Digital Map Research Articles

МЕТОДИКА РОЗРАХУНКУ ЗОН ПОКРИТТЯ ЦИФРОВОГО ТЕЛЕВІЗІЙНОГО МОВЛЕННЯ

The introduction of DVB-T2 digital television broadcasting in the VHF waveband is currently underway, and test trials have already been conducted in some regions of Ukraine. To assess the effectiveness of the projects, it is of particular interest to compare the radio coverage areas of digital TV transmitters operating in the VHF and UHF wave bands. This requires extensive calculations for specific locations. For comparison, the city of Kyiv was chosen, where there is one multiplex MX-7 (8 TV channels) in the VHF band, and the following four multiplexes are represented in the UHF band: MX 1 (26 TV channels) and MX 3 (49 TV channels), which operate at the lowest and highest frequencies of the band. In this article, to predict the coverage areas of digital television broadcasting transmitters, the justification of the selected Longley-Rice radio wave propagation model is provided, which allows calculating the radio signal attenuation compared to free space propagation using terrain profiles (digital elevation maps) and the electrical characteristics of the Earth's surface, Taking into account the main mechanisms of radio wave propagation (interference on the line of sight, diffraction on irregular terrain, tropospheric scattering), it is more accurate and its results are closer to actual measurements. The calculations will be carried out using Radio Mobile software based on the Longley-Rice Irregular Terrain Model (ITM) and using Terrain Mission SRTM satellite maps with the possibility of further adding map data and roads from various sources. Based on the ITU R Recommendations on the design of DVB-T2 digital television broadcasting networks, the minimum required field strength to ensure reception with a given quality was calculated and the initial data for predicting the coverage areas of DVB-T2 digital television transmitters were prepared using the example of Kyiv. The proposed methodology and the data obtained will allow to calculate the boundaries of the coverage areas of DVB-T2 digital standard television transmitters in the VHF and UHF wave bands for selected television channels

Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt

Abstract Airports are among the constructions that must meet international standards and specifications established by the International Civil Aviation Organization (ICAO). Some of these parameters and criteria include topography, environmental, and operating circumstances. Navigation operations are also affected by terrain and human barriers, while noise, infrastructure, and weather factors affect the environment around airports. This article investigates using the interpretative structure model (ISM) and analytical network process (ANP) together as a new technique to select and determine the optimum site selection for constructing new international airports in the Nile Delta, Egypt. The criteria used (16 international criteria) are selected depending on the standards of the ICAO, field surveys, and previous studies. Nile Delta, Egypt, is chosen as a case study because it suffers from the lack of international airports and has a population of approximately 23 million. Therefore, new international airports must be established to serve this region, boost tourism, improve transportation, and stimulate commercial traffic. The results using the suggested new technique are compared with the traditional used methods for site selection, such as fuzzy-analytic hierarchy process. Landsat 8 images are used in this research. A quality test using the area under the curve and the receiver operating characteristic curve was applied to evaluate the new technique for site selection of international airports, depending on calculating the highest suitability index for each proposed site. From the quality tests, it is deduced that the suggested method (ISM–ANP) for airport site selection is more accurate than any other traditional method. ArcGIS 10.5 software is used to draw the final digital maps containing the proposed resulted sites. As a result, three new locations for the construction of international airports were found and selected throughout the research region (Delta Nile, Egypt) based on the used mathematical models. Therefore, the proposed novel method for determining the locations of international airports is thought to be effective and feasible, and it can be used to determine the locations of any development projects in general, particularly in developing countries, which benefits the decision-makers in making the right decisions.

LOLA DEM Assisted Photogrammetric Processing of Lunar Reconnaissance Orbiter NAC Images for the Lunar South Pole

Abstract. Because of its special significance, exploration missions and research at the Lunar South Pole (LSP) are of considerable importance. The mapping products of the LSP such as Digital Ortho Maps (DOMs) and Digital Elevation Models (DEMs) are essential to support exploration missions. However, in the LSP region, the available data are relatively limited. The large amounts of shadows and the special environment of the LSP has an important impact on both image matching and generation of mapping products. In this paper, we propose a photogrammetric processing method to improve the accuracy of the LRO NAC images by using LOLA DEM as auxiliary control data. The LOLA DEM was first rendered using the same illumination conditions as the corresponding LRO NAC raw images, and then matched to the LRO NAC DOMs. Next, the corresponding elevations of the tie points between LOLA DEMs and LRO NAC images were extracted and automatically added to the control network for bundle adjustment. In order to verify the feasibility of the method, we selected 17 LRO NAC images near the LSP at 85° south latitude. The results show that the proposed method can efficiently acquire uniformly distributed elevation control points for bundle adjustment and the generated DEM has a good consistency with the LOLA DEM.

