristics that span the entire region. Building upon this assumption, this research utilized the classification power of deep learning algorithms and the generalization ability of RSMA to construct a model for the RSMA method. Additionally, we interpreted existing vegetation plot information for land cover labels as validation data to reduce inconsistency in the human interpretation. Our validation results indicate that the RSMA method improved the quality of the training data derived solely from the NLCD by approximately 30% for the overall accuracy. The validation assessment also demonstrates that the RSMA method can generate reliable training data on large scales in regions that lack sufficient reliable data.

Carbon stocks under different land cover types in the Hallaydeghe wildlife reserve, northeastern Ethiopia

The Hallaydeghe Wildlife Reserve, with its semi-arid to desert climate, features diverse land cover classifications. This study examined land cover changes and total carbon stocks from 1999 to 2019, focusing on forest, woodland, grassland, and bushland vegetation types. Land cover classes were created using 1999, 2003, and 2019 Landsat images. ArcGIS 10.4.1 and ERDAS IMAGINE were used for map creation, while future land use changes were simulated using the CA-Markov model. Carbon stock assessment involved five pools: soil, dead wood, aboveground, belowground, and litter carbon. Soil samples were collected at three depths, and data on woody plant species, soils, and litter were gathered through random systematic sampling. From 1999 to 2019, woodland grew by 57.94%, while grassland and bushland decreased by 6.86% and 10.51%, respectively. Projections for 2035 indicate a 40.68% increase in bushland, decreases in forest and grassland, and the emergence of bareland. Total carbon stocks varied among vegetation types: forest (35.94±6.63 tC ha-1), grassland (22.55±3.35 tC ha-1), and bushland (23.65±3.25 tC ha-1). The soil organic carbon pool contributed the most across all land cover categories. The shift from grassland to forest may impact the Grevy’s zebra’s habitat. Effective land use management is crucial for preserving carbon stores and ecosystem health. Implementing climate-smart policies, including community-based conservation and sustainable management practices, is essential to mitigate the impacts of land cover changes on wildlife and carbon stocks in the reserve.

Nonlinear associations between design, land-use features, and active travel

The nonlinearity correlations of design and land-use features with active travel (AT) are yet to be systematically identified by recent studies. This study aims to apply the extreme gradient boosting approach to examine the nonlinear relationships between design and land-use built environment (BE) features and AT. Based on a comprehensive travel survey in Wuhan’s main agglomeration with various land use and urban functions, we collected 2073 valid samples to obtain residents’ daily travel patterns and socio-demographic attributes. We found that all BE features have nonlinear relationships with AT. The collective relative importance of design features has greater predictive power for AT than land-use features. The most effective range of high contributions of street connectivity and street greenery was 21–34/km2 and 0.22–0.34, respectively. Planners should improve the street-level features and build dense, diverse land use and high street connectivity urban environments to promote AT activity.

Conflicts in Land Use and Management

As land is limited, conflicts between land uses, and, consequently, conflicts between land users about land use inevitably arise. The objective of this paper is to provide a broad, cross-sectional overview of land use conflicts as perceived by different actors and to explore the actors’ experiences with these conflicts. We conducted 45 face-to-face interviews with key land use actors in the Republic of North Macedonia. Then we applied a qualitative text analysis or identification of keywords to identify the most relevant conflicts across all land use sectors (agriculture, forestry, nature protection, etc.) and their impacts on local actors’ daily experiences. The results show that actors are aware of many diverse land use conflicts, most frequently regarding land uses for environmental/species conservation, forestry, and agriculture. Thus, land use conflicts play an important and mostly negative role in the experiences of land use actors. These findings fill current gaps in the literature on land use conflicts regarding the types of conflicts of which actors are aware and the consequences of these conflicts. The results also underline the relevance of addressing conflicts in land use planning and governance, the need for appropriate conflict management, and the necessity of providing local actors with sufficient resources to deal with land use conflicts.