Land Characteristics Research Articles

CLASS STUDY AND MAP OF FLOOD VULNERABILITY LEVELS IN THE KRUENG JREUE SUB WATERSHED ACEH Besar

The increasing intensity of land conversion in the Krueng Jreue Aceh Besar Sub-watershed due to changes in land use causes changes in the biophysical characteristics of the land. Changes in the biophysical characteristics of land cause an increase in the level of flood vulnerability. This research uses a descriptive method (survey). The research results show: The variables determining the level of flood vulnerability are based on the biophysical aspects of the land, namely: dynamic factors (rainfall, land use) and static factors (soil infiltration, slope slope). Flood Vulnerability Level Classes and Maps (TKB), consisting of: Very Vulnerable, residential areas, rice fields (42≤TKB≤50); Vulnerable, moor (34≤TKB≤41); and Medium, open land, shrubs, grasslands, secondary forests, primary forests (26≤TKB≤33), average 32.38 (medium class).Primary forests and secondary forests in non-cultivation areas, covering an area of ​​12,598.00 ha or 54.26% of the total area of ​​the Krueng Jreue Sub-watershed (23,218.06 ha), have the lowest level of flood vulnerability. Flood disasters occur in November-December, when rainfall is high, which causes residents to be exposed, physical and economic losses and land damage.

IDENTIFIKASI MINERAL SEKUNDER UNTUK MENILAI POTENSI TANAH LAHAN PERTANIAN CABE HIYUNG DI DESA HIYUNG KABUPATEN TAPIN KALIMANTAN SELATAN

Hiyung Village is part of Tapin Tengah District, Tapin Regency, South Kalimantan. Besides rice, superior agricultural commodities in this village is Hiyung Cayenne Pepper. High demand and prices have caused more and more cayenne pepper to be cultivated. Hiyung Cayenne Pepper Cultivation is expected to increase farmers' income, so efforts are needed so that agricultural activities can continue. The sustainability of agricultural can be maintained if they are managed well. Appropriate management actions can be implemented if the soil and land characteristics are known. Soil is one of the things that really determines the success of an agricultural business, especially the availability of nutrients. Analysis of the secondary mineral composition of soil (sand) is very important in order to determine the nutrient reserves in the soil. Information about nutrient reserves in the soil can be used as a basis for good soil management so that it meets plant needs and does not cause damage to the soil and the environment. The specific aim of this research is to determine the composition of primary minerals (sand) to identify nutrient reserves in the soil. This research was carried out using a survey method to obtain data or information on soil and land characteristics and current management. Observations and soil sampling in the field were carried out at 5 locations with a distance of 1 km between locations. Sand mineral analysis uses a polarizing microscope to determine the mineral composition. Primary minerals (sand) at the research location are dominated by rock fragments and quartz. The weatherable mineral content is very small so the potential for nutrient reserves is very small.

Response of Land Surface Albedo to Fire Disturbance in the Sierra Nevada Seasonal Snow Zone Over the MODIS Record

AbstractWildfires in the snow zone can brighten winter and spring landscapes by removing forest canopy, revealing underlying snow cover. Land surface albedo (LSA) alterations associated with transitioning from a canopied, snow‐hiding vegetation regime to a snow‐revealing landscape have impacts on the surface energy balance, with implications for climate and water supply. Forest fires are increasing in frequency, size, and elevation, but the change in LSA due to fire in the seasonal snow zone (SSZ) is poorly understood. This study addresses this knowledge gap for the Sierra Nevada, where recent climatic changes have contributed to droughts, earlier and more rapidly declining snowpacks, and worsening wildfire impacts. Remotely sensed snow fraction and LSA data from Moderate Resolution Imaging Spectrometer were used to assess the impact of wildfire on landscapes in the Sierra Nevada SSZ by comparing LSA in burn scars to unburned control areas and the historical average LSA, then quantifying the surface radiative forcing (RF) associated with change in LSA. Among high and moderate burn severity fires, winter LSA varied depending on snow cover, land characteristics, and burn severity, ranging from 0.12 in low‐snow fire scars to 0.47 in snow‐covered fire scars. This study adds to understanding of how landscapes respond to wildfires and the subsequent impacts on the surface energy balance.

