Diverse Land Research Articles

Relationships among soil moisture at various depths under diverse climate, land cover and soil texture

Soil moisture is an important component of the hydrological cycle and a key mediator between land surface and atmospheric interactions. Although substantial progress has been made in remote sensing of soil moisture at different spatial scales, the shallow penetration depth of remote sensors greatly limits their utility for applications in meteorological modelling and hydrological studies where the critical variable of interest is the root-zone soil moisture content. Therefore, this study assesses the relationship between soil moisture at the surface (10 cm) and in lower soil layers (20, 40, 60, 80, 100, and 120 cm) under varying climates, soils, and crop types. Cross-correlation analysis is applied to daily in-situ soil moisture measurements from 4712 locations in agricultural lands across the contiguous United States. Our analysis demonstrates that zero-day lag always produced the highest correlation between 10 cm soil moisture and soil moisture in the lower layers. In addition, a positive and strong relationship between 10 and 20 cm soil moisture (r = 0.84) was observed, while the relationships between 10 and 40 cm soil moisture were moderate (r = 0.52). The decline in cross-correlation continued to the deeper soil layers, which indicated that, on a daily timescale, the surface soil moisture gradually becomes decoupled with soil moisture at greater depths. Therefore, our research suggests that the estimation of soil moisture in the soil layers up to 40 cm based on surface soil moisture is most promising. However, the influence of climate, crop type, and soil texture on the strength of relationships between surface and lower layers makes the prediction difficult. The comparatively weak relationship between precipitation and soil moisture (0.09–0.32), as well as the relationship between reference evapotranspiration (ETo) and soil moisture (−0.19–0.18), in this study can be attributed to scale mismatching from different data sources.

Water resource management and land tenure on allotted and tribal lands

Historical land tenure policies often prevent tribal members from accessing and managing resources on lands to which they hold allotment titles. Tribal trust landowners continue to experience challenges in applying long-contested water resource rights for beneficial use on their land. This study examines the relationship between land tenure and water resource management in tribal lands. Conflicts arising from diverse land ownership types and the implications of water rights management of tribal trust and non-tribal assets are analyzed. Watershed analyses of landownership types are performed to provide a comprehensive picture of the extent of tribal control and management of water resources. These analyses provide an estimate of land within the reservation boundary not controlled by the tribe, thus highlighting the restrictions that such land presents for tribal resource management. To further detail the potential impact of these restrictions, the land tenure of the tribal land base is outlined, including the percentage of tribal trust, fee, or non-trust land types, with the goal of specifically addressing the potential limitations on water resource usage for allottee landowners on Crow tribal lands. Using highly fractionated lands as a proxy for idle lands, 33% of tribal trust lands were estimated to be precluded from water resource management. The convergence of large-fee land and fractionated idle land is identified as alienated land. The primary waterway basins consist of over 60% fee and idle land as alienated land. Fee and non-trust lands are subject to state control over water rights, and the reserved rights associated with idle allotted land are inaccessible to the tribal management authority. This leaves allottee landowners unable to use and manage their land and related water resources. The key to effectively managing tribal water resources is to ensure that all landowners have the means to participate in resource management.

Variability of hydrogen sources within nominally anhydrous soil across a desert-oasis ecoregion, Northwest China

The accuracy of soil moisture measurements by cosmic-ray neutron sensing (CRNS) is significantly influenced by hydrogen sources within nominally anhydrous soil (NAS), particularly in arid regions. However, the variability of hydrogen in NAS, specifically soil lattice water (H2OL) and water equivalent of soil organic matter (H2OSOM), is seldom investigated in arid heterogeneous landscapes. This study assessed H2OL and H2OSOM amounts across four land covers (forest, cropland, gravel desert, and sandy desert) and three soil depths (0–10 cm, 10–20 cm, and 20–30 cm), and employed a support vector machine (SVM) to estimate H2OL amounts. Results indicated that H2OL amounts varied by land cover: 7.48 % in cropland, 6.06 % in forest, 5.78 % in gravel desert, and 3.17 % in sandy desert·H2OSOM amounts were 1.14 % in cropland, 0.71 % in forest, 0.65 % in sandy desert, and 0.27 % in gravel desert. The higher H2OL and H2OSOM in cropland and forest soils can be attributed to greater silt and clay fractions and organic matter content. Both H2OL and H2OSOM amounts decreased with soil depth. The SVM model accurately estimated H2OL amounts (R2 = 0.74) with a low root mean squared error (1.16 %), identifying soil organic matter as the key variable. Understanding the variability of H2OL and H2OSOM is crucial for accurate CRNS-based soil moisture estimation across diverse land covers, thereby enhancing the applications of CRNS in dry conditions.