Curie point depth, thermal gradient and heat flow along the Ethiopia Rift System and adjacent plateaus using spectral evaluation approach: implications for geothermal resources

The Ethiopia Rift System (ERS) is a section of the East African Rift System within Ethiopia extending from the Afar in the northeast to the Kenya border in the southwest. It is apparent that magmatism and magmatic intrusions influence the crustal shape in the ERS resulting in its thinning and the shallowing of magmatic sources at various locations within it. As a consequence, more than 31 volcanoes hosting hydrothermal structures with a conceivable potential to generate massive quantities of geothermal energy have been identified along the ERS. In this study, we map the Curie Point Depth (CDP) over the ERS based on the analysis of aeromagnetic data extracted from the World Digital Magnetic Anomaly Map. Spectral evaluation method was used to estimate the boundaries (top and bottom) of the magnetized crust. Reduced-to-pole (RTP) aeromagnetic records have been divided into 105 (50% overlap) square blocks of 200 × 200 km size. The Curie temperature (580 °C) of magnetite was used to determine the thermal gradient and the heat drift in the area. The depths obtained for the bottom of the magnetized crust are assumed to correspond to the Curie Depths, where the magnetic layer loses all its magnetization. The determined values of Curie Point Depth, geothermal gradient and heat flow for the 50% overlapped 105 blocks, respectively, range from 8.85 to 55.85 km, 10.38 to 65.54 °C/km and 25.96 to 163.84 mW/m2. Lower CPD (< 20 km) in the ERS was obtained between Mille and Gewane (southwest Afar), between Adama (Nazret) and Yerer (NMER) and between Wendo Genet and Koti (SMER) localities. These areas, showing low CPD, exhibit excessive geothermal gradient and high heat flow all of which indicate the presence of significant geothermal potential.

Research on Dynamic Updating Technology of Digital Maps for Mobile Communications Based on Multi source Remote Sensing Images

Abstract. With the rapid development of 5G networks in China, mobile communication network simulation technology has gradually become a widely studied direction in the academic and application fields. This study focuses on three-dimensional digital geographic information data for mobile communication, including a large amount of geographic information data that significantly affects the propagation of mobile communication radio waves, such as terrain height and ground use types. These data play a crucial role in coverage prediction, interference analysis, and frequency planning in various telecommunications planning software. This article starts from the perspective of mobile communication network management, and combines typical large-scale terrain data production practice projects for mobile communication to deeply study the process and system function design of combining geographic information systems with multi-source remote sensing data in mobile communication network monitoring and management. On this basis, this study designed and implemented a dynamic updated terrain database for mobile communication. This database can update geographic information data in real-time, ensuring the timeliness and accuracy of the data. At the same time, by optimizing the database structure and query algorithms, the speed and efficiency of data processing have been improved, providing efficient and reliable solutions for coverage prediction, optimization analysis, and planning management of large-scale mobile communication networks.In summary, dynamic updating database of three-dimensional digital geographic information has important application value in mobile communication network simulation technology. Through in-depth research and application of this database, the performance and service quality of mobile communication networks can be effectively improved, laying a solid foundation for future communication technology development.

Driving Domain Classification Based on Kernel Density Estimation of Urban Land Use and Road Network Scaling Models

Current research on automated driving systems focuses on Level 4 automated driving (AD) in specific operational design Domains (ODD). Measurement data from customer fleet operation are commonly used to extract scenarios and ODD features (road infrastructure, etc.) for the testing of AD functions. To ensure data relevance for the vehicle use case, driving domain classification of the data is required. Generally, classification into urban, extra-urban and highway domains provides data with similar ODD features. Highway classification can be implemented using global navigation satellite system coordinates of the driving route, map-matching algorithms, and road classes stored in digital maps. However, the distinction between urban and extra-urban driving domains is more complex, as settlement taxonomies and administrative-level hierarchies are not globally consistent. Therefore, this paper presents a map-based method for driving domain classification. First, potential urban areas (PUA) are identified based on urban land-use density, which is determined based on land-use categories from OpenStreetMap (OSM) and then spatially smoothed by kernel density estimation. Subsequently, two road network scaling models are used to distinguish between urban and extra-urban domains for the PUA. Finally, statistics of ODD feature distribution are analysed for the classified urban and extra-urban areas.