Performance test and economic analysis of paddy direct seed planter modified pulled by tractor (amator) in tidal swamp, South Sumatra

Abstract Lack of labor is another obstacle to the characteristics of the tidal swamp land itself so that the use of agricultural equipment and machinery such as Amator can be used as solution. This paper aims to analyze the use of the amator in terms of technical and economic aspects of the equipment so that it is known whether it is efficient and profitable for farmers. Data collection was carried out through observation of equipment performance tests in the field and key informant interviews. This research was carried out in Sri Katon Village, Air Salek District, Banyuasin Regency. The data observed are technical performance including work capacity and work efficiency, as well as economic analysis in the form of basic costs and break-even point of using equipment. The results of the performance test show that the amator has a high efficiency (87.52%) with an effective working capacity value of 0.6244 ha hr−1 (1.6012 hr ha−1). The basic cost incurred for the use of the amator is IDR 193.145 ha−1 with a BEP of 16,8 ha yr−1. The use of amator with seeds only 35 kg ha−1 can save planting costs of IDR 200,000 ha−1 compared to the farmer practice.

Reduction Of The Potential Of Landslides With Vegetative Methods In The Sub-DAS Of Kali Arus, Banyumas Regency

Abstract Landslides are one of the natural disasters that often occur. The magnitude of the potential for landslides is caused by several factors including geomorphological conditions, geology, soil, land use, and rainfall. The goal of this research is to investigate the technique of reducing the potential for landslides in the research area using the vegetative method with a geomorphological approach. The research method uses a survey method, which includes fieldwork and laboratory work. Data processing uses scoring or weighting techniques. Data analysis for landslide potential reduction techniques utilizing qualitative descriptive. Reducing the potential for landslide natural disasters vegetatively, namely by selecting the type of vegetation that is following the characteristics and quality of the landform. Plants can be used to control landslides as a result of the research. Mahoni, Jati, and Sengon are plants that can be used to control landslides in the suitable and marginally suitable land suitability classes, reducing the potential for landslides through the analysis of land characteristics and land quality in each landform unit. The results of the land characteristics and land quality analysis are used to map land suitability classes for a variety of annual crops.

Firefly Algorithm for SVM Multi-class Optimization on Soybean Land Suitability Analysis

Soybean is the primary source of vegetable protein nutrition, containing fat and vitamins that Indonesian people widely consume. The decline in soybean production in Indonesia every year is due to the reduced area of soybean cultivation, thereby increasing dependence on imports from other countries. Land suitability maps can provide directions for priority locations for soybean cultivation based on land characteristics and weather to produce optimal production. The SVM multi-class algorithm has been applied to classify land suitability data to create a land suitability map but has yet to obtain optimal accuracy, especially for sigmoid kernels. The objective of this study is to enhance the performance of the sigmoid kernel SVM by utilizing the firefly algorithm. The study focuses on evaluating the suitability of soybean cultivation in Bogor and Grobogan Regencies. The results of the tests indicate that the firefly algorithm-optimized SVM (FA-SVM) significantly improves accuracy compared to the SVM without optimization. The accuracy achieved by FA-SVM is 89.95%, while the SVM without optimization only achieves an accuracy of 65.99%. The best parameters produced by the firefly algorithm are C=2.33 and σ=0.45 obtained from firefly customization, and the number of generations is 10. Based on this, the optimization algorithm can be used to produce an optimal model. The best optimal model obtained can be used as a guide for priority locations/areas for soybean cultivation by farming communities, so as to produce maximum soybean productivity.