Achieving urban vibrancy through effective city planning: A spatial and temporal perspective

Achieving urban vibrancy is a critical goal for urban planning and policy-making, and the city's physical plan plays an important role in shaping and supporting urban vibrancy. However, the effectiveness of urban planning strategies in optimizing resource allocation and fostering a vibrant city remains unclear. This study aims to contribute to this understanding by exploring the spatial and temporal effects underlying the association between urban vibrancy and urban forms. Using mobile-phone location-request data along with urban nighttime light data as a proxy for urban vibrancy in Shenzhen, China, we conducted a geographically and temporally weighted regression (GTWR) analysis to examine the relationship between land use, street configuration, and urban vibrancy. The results showed that a diverse land use mix and the proportions of land allotted for sports and cultural usage, parks, and greenspace generally have a positive impact on urban vibrancy. We noted the facilitating role of degree centrality and closeness centrality on urban vibrancy, compared to the negative impact of betweenness centrality. Our analysis of local spatial variations in the influence of road networks suggests a potential connectivity threshold for attracting people to specific areas. Finally, we identified a divergence between weekdays and weekends in terms of temporal variations.

Implementation of Deep Learning Technique for Accurate Land Cover Classification

Detecting and modelling Land Cover are crucial for managing natural resources, assessing environmental impacts, and preserving ecological connectivity. Convolutional Neural Networks (CNNs) have become pivotal in advancing remote sensing applications, particularly in land cover classification. Therefore, this study introduces a CNN classifier for the prediction land cover efficiently. The pre processing is carried out based on Principle Component Analysis (PCA), which reduces the dimensionality of the input data while retaining essential information. This step aims to enhance computational efficiency and improve the CNN’s ability to extract relevant features during training. Following pre processing, a comprehensive training phase involves optimizing the CNN architecture using a labelled dataset. This process allows the network to learn intricate spatial patterns and spectral characteristics crucial for accurate land cover classification. Evaluation of the trained model occurs in the testing phase, where performance metrics such as accuracy, precision, recall, and F1 score are computed using an independent validation dataset. Results obtained from the python software demonstrate that the CNN PCA framework’s capability to achieve high classification accuracy across diverse land cover types.

Spatial Analysis of Forest Fire Potential in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi

This study aims to analyze the potential for forest fires in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi, by utilizing geographic information system data and mapping areas based on fire potential. The research method involved field surveys and the analysis of primary and secondary data. Forest fire hazards maps were obtained from the analysis of Landsat satellite images that were classified to provide information on land cover, slope, distance from roads, and categorized based on predetermined weighting categories. The overlay process was conducted using Geographic Information System software. The main findings indicate that the area has diverse land cover, with reed grassland savanna having the highest fire hazards covering an area of 1203.78 ha. Very steep slopes cover 339.94 ha, and the distance from roads and settlements to the highest hazards point is 140 meters. These variables also influence the level of hazards. Implications of this study include the placement of forest fire prevention patrols, the use of weather modification technology, and post-fire support to restore ecosystems.