Georeferencing of Satellite Images with Geocoded Image Features

Abstract. Currently, using digital orthophoto map (DOM) and digital elevation model (DEM) as reference to achieve geometric positioning of newly acquired satellite images has become a popular photogrammetric approach. However, this method relies on DOM and DEM data which requires a lot of storage space in practical applications. In addition, for geometric positioning of satellite images, only sparse image feature points are needed as control points. Consequently, for the sake of convenience, the compression of control data emerges as a necessity with significant practical implications. This paper investigates a "cloud control" photogrammetry method based on geocoded image features. The method extracts SIFT feature points from DOMs, and obtains their ground coordinates, then constructs geocoded image feature library instead of DOM and DEM data as control, thus realizing the compression of control data. Experiments conducted on the Tianhui-1, Ziyuan-3 and Gaofen-2 satellite images demonstrate that the proposed method can achieve high-precision geometric positioning of satellite images and greatly reduce the size of the control data. Specifically, with the reduction of the reference data from 180~1248 MB 2 m DOM and 30 m DEM to 5~10 MB geocoded image features, the geopositional accuracies of the test Tianhui-1, Ziyuan-3 and Gaofen-2 images are improved from 3.12 pixels to 1.74 pixels, 3.69 pixels to 1.09 pixels, and 150.93 pixels to 2.67 pixels, respectively.

Characterization of Soils in Coffee Productive Areas in the Provinces of Cocle and Veraguas-Republic of Panama

The objective of the work was to characterize the properties of the soils of coffee-producing farms in the provinces of Cocle and Veraguas that allow the preparation of regionalized fertilization cards according to the edaphoclimatic characteristics of each area. Planting robusta coffee is gaining greater interest among Panamanian farmers as an alternative for soil conservation. On 16 farms in the province of Coclé and 18 in Veraguas, samples were taken (0-20 cm) to understand their properties and initiate a sustainable fertilization program that contributes to improving productivity. Digital maps were created using the Q-Gis v.3.32.1 program and regionalized fertilization cards for each zone. The soils of the province of Coclé presented a high percentage of aluminum saturation, 88% with low porosity problems, average pH of 4.9, low levels of organic matter and phosphorus (100%), and 75% low in potassium. 56% low in calcium. Imbalances were found in the Ca/Mg and Ca + Mg/K ratios, which causes nutritional problems. In Veraguas soils, 22% presented a high percentage of aluminum saturation, 67% average acid pH, 94% low organic matter and phosphorus content, 89% low in potassium, medium and high levels of magnesium and calcium. A positive and statistically significant correlation was found between % clay, organic matter, pH, and calcium. It is expected that this soil characterization and regionalized fertilization cards will contribute to improving soil properties and achieving sustainable production. Fertilization recommendation cards have been prepared for these two provinces and they take into account this entire situation and physical problems found in the soils. Much emphasis is also placed on organic fertilization of plantations as a complement to traditional fertilization, seeking more balanced nutrition.

Digital mapping of machine tool's volumetric and tracking errors left on the five axis tool paths

Five-axis contouring errors left on the parts contributed by feed drive servos, and intra- and inter-axis geometric errors are predicted along the five-axis tool paths. Quasi-static geometric errors are measured and modeled in the machine's workspace. Servo tracking errors of the three translational and two rotary drives are measured during machining or predicted from the CNC model. All errors are projected to the tooltip using a generalized screw theory-based forward kinematic model. The proposed model is validated experimentally on a CNC machining center for two test parts.