Evaluation of Soil Fertility Status on Oil Palm Cultivation Land (Elaeis guineensis Jacq.) In Pulo Padang Village

Declinen soil fertility on crop cultivation land has become a problem that must be considered. As an effort to rehabilitate these lands, it is very necessary to carry out appropriate actions. One way is to evaluate soil fertility by looking at the soil chemical characteristics of land that has been managed intensively. The aim of this research is to determine the chemical characteristics of the soil and the status of soil fertility on agricultural land in Pulo Padang Village, Rantau Utara District, Labuhanbatu Regency. The method used in this research was a survey method and soil sampling which was carried out by purposive sampling on oil palm plants which were composited, air-dried and chemical analysis of the soil was carried out in the laboratory. The results of research on soil chemical characteristics show a soil pH value of 5.0-5.2 with acid status, C-organic 1.42-1.67% (Low), Total Nitrogen 0.13-0.15% (low), P2O5 with a value of 283-400 mg/100g (Very High ), P available 36.2-126.6 ppm (Very High), Base cations can be exchanged Ca2+, Mg2+, K+, Na+ namely 7.19-8.13 cmol/kg (Medium), 3.22-3.93 cmol/kg (High), 0.17-0.30 cmol/ kg (Low), 0.08-0.22 cmol/kg (Very low), CEC 14.66cmol/kg (Very Low) and 21.98 cmol/kg (Medium), KB 57-73 % (Very High). The soil fertility status of agricultural land in Pulo Pandang Village, Rantau Utara District, Labuhanbatu Regency has a low fertility status, so management measures are needed to increase soil fertility

A Study on The Frequency Characteristics of Typhoon Landing in Guangdong, China, Based on Machine Learning Methods

Located in the southern coastal region of China, Guangdong Province is perennially threatened by typhoons originating from the Northwest Pacific Ocean. Compared to studies focusing on typhoon intensity, paths, and catastrophic effects, research on typhoon landing frequency features remains relatively limited. This study systematically constructs a dataset based on the landing typhoon frequency of Guangdong over 71 years (as the target variable) and a set of 88 atmospheric circulation indices as well as 26 sea surface temperature indices (as the potential feature variables). Two machine learning models, including Random Forest Regression (RR) and Support Vector Regression (SVR), are used to predict the frequency of typhoons making landfall in Guangdong, and the results show that both models have good characterisation ability while the RR model has slightly better fitting performance on the training set. This study provides scientific support for understanding the characteristics of typhoons in Guangdong and better responding to typhoon disasters.

GNSS Reflectometry-Based Ocean Altimetry: State of the Art and Future Trends

For the past 20 years, Global Navigation Satellite System reflectometry (GNSS-R) technology has successfully shown its potential for remote sensing of the Earth’s surface, including ocean and land surfaces. It is a multistatic radar that uses the GNSS signals reflected from the Earth’s surface to extract land and ocean characteristics. Because of its numerous advantages such as low cost, multiple signal sources, and all-day/weather and high-spatiotemporal-resolution observations, this new technology has attracted the attention of many researchers. One of its most promising applications is GNSS-R ocean altimetry, which can complement existing techniques such as tide gauging and radar satellite altimetry. Since this technology for ocean altimetry was first proposed in 1993, increasing progress has been made including diverse methods for processing reflected signals (such as GNSS interferometric reflectometry, conventional GNSS-R, and interferometric GNSS-R), different instruments (such as an RHCP antenna with one geodetic receiver, a linearly polarized antenna, and a system of simultaneously used RHCP and LHCP antennas with a dedicated receiver), and different platform applications (such as ground-based, air-borne, or space-borne). The development of multi-mode and multi-frequency GNSS, especially for constructing the Chinese BeiDou Global Navigation Satellite System (BDS-3), has enabled more free signals to be used to further promote GNSS-R applications. The GNSS has evolved from its initial use of GPS L1 and L2 signals to include other GNSS bands and multi-GNSS signals. Using more advanced, multi-frequency, and multi-mode signals will bring new opportunities to develop GNSS-R technology. In this paper, studies of GNSS-R altimetry are reviewed from four perspectives: (1) classifications according to different data processing methods, (2) different platforms, (3) development of different receivers, and (4) our work. We overview the current status of GNSS-R altimetry and describe its fundamental principles, experiments, recent applications to ocean altimetry, and future directions.

Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia

The transboundary region along the Heilongjiang River, encompassing the Russian Far East and Northeast China, possesses abundant agricultural natural resources crucial for global food security. In the face of the challenge of disruptions in the global food supply chain, the precise monitoring and exploitation of agricultural resources in the Heilongjiang Basin becomes imperative. This study employed deep learning to classify crop status in 2023 in the Heilongjiang Basin using Sentinel-2 satellite remote sensing images at a 10 m resolution. Various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Enhanced Vegetation Index (EVI), the Modified Soil Adjusted Vegetation Index (MSAVI), and others, were computed and analyzed for different crops. The Google Earth Engine (GEE) platform was utilized for validation point sampling based on plot objects. The random forest (RF) classification method was successfully employed to classify and identify major crops in the study area (wheat, maize, rice, and soybean), as well as wetlands, tree cover, grassland, water, and constructed land, with an overall classification accuracy of 86%. Tree cover dominated the land cover, constituting 62%, while wheat, maize, rice, and soybeans accounted for 7% of the total area. Of these, soybeans occupied the largest area (57,646.60 hectares), followed by rice (53,209.53 hectares), maize (39,998.37 hectares), and wheat (8782.31 hectares). This study demonstrated that sample selection based on plot objects facilitates efficient sample labeling, providing insights into crop classification in other, potentially larger, areas. This method simultaneously distinguishes wetland, cultivated land, and forest features, supporting further integrated investigations for more natural resources.

Dominant conservation parameters to control environmental destruction of oxbow lake in Kapuas River Basin, Indonesia

Global climate change has significantly impacted the ecosystem of a specific aquatic region, including its lake area. Lakes provide immense benefits in efforts to meet various needs and enhance community well-being. These include the provision of raw water for clean drinking, household and urban water supply, industry, supplemental irrigation for agriculture, hydroelectric power generation, and various other purposes. However, fluctuations in river water availability trigger adjustments in both the river flow patterns and their inherent characteristics. These modifications in land features and ecosystems have consequently brought the morphological transformations in rivers throughout the adjacent region. The characteristics and functions of oxbow lakes must be preserved, and conservation efforts must be undertaken. To maintain their functionality, it is essential to conduct a study to determine the dominant parameters that influence the characteristics of oxbow lakes. This study aims to identify the primary parameters for conservation efforts in oxbow lakes. Based on a field survey of physical conditions, most oxbow lakes exhibit suboptimal conditions, as nearly all water storage areas are already overgrown with wild vegetation and possess high sediment levels. It is essential to continuously monitor several parameters to maintain the performance of oxbow lakes. The results of this research show that the dominant parameters for the oxbow lake in west Kalimantan are divided into 3 (three) aspects: reservoir, oxbow lake embankment structure, and oxbow lake embankment crest.

Land Suitability Analysis of Mung Bean Plants (Vigna radiate L.) by Growing Degree Days Method

Mung bean (Vigna radiate L.) is one of the most widely cultivated crops after soybean and peanut. Mung beans (Vigna radiate L.) became one of the crops that experienced a decline in production amid the increase in the area of harvested land in West Sumatra. The purpose of this study was to analyze the level of land suitability of mung bean crops using the Growing Degree Days method in West Sumatra Province. The research method used in this research is Scoring and Overlay using ArcGis 10.5 software. Land suitability analysis is carried out by giving scores to several parameters of land characteristics. The parameters used are slope, land use, rainfall, soil texture and soil pH. Growing Degree Days analysis was conducted by accumulating the daily temperature values of plants based on planting scenarios. These two analyses were combined with Overlay clustering. The results showed that the land area of West Sumatra whose GDD value is optimal for mung bean growth with the level of land suitability in physical characteristics in S1 class is 15,819.29 ha and in S2 class is 877,501.06 ha. Areas that are suitable in terms of physical characteristics and values with an area of > 100 ha are Lima Puluh Kota District, Pasaman District, Solok District, and South Solok District. The best planting time for mung bean based on the planting scenario is from December to March.