Soil organic carbon pool in diverse land utilization patterns in North-East India: an implication for carbon sequestration

Soil organic carbon pool in diverse land utilization patterns in North-East India: an implication for carbon sequestration

Variation of trace metal minerals among different soil orders under different land use systems falling in Malwa region of Punjab in North-western India

BackgroundTrace metal deficiency has become a major constraint on the productivity and sustainability of soils. Agriculture becomes more complex, advanced and intensive farming systems develop, however, the deficiency of trace metal are more frequent and extensive in different systems. MethodsThis study investigated the variation of trace metal minerals in soil profiles of diverse land use systems (LUSs), i.e. agriculture, horticulture and forestry, falling under three dominant soil orders (Entisol, Inceptisol and Aridisol) in Malwa region of Punjab. Through comprehensive analyses, we explore the intricate relationships between soil characteristics, land use practices, and the distribution of key trace metals—namely zinc (Zn), copper (Cu), iron (Fe) and manganese (Mn). ResultsThe mean values of DTPA-extractable trace metal from soil profiles of different land uses varied from 1.75 to 2.09, 1.64–2.32, 1.18–1.69 mg kg−1 for Zn; 0.59–1.02, 0.59–0.99, 0.64–0.92 mg kg−1 for Cu; 12.12–16.49, 9.84–16.05, 7.94–11.56 mg kg−1 for Fe; 7.82–9.99, 4.66–9.39, 6.51–7.46 mg kg−1for Mn in soil orders Entisol, Inceptisol and Aridisol, respectively. The concentration of trace metals in different soil orders follows the pattern of ntisol>Inceptisol>Aridisol. In contrast, there is a considerable amount of variation and no discernible pattern in the distribution of trace metals under various LUSs in each soil order. Several LUSs were examined, and agriculture land use system (ALUS) had the greatest Fe and Mn content, while the horticulture land use system (HLUS) had the highest Zn and Cu content. The Irrespective of soil orders and LUSs, the concentration of trace metals decreased as soil depth increased. Diverse LUSs and management practices have a substantial impact on the physico-chemical characteristics of soils, which in turn influence the availability of trace metals. According to the Pearson correlation studies, trace metals were positively associated with soil OC and negatively associated with soil pH, electrical conductivity (EC), and calcium carbonate (CaCO3) and there was also a positive correlation between DTPA-Zn, Cu, Fe, and Mn in the investigated soils. The results of the principal component analysis (PCA) revealed that soil CaCO3 and organic carbon (OC) content were the most variable soil parameters influencing crop trace metal availability in different soil orders and land use systems. ConclusionThe findings contribute to a nuanced understanding of the dynamic interplay between soil properties and land use, providing valuable insights for sustainable agricultural practices and environmental management in the region.

Soil Phosphorus Partition and Transformations Under Diverse Land Uses

ABSTRACT This study investigates soil phosphorus (P) partitioning and transformations through chemical fractionation and modeling across various land uses to elucidate their impact on P dynamics and the potential for eutrophication. Geo-referenced soils (0–30 cm at various depth intervals) were collected from diverse land uses, including corn, soybean, wheat stubble, modified relay intercropping, corn-soybean with rye under conventional tillage and no-till systems, and sheep pastures, alongside an adjacent forest (control). Processed soils were sequentially extracted to quantify soluble reactive (SRP), exchangeable (EP), Ca and Mg bound (CaMgP), Fe and Al bound (FeAlP), particulate organic (POP), residual (RP), and total P pools. Results showed significant effects of land use and soil depth on extracted P pools. Notably, corn-soybean with rye exhibited a higher SRP accumulation (2.8 mg/kg) compared to the forest. The labile (SRP + EP), moderately labile (FeAlP + CaMgP + POP), and non-labile P (RP) pools accounted for 1 to 2, 23 to 29, and 65 to 76% of total P, respectively. The structural equation model elucidated soil P transformation, indicating the contribution of labile, moderately labile, and non-labile P pools to SRP accumulation in various land uses. Our findings underscore the critical link between land use, soil P dynamics, and eutrophication risk, offering valuable insights for developing agricultural management practices aimed at mitigating edge-of-field P loss and protecting water quality against eutrophication.