Spatializing soil elemental concentration as measured by X-ray fluorescence analysis using remote sensing data

Spatial soil information has significantly contributed to public policy and planning. The use of X-ray fluorescence spectrometry (XRF) in soil science, primarily in laboratory settings or for isolated point analyses, has been well documented. This study broadens the application of XRF data by integrating it with remote sensing data for a comprehensive understanding of soil patterns and processes. Focusing on a 2574 km2 area in Brazil, we utilized a lab-based XRF instrument to measure the total concentrations of key chemical elements in the soil, such as Al, Si, Ti, and Fe. These geochemical data were then spatialized using the digital soil mapping (DSM) framework. We employed a synthetic bare soil image and elevation data as covariates in the analysis. The spatial accuracy assessment yielded R2 values ranging from 0.65 to 0.81 for Fe, 0.62 to 0.78 for Ti, 0.52 to 0.63 for Si, and 0.48 to 0.63 for Al. The digital soil maps of geochemical elements aligned well with existing pedological, and geological maps as evaluated using multiple correspondence analysis. The integration of spatialized XRF data with DSM and remote sensing techniques shows significant promise in assessing soil variations across landscapes based on their chemical composition.

A Mixed Methods Pilot Study to Evaluate User Engagement with MedMicroMaps: A Novel Interactive E-learning Tool for Medical Microbiology

A Mixed Methods Pilot Study to Evaluate User Engagement with MedMicroMaps: A Novel Interactive E-learning Tool for Medical Microbiology

Civics Textbook Supplement Design Based on Pancasila Student Profiles to Increase Student Activity

The condition of diverse learning needs and the rapid development of education requires textbooks prepared by the government to meet learning standards that are expected to fulfill the success of learning activities in schools. The results of the needs analysis of the Civics Junior High School Textbook in Bandar Lampung City show that there is a need for a complementary textbook that is expected to support typical Civics learning activities. This research method is a development research used to produce certain products, and test the effectiveness of these products. The development model and procedures used in this study are divided into 4 stages, 1) Needs Analysis Stage, 2) Product Design Stage, 3) Validation and Evaluation Stage, and Final Product Stage. The results of this study show that the use of textbook supplements has a significant impact on students' understanding and learning outcomes. Effectiveness testing shows that the average formative test results for students who use digital map learning are greater than the average formative test results for students who use conventional learning. Efficiency testing can be concluded that Civics learning, especially the material displaying a positive attitude towards the flow of drafting the foundation of the Indonesian state and norms in everyday life using textbook supplement learning has the efficiency of saving more time than learning that does not use textbook supplements. It can be concluded that students' attitudes towards the attractiveness of digital map learning that has been applied in experimental classes have a positive impact on learning outcomes.

Spatial variability of heavy metals concentrations in soil of auto-mechanic workshop clusters in Nsukka, Nigeria

The indiscriminate disposal of spent engine oils and other hazardous waste at auto mechanic workshops clusters in Nsukka, Enugu State, Nigeria is an environmental concern. This study examines the concentration of heavy metals in the soil inside the workshop cluster and in the unpolluted soil outside the workshop cluster at approximately 100 m. Ten sampling points were randomly selected from within the cluster and another ten from outside the cluster. Using a hand-held Global Positioning System, the coordinates of the selected points were established and used to create a digital map. Soil samples at depths of 0–30 cm and 30–60 cm, were analyzed for Cu, Fe, Zn, Pb, As and Cd using Spectrophotometer. Moisture content determination and particle size analysis were also done on the samples. Spatial variability of heavy metals concentrations of the studied site was also mapped with ArcGIS 10.2.2 using interpolation methods. Results showed that the soil ranged from sandy loam to sandy clay loam. Cadmium and Zinc had the lowest and highest concentration, respectively, in the studied area. Comparing the concentrations of heavy metals in soils within and outside the auto mechanic cluster revealed notable differences across various depths (0–30 cm and 30–60 cm). The analysis results for soil samples within the cluster exhibited concentration levels (mg/kg) ranging from 0.716–0.751 (Cu), 2.981–3.327 (Fe), 23.464–30.113 (Zn), 1.115–1.21 (Pb), 2.6–2.912 (As), and 0.133–0.365 (Cd) demonstrating a variation pattern in the order of Zn > Fe > As > Pb > Cu > Cd. Conversely, for soil samples outside the cluster, concentration levels (mg/kg) ranged from 0.611–0.618 (Cu), 2.233–2.516 (Fe), 12.841–15.736 (Zn), 0.887–0.903 (Pb), 1.669–1.911 (As), and 0.091–0.091 (Cd). To assess the disparity in heavy metal concentration levels between samples collected within and outside the clusters, ANOVA test was performed. The test showed significant difference in heavy metal concentrations between samples within and outside the auto mechanic cluster (p < 0.05), implying auto mechanic activities significantly impact heavy metal levels within the cluster compared to outside areas. The assessment of soil pollution utilized indices including the Geo-accumulation Index (Igeo), Contamination factor (Cf), and anthropogenic metal concentration (QoC). Zinc, Cadmium, and Arsenic showed the highest contamination factors, indicating significant soil contamination likely due to anthropogenic activities. The concentrations of the metals analyzed were within WHO permissible limits while the metals concentrations were also observed to decrease as depth was increased. Using ArcGIS 10.2.2, spatial maps showing heavy metal distribution were developed, with the Kriging method proving superior. This study suggests that heavy metal levels in the soil at the area be monitored on a regular basis.