Distribution, Preservation Characteristics of Land and River Natural Aggregates in Nonsan City, Korea

Distribution, Preservation Characteristics of Land and River Natural Aggregates in Nonsan City, Korea

Distribution Characteristics of Land and River Aggregate Resources in Yeongam Area by Deposition Period

Distribution Characteristics of Land and River Aggregate Resources in Yeongam Area by Deposition Period

Analyzing the Factors of Vacant Home Occurrence for Urban Sustainability: A Case Study of Medium-Sized Cities Focusing on Asan City, Chungcheongnam-do

This study aims to enhance urban sustainability by analyzing the spatial distribution and underlying causes of vacant homes in Asan, Chungcheongnam-do. Various statistical methods were employed to analyze date concerning the number of vacant stores, population changes, land use complexity, and the physical characteristics of land and buildings; these factors were found to influence the prevalence of vacant homes across Asan. Additionally, it was found that distinct factors differentially affect specific regions, such as old downtown areas versus rural villages. This indicates that reducing vacant homes in these areas requires distinct policies tailored to the unique circumstances of each region. For instance, in old town areas, small lot ratios and land use complexity are significant, while in rural villages, the average number of floors and land use complexity play a major role. This study highlights the diverse factors influencing the prevalence of vacant homes and suggests that to effectively address this issue, policies should be developed that are tailored to the unique characteristics of each area, categorized at both the city and local levels.

BLEI: Research on a Novel Remote Sensing Bare Land Extraction Index

Bare land, as a significant land cover type on the Earth’s surface, plays a crucial role in supporting land-use planning, urban management, and ecological environmental research through the investigation of its spatial distribution. However, due to the diversity of land-cover types on the Earth’s surface and the spectral complexity exhibited by bare land under the influence of environmental factors, it is prone to confusion with urban and other land features. In order to extract bare land rapidly and efficiently, this study introduces a novel bare land extraction index called the Bare Land Extraction Index (BLEI). Then, considering both Ganzi Tibetan Autonomous Prefecture and Urumqi, China as the study areas, we compared BLEI with three presented indices: the Bare-soil Index (BI), Dry Bare Soil Index (DBSI), and Bare Soil Index (BSI). The results show that BLEI exhibits excellent efficacy in distinguishing bare land and urban areas. It gets the most outstanding accuracy in bare land identification and mapping, with overall accuracy (OA), kappa coefficient, and F1-score of 98.91%, 0.97, and 97.89%, respectively. Furthermore, BLEI is also effective in distinguishing bare land from sandy soil, which can not only improve the mapping accuracy of bare land in soil-deserted areas but also provide technological support for soil research and land-use planning.

Aboveground Biomass Inversion Based on Object-Oriented Classification and Pearson–mRMR–Machine Learning Model

Cities play a crucial role in the carbon cycle. Measuring urban aboveground biomass (AGB) is essential for evaluating carbon sequestration. Satellite remote sensing enables large-scale AGB inversion. However, the apparent differences between forest and grassland biomass pose a significant challenge to the accurate estimation of urban AGB using satellite-based data. To address this limitation, this study proposed a novel AGB estimation method using the eastern part of the Zhahe mining area in Huaibei City as the study area, which integrates land cover classification, feature selection, and machine learning modelling to generate high quality biomass maps of different vegetation types in an urban area with complex feature distribution. Utilizing the GEE platform and Sentinel-2 image, we developed an object-oriented machine learning classification algorithm, combining SNIC and GLCM to extract vegetation information. Optimal feature variables for forest and crop-grass AGB inversion were selected using the Pearson–mRMR algorithm. Finally, we constructed nine machine learning models for AGB inversion and selected the model with the highest accuracy to generate the AGB map of the study area. The results of the study are as follows: (1) Compared with the pixel-based classification method, the object-oriented classification method can extract the boundaries of different vegetation types more accurately. (2) Forest AGB is strongly correlated with vegetation indices and physiological parameters, while agri-grass AGB is primarily associated with vegetation indices and vegetation physiological parameters. (3) For forest AGB modelling, the RF-R model outperforms other machine learning models with an R2 of 0.77. For agri-grass AGB modelling, the XGBoost-R model is more accurate, with an R2 of 0.86. (4) The mean forest AGB in the study area was 4.60 kg/m2, while the mean agri-grass AGB was 0.71 kg/m2. High AGB values were predominantly observed in forested areas, which were mainly distributed along roads, waterways, and mountain ranges. Overall, this study contributes to a better understanding of the health of local urban ecosystems and provides valuable insights for ecosystem protection and the sustainable use of natural resources.