Integrated occurrence of contaminants of emerging concern, including microplastics, in urban and agricultural watersheds in the State of São Paulo, Brazil

Contaminants of emerging concern (CECs), including microplastics, have been the focus of many studies due to their environmental impact, affecting biota and human health. The diverse land uses and occupation of watersheds are important parameters driving the occurrence of these contaminants. CECs such as pesticides, drugs, hormones, and industrial-origin substances were analyzed in urban/industrial (Atibaia) and agricultural (Preto/Turvo) watersheds located in São Paulo state, Brazil. A total of 24 CECs were investigated, and, as a result, only 5 (caffeine, carbendazim, atrazine, ametrine and 2-hydroxytrazine) were responsible for 81.73 % of the statistical difference between watersheds contamination profile. The Atibaia watershed presented considerable concentrations of caffeine (ranging from 75 to 2025 ng L−1), while carbendazim (44 to 1144 ng L−1) and atrazine (3 to 266 ng L−1) presented highest levels in Preto/Turvo watershed. In all sampling points, the cumulative potential aquatic life risk assessed by the NORMAN database indicates some level of environmental concern associated to pesticides and caffeine (risk quotient >1). Microplastics had been analyzed in both watersheds, being the white/transparent fragments in size between 100 and 250 μm the most detected in this study. The estimated abundance in the Atibaia watershed ranged from 349 to 2898 items m−3 presenting some influence of pluviosity, while in Rio Preto/Turvo ranged from 169 to 6370 items m−3, being more abundant in the dam area without a clear influence of pluviosity. In both basins, polyethylene and polypropylene were the most detected polymers, probably due to the intense use of single-use plastics in urban areas. Possibly, due to the distinct physic-chemical properties of microplastics and organic CECs, no correlations were observed between their occurrence, which makes us conclude that they have different transport mechanism, behavior, and fate in the environment.

Insights to the water balance of a Boreal watershed using a SWAT model

The hydrological characteristics of a watershed play a crucial role in shaping ecosystems within the Boreal zone and have a significant impact on regional environments. Knowing these characteristics, such as the distinctive topography, vegetation, soil composition, and climatic conditions in the Canadian Boreal ecozone, is essential for implementing sustainable water management. This study focuses on assessing the hydrological dynamics of the Upper Humber River Watershed (UHRW) in western Newfoundland, Canada, using the Soil and Water Assessment Tool (SWAT) model. The UHRW includes sub-basins and hydrological response units (HRUs), with diverse land uses, soil types, and slope characteristics. Key parameters influencing streamflow simulation were identified through sensitivity analysis, including the runoff curve number, the effective hydraulic conductivity, the temperature lapse rate, the soil evaporation compensation factor, and the available water capacity of the soil layer. The SWAT model, using data from the Reidville hydrometric station, shows favorable performance metrics, with R2 values of 0.79 and 0.83 during the calibration and evaluation periods, respectively. The model effectively captures seasonal and monthly flow patterns, displaying right-skewed distributions and seasonal variations. The analyzed hydrological parameters, such as precipitation, evaporation, transpiration, surface runoff, and groundwater flow, reveal their significant contributions to the water balance. The flow duration curve analysis indicates the model’s capability to estimate peak and low flows, with slight under-prediction during the recession phase. Seasonal analysis further supports the model’s performance, with positive Nash-Sutcliffe Efficiency (NSE) values ranging from 0.65 to 0.91. The study concludes that the SWAT model is suitable for simulating the hydrological processes in the studied watershed providing valuable insights for sustainable water resource management and decision-making in the UHRW. The results can be useful for other Boreal ecozone watersheds.

Exploring the relationship between land use change patterns and variation in environmental factors within urban agglomeration

Sustainable Development Goal (SDG) 11 aims to promote a more livable urban environment for humanity. However, the rapid global urbanization process has exerted significant pressure on urban environment, posing challenges for many cities to achieve this target. The urbanization process within urban agglomeration is accompanied by rapid and diverse land use change. Exploring the relationship between land use change and environmental factors is crucial for achieving sustainable urban development. To address this issue, this study investigates the interactive relationship between pixel-scale land use change and environmental indicators (CO2, Land Surface Temperature, and PM2.5) in China's three major urban agglomerations from 2000 to 2020. This study aims to analyze major land use change patterns, explore intrinsic connections among environmental indicators, and assess the impact of land use change on these indicators. The findings show that cropland, forestland, and built-up land are the major types of land change in all three urban agglomerations. Regions characterized by high population density, heavy industry, and intense transportation exhibit more pollutant emissions. Rapid urban construction has resulted in different types of environmental degradation issues. This study provides a comprehensive examination of the relationship between urbanization and environmental issues.