Seeing through their eyes: Revealing recreationists’ landscape preferences through viewshed analysis and machine learning

Planning for outdoor recreation requires knowledge about the needs and preferences of recreationists. While previous research has mainly relied on stated preferences, recent advances in spatial data collection and analysis have enabled the assessments of actual usage patterns. In this study, we explored how landscape characteristics interact with the attributes of recreationists to determine their area choice for recreation. Using a public participation GIS (PPGIS) approach we asked residents of a Swedish city in the boreal region to draw typical recreational routes and identify favourite places for recreation on a digital online map (1389 routes, 385 individuals). We employed a novel methodology, where LiDAR data was used to calculate what was visible along all routes and at favourite places (viewsheds) in order to more realistically capture the landscape that each recreationist had experienced. Using machine learning modelling, we compared landscape characteristics of experienced areas with areas available to each recreationist. Our novel approach yielded accurate models that revealed that water environments, recreational infrastructure and deciduous forests increased the probability of choosing an area for recreation, while urban environments, noise, forest clearcuts and young forests had the opposite effect. Characteristics of the recreationists such as age, gender, level of education, or of the activity, such as type of activity performed, did not meaningfully influence area choice. Our findings suggest that it is possible to improve the conditions for recreation by developing recreational infrastructure, maintaining recreation opportunities close to waters, and adapting forest management in areas important for recreation.

Evaluation of the soil carbon sequestration potential and toward digital soil mapping under semi-arid Mediterranean ecological condition

Evaluation of the soil carbon sequestration potential and toward digital soil mapping under semi-arid Mediterranean ecological condition

A method of drawing double arrow symbols with automation support for 2D digital map applications

This paper addresses the challenges of translating pivotal military symbols like double arrows onto 2D digital maps, particularly when automation is essential. It proposes a comprehensive method integrating data structure, user workflow, and algorithms to generate tactically aligned, balanced, and visually appealing double arrows for military 2D digital maps. Experimental validation affirms its efficacy in meeting tactical requisites and aesthetic standards. Additionally, it outlines avenues for future research to further enhance and broaden the applicability of this method. In essence, this study presents a robust solution for the automated generation of double arrows on 2D digital military maps, marking a significant step in advancing automated symbolization for military planning.

The Western Greece Soil Information System (WΕSIS)—A Soil Health Design Supported by the Internet of Things, Soil Databases, and Artificial Intelligence Technologies in Western Greece

Soil quality is vital for ecosystem stability, impacting human, plant, and animal health. Traditional soil quality assessments are labor-intensive and costly, making them unsuitable for smart agriculture. To overcome this, Internet of Things (IoT) and artificial intelligence (AI) technologies are employed for sustainable agriculture, enabling real-time data collection and analysis, trend identification, and soil health optimization. The Western Greece Soil Information System (WΕSIS) offers open-access data and services for soil health and sustainability. It includes modules for soil quality indicators, sustainable fertilization management zones, soil property distribution, prediction, mapping, statistical analysis, water management, land use maps, digital soil mapping, and crop health calculation. Integrating the IoT and AI allows for real-time and remote monitoring of soil conditions, managing soil interventions adaptively and in a data-driven way, enhancing soil resources’ efficiency and sustainability, and increasing crop yield and quality. AI algorithms assist farmers and regional stakeholders in optimizing production lines, methodologies, and field practices, reducing costs and increasing profitability. This promotes a circular economy, a soil- and climate-resilient future, biodiversity protection targets, and enhanced soil fertility and productivity. The proposed IoT/AI technical architecture can underpin the development of soil health monitoring platforms, integrating data from various sources, automating data collection, and providing decision support tools.

Affective routes in interviews: Participants exploring a digital map as a live elicitation method

How we elicit rich reflections from people about their feelings and experiences is a central consideration of qualitative research. Creative techniques of elicitation can open reflective dialogic spaces between participants and researchers, bridging memory and meaning. In this article we discuss participant-led explorations of a digital story-mapping platform as an elicitation technique in qualitative interviews. This platform is Queering the Map, a community-generated counter-mapping project that digitally archives queer moments in place. An atemporal cartographic representation of queer life, visitors to the site zoom, drag and click to reveal the anonymous contributions of others: micro-stories of experience, poems, messages and yearnings, claiming a global landscape of queer feeling. Here we offer reflections from our experience of asking participants to explore and guide us around the map within an interview. We chart how this method of live digital discovery was generative of elicitation and evocation, of insights on affective roots (where have I come from?) and affective routes (where can I take you?). This article contributes to scholarship on elicitation and live methods, including digital and spatial approaches, and to conceptualisations of orientation and mapping.

Relationship between monthly surface water derived from Sentinel-2 imagery and meteorological data (precipitation and evaporation) at Baghdad, Iraq

Abstract This study was carried out using two main monthly datasets: satellite images and some meteorological parameters (like precipitation and evaporation) for 2 years (2018 and 2021). Using the fine spatial resolution images acquired from Sentinel-2, digital maps of surface water areas were produced by calculating the Modified Normalized Difference Water Index for all months in QGIS and then classifying them into two main categories (water and non-water bodies). The results show that the largest areas were in January 2021 (77.2 km2) and 40.7 km2 in November 2018, while the lowest (18.7 km2) were in June 2021 and July 2018. Monthly mean values of cumulative precipitation and evaporation were used to find their relationships with surface water areas. The results show that the year 2021 was a severe dry year, whereas the lowest precipitation (25 mm) in that year was associated with higher evaporation losses (3,021.8 mm) during this year. When combined with surface water areas and meteorological parameters, a non-linear relationship between water areas and evaporation was found for both years, and a linear relationship between precipitation and water areas for the wet year of 2018.

Enhancing precision of root-zone soil moisture content prediction in a kiwifruit orchard using UAV multi-spectral image features and ensemble learning

Accurate and real-time monitoring of soil moisture content (SMC) is of utmost importance for effective field irrigation and maximizing crop water productivity. However, a comprehensive investigation into the inversion study for determining suitable combinations of unmanned aerial vehicle (UAV) image features and enhancing the precision of SMC model prediction has yet to be fully validated within a kiwifruit orchard setting. This study addresses this gap by employing a pre-processing method and an optimal band combination algorithm to assess the impact of various combinations of kiwifruit canopy reflectance and fraction vegetation coverage (FVC) features on the sensitivity of root-zone SMC. Furthermore, an optimal ensemble learning (EL) framework was developed to monitor SMC at various root-zone depths (0–10 cm [SMC10], 0–20 cm [SMC20], 0–30 cm [SMC30], 0–40 cm [SMC40], 0–50 cm [SMC50], 0–60 cm [SMC60]). The key findings of this research highlight the successful derivation of 10 wavebands and FVC features, exhibiting a strong correlation with SMC at different root depths. The gradient boosting (GBDT) model demonstrated the exceptional accuracy in estimating SMC10, with an impressive R2 value of 0.963 ± 0.030 and low RMSE values of 0.238 ± 0.111. Similarly, the eXtreme Gradient Boosting (XGBoost) model outperformed in estimating SMC20 to SMC60, with R2 and RMSE values of 0.963 ± 0.024 and 0.117 ± 0.053, respectively. Additionally, the utilization of the optimal EL model allows for digital mapping of SMC at different depths across fruit growth stages, showcasing superior adaptability for SMC30 to SMC60 (with R2 and RMSE of 0.782 ± 0.090 and 0.037 ± 0.011) compared to SMC10 and SMC20 (with R2 and RMSE of 0.765 ± 0.097 and 0.056 ± 0.024). These results underscore the potential of the EL estimation framework in characterizing the spatial distribution of root-zone SMC at the individual kiwifruit plant level.