A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images

Jakarta holds the distinction of being the largest capital city among ASEAN countries and ranks as the second-largest metropolitan area in the world, following Tokyo. Despite numerous studies examining the diverse urban land use and land cover patterns within the city, the recent state of urban green spaces has not been adequately assessed and mapped precisely. Most previous studies have primarily focused on urban built-up areas and manmade structures. In this research, the first-ever detailed map of Jakarta’s urban green spaces as of 2023 was generated, with a resolution of three meters. This study employed a combination of supervised classification and evaluated two machine learning algorithms to achieve the highest accuracy possible. To achieve this, various satellite images were utilized, including VV and VH polarizations from Sentinel-1, multiple bands from Sentinel-2, and eight bands from Planet. The Planet data were subsequently transformed into the Red-Edge Triangulated Vegetation Index and Red-Edge Triangulated Wetness Index. The data training and testing samples for urban green spaces were obtained using the Street View images available on Google Maps. The results revealed that using the Random Forest classifier algorithm and only eight bands of Planet images achieved an accuracy rate of 84.9%, while a combination of multiple images achieved an impressive 95.9% accuracy rate. Jakarta’s urban areas cover approximately 33.2% of green spaces. This study provides unprecedented insights into the type, size, and spatial distribution of Jakarta’s urban green spaces, enabling urban residents and stakeholders to explore and promote healthier living and better manage these green areas. Additionally, a previously unexplored concept, Jakarta’s urban green belt, is introduced.

Application of Sentinel-2A Images for Land Cover Classification Using NDVI in Jember Regency

The advancement of remote sensing technology has led to the development of sophisticated image processing methods that yield highly accurate land cover classification information, minimizing misinterpretations. The Normalized Difference Vegetation Index (NDVI) is a widely utilized method in remote sensing for measuring green vegetation. A significant portion of the Jember Regency area is covered by vegetation. This study aimed to identify various land cover types in the Jember Regency area, quantify the area for each classification, and establish the NDVI value ranges for each type of cover. Sentinel-2 was employed as the primary data source, and the NDVI method was utilized for land cover classification in the Jember Regency. The region exhibited diverse land cover types. Data from Sentinel-2A captured in June and October 2019 were chosen due to their accessibility, open-source nature, and adequate spectral, spatial, and temporal resolution. The classification in this study encompassed five classes: water bodies, settlements, dry fields, irrigated paddy fields, and forests. Error analysis was conducted using a confusion matrix with the Overall and Kappa algorithms. The accuracy results for June indicated a Kappa Accuracy of 37.7% and Overall Accuracy of 54.5%. In October, the Kappa Accuracy increased to 39.9%, and the Overall Accuracy reached 56.5%. In conclusion, the NDVI method did not meet the criteria for accurately interpreting land cover classification.

Design Strategies to Improve Metro Transit Station Walking Environments: Five Stations in Chongqing, China

While transit-oriented development (TOD) has been widely adopted in urban design alongside the expansion of urban metro transit, the creation of pedestrian-friendly environments has often been overlooked during implementation. This has resulted in a lower walking advantage around metro transit stations. To address this issue and encourage walking and public transport use in metro transit station areas, this study undertook a quantitative comparative analysis of the pedestrian environment in five Chongqing metro transit station areas. The analysis focused on three key dimensions: “comprehensive evaluation”, “basic scale”, and “structural quality”. The comprehensive evaluation considered factors such as the pedestrian catchment area ratio, POI kernel density distribution, and crowd agglomeration. The basic scale dimension comprised floor area ratio, building density, pedestrian road density, and the quantity of station entrances and exits. Finally, structural quality factors included land use type mixing degree, POI function mixing degree, intersection connectivity, median street length, pedestrian route directness, and green view index. Based on these analyses, this study proposes a series of pedestrian environment design strategies including land use and transportation. The strategies for land use advocate for “developing compact and diverse land use”, “strengthening attraction of station center”, “positioning large projects on the edge”, “restricting private transportation capabilities”. The strategies for transportation consist of “increasing pedestrian road density”, “traffic calming organization”, “subdivision of road types”, and “three-dimensional pedestrian traffic system”. These strategies aim to create a more humanized and environmentally friendly pedestrian environment, proactively rise to the challenge of climate change, thereby cultivating sustainable urban development.

Examining Soil Microplastics: Prevalence and Consequences Across Varied Land Use Contexts

In an extensive exploration of microplastics within soil environments, our study aims to investigate the presence, spread, and ecological impact of microplastics in soil, focusing on Makassar City, Indonesia. Using a Sinher binocular digital microscope, we visually examined soil samples in Petri dishes, measuring microplastic sizes with Image-J software. Fourier-transform infrared (FTIR) spectroscopy was also employed for additional identification and analysis of polymer compositions. Our research uncovered a widespread presence of microplastics across diverse soil types and land uses, including residential, fishpond, agricultural, landfill, coastal, and bareland areas. The concentration of these microplastics was found to be between 16.6 to 21.9 particles/gram, showing consistency across most land uses, with some variations in coastal areas. We noted a significant variety in microplastic forms, predominantly fragments and films, across the different land uses. A wide range of colors was observed, including blue, green, red, and transparent. Polyethylene (PE) and polypropylene (PP) were identified as the predominant polymers. Our study highlights the non-uniform distribution of microplastics in soil, suggesting potential significant impacts on soil organisms and the wider ecosystem. These findings underscore the critical need for more comprehensive research on the ecological implications of microplastics in soil environments. Doi: 10.28991/CEJ-2024-010-04-017 Full Text: PDF

Dynamic land cover evapotranspiration model algorithm: DyLEMa

This study presents the “Dynamic Land Cover Evapotranspiration Model Algorithm: DyLEMa” for continuous spatiotemporal evapotranspiration (ET) estimates across diverse land uses. DyLEMa employs a coupled Random Forest model with a novel dynamic recalibrating strategy to improve pre-optimized seasonal hyperparameters following satellite acquisition alongside land cover classes. An analysis of feature importance indicated the significant variability in ET processes across different land cover classes and seasons. Hence, DyLEMa was applied to 20 years of daily 30x30 m pixel resolution Landsat-derived ET data in Illinois to address spatial and temporal discontinuities due to cloud contamination and sensor failures. DyLEMa performance was evaluated on Eddy Covariance measurements to find out that DyLEMa predictions reduced the average PBIAS error from + 31 % to −7% compared to existing US Geological Survey ET products. Spatially, DyLEMa underscores the value of a land cover-aware approach in ET estimation under varied cloud cover rates and their ability to preserve landscape features. However, the performance of DyLEMa was affected by the quality of land cover classification, suggesting the need for a refined region-specific land cover classification. DyLEMa’s flexibility and performance suggest its applicability to other regions and satellite datasets, offering a promising reduction in uncertainty of ET estimates with impacts on environmental and water resources assessments on regional scales.

SOC and TN fluctuations determine the variations in microbial enzymatic activities under diverse land use types in the Central Himalaya, India

Soil microbial enzymatic activities, often used as indicators of soil health and fertility, are vulnerable to changes in climate and anthropogenic activities. This study analyses the responses of specific soil enzymatic activities– dehydrogenase (DHA), acid phosphatase (APH), β-glucosidase (BG) and urease (URE)– in Nitosols/Luvisols of the subtropical zone of the Central Himalaya, India. We investigated how these enzymatic activities vary with different land uses: arable land (AL), kitchen garden (KG) and peach orchard (PO), across four villages– Niglat, Kainchidham, Majhera and Syalikhet. Considering associated management practices, we quantified cumulative average soil enzymatic activities for the rabi season (2017–2018). In general, catalytic activity of the selected enzymes was highest in Syalikhet and lowest in Majhera. However, for land use types, enzymatic activities were lower in AL and KG (75.56–90.17% and 39.41–61.72%, respectively) compared to PO values. MANOVA analysis evaluated significant spatial differences (P < 0.001) in enzymatic activities. Soil enzymatic mean values were highest in the surface layer (0–10 cm) and decreased gradually with increasing depth. Parameters such as soil moisture content (SMC) and pH, along with vegetation, management practices, and nutrients such as SOC, TN, K, AP, and N forms (NH4+–N and NO3––N) showed significant and strong correlations with all the soil enzyme activities (r = 0.40–0.97; P < 0.05). TN, SOC, K, SMC, pH, AP, NH4+- N, and clay content were key determining factors of soil enzymatic activities, explaining approximately 59% of the variation. These results suggest that using soil microbial enzymatic activities as soil quality indicators is a feasible approach to assess the impacts of vegetation, management practices, and soil profile depths in soils under different land use types/systems in selected impoverished villages of the fragile Central Himalaya, India.

Coffee pollination and pest control are affected by edge diversity at local scales but multiscalar approaches and disservices can not be ignored

ContextManaging landscapes to increase multiple services provision in search of higher agricultural yield can be an alternative to agricultural intensification. Nonetheless, to properly guide management, we need to better understand how landscape structure affects multiple services at different scales.ObjectivesFocused on synergies and trade-offs in real-world landscapes, we investigated how and at which scale landscape features related to the supply, demand and flow of services act as a common driver of pollination and pest control in coffee plantations.MethodsConsidering landscapes in an important coffee-producing region in Brazil, we tested the effects of forest and coffee cover, distance to forest, forest-coffee edge density and coffee edge diversity at multiple scales on pollination and pest control by birds, bats and ants.ResultsCoffee edge diversity (number of land uses in contact with coffee) was an important driver of pollination and pest control, being consistently relevant at local scales (up to 300 m). However, services were also affected by other landscape features and the ‘scale of effect’ of these relationships varied. Additionally, results show the complex nature of pest control once the direction of effect revealed services and disservices.ConclusionsBesides reinforcing the importance of known landscape effects, this study adds to previous studies by showcasing the relevance of diverse land uses around coffee crops as a common driver of pollination and pest control provision by different species. Moreover, we highlight how understanding the combined local and landscape effects may aid in offsetting disservices and tackling the variety of ‘scales of effect’ found.

Penilaian Kerentanan Pesisir Berdasarkan Parameter Fisik di Pantai Utara Kabupaten Bekasi

Northern part of Bekasi Regency (Pantura Bekasi) has diverse land: a port area, fisheries, marine tourism, agriculture, residential, industrial and government. Various types of development are occured to support the community welfare. Developments occur without considering sustainability will result in a decline of environmental conditions, and each region will have its own ability to anticipate the impacts of changes that occurs. This research studied coastal vulnerability in the Pantura Bekasi and the relationship between their coastal physical parameters and the vulnerability. Field observations were also conducted as field validation and perceive current conditions. The results show that vulnerability is very high in Fishery port (PPI) Muara Jaya port zone (Mekar Coast), and low vulnerability occurred in Taruma Jaya port zone (Taruma Jaya Coast). Two paramaters are different in these two locations, they are coastline change and geomorphological. The coastline change at Mekar Coast is abrasion, and Taruma Jaya Coast is accretion. The geomorphological at Mekar Coast are a muddy beach and delta, while Taruma Jaya Coast is a swampy beach. Field conditions show that Mekar Coast has low mangrove density, while Taruma Jaya Coast has high mangrove density. These results hopefully can be used as policy consideration for the local government in optimizing coastal management planning, where the spatial plan for Pantai Mekar Beach is designated as a conservation area and demersal fisheries, while on Pantai Taruma Jaya as a public use area such as for Gas and Steam Power Plant (PLTGU) Muara Tawar, PPI Paljaya and port zone.