Effect of aircraft landing angle on hydrodynamics characteristics during wave landing

In order to study the effect of different courses on the landing performance of aircraft when landing in waves, a Computational Fluid Dynamics (CFD) method is developed to investigate the landing performance of the NACA TN 2929 A aircraft model. The solution of fluid flow is based on the Reynolds-averaged Navier-Stokes equations with the shear stress transport (SST) k−ω turbulence model, and the numerical scheme employed is the Finite Volume Method (FVM). The fifth-order Stokes wave is generated by using the velocity inlet wave-making method. In the work, the slamming load, pitch change and motion response of the aircraft landing at different wave positions are analyzed. The landing characteristics of aircraft landing parallel to the wave, perpendicular to the wave and at the yaw angle are studied. It is found that the landing characteristics of the aircraft at the 1/2 π phase are better than those at other ones, with the maximum load at the π phase. When the aircraft lands parallel to the wave, yaw displacement generated at 1/2 π phase and 3/2 π phase is equal in magnitude and opposite in direction in the initial short time. These results are important for reducing slamming loads and improving the airworthiness of the aircraft structure when landing on a wave surface.

Analysis of the initial steps of the Market Comparison Approach (MCA) for its application to agricultural land: parameters of the market segment and real estate data

In the current Italian real estate market, estimates of the market value of rural properties are carried out by appraisers mainly using the single-parameter comparison procedure and, less frequently, the income capitalization method. To overcome problems of appraiser subjectivity and other issues related to these methods, and to gradually comply with real estate appraisal standards, this research paper aims to develop and validate a scientifically rigorous method. This article carries out a first attempt to apply the mixed approach based on the integration of Market Comparison Approach (MCA) and General Assessment System (GAS) to agricultural land, currently used only in the field of urban real estate appraisal. The study focuses on the first steps of this valuation procedure: choice of the parameters which characterise the market segment and identification of the land characteristics to be included in the procedure. These two steps are preparatory to the following phase concerning the estimate of marginal prices, which represents the core of the whole valuation procedure. In the current Italian real estate market, estimates of the market value of rural properties are carried out by appraisers mainly using the single-parameter comparison procedure and, less frequently, the income capitalization method. To overcome problems of appraiser subjectivity and other issues related to these methods, and to comply with real estate appraisal standards, this research paper aims to develop and validate a scientifically rigorous method. The Market Comparison Approach (MCA) is used as a starting point for a comparative multiparameter approach, which is currently only used in the field of urban real estate appraisal.This paper carries out a first attempt to apply the MCA to agricultural land, contributing an initial exploration of developing the proposed methodologies. Specifically, this paper reports the first steps that have been carried out in this line of research: choosing the parameters to identify the market segment; identifying the characteristics to include in the procedure for which to determine the respective marginal prices.

Distribution of Levels of Suitability of Land Characteristics and Growing Degree Days (GDD) for Corn (Zea mays L.) in West Sumatra

The decline in corn production is influenced by several factors, one of which is the suitability of land characteristics. This research analyses land characteristics' suitability and GDD for West Sumatra Province corn crops. The suitability of land characteristics is studied by combining the suitability of land characteristics with the GDD value in the area. The suitability of land characteristics is determined by matching plant growth requirements and land characteristics. The parameters used to determine the suitability of land characteristics are land use, slope, soil pH, soil texture and rainfall. GDD suitability by matching the accumulated daily temperature data during one growing season to the optimal GDD value for corn plant growth. This research created 12 planting time scenarios, and the analysis was carried out based on Regency/city administrative areas in the West Sumatra region. The analysis results obtained a suitable land area based on land characteristics and GDD values, namely an area of 1,206,079.48 ha with a planting season of December-March. Areas in West Sumatra Province with great potential for corn crop development are Lima Puluh Kota Regency, Pasaman Regency, Sijunjung Regency, South Solok Regency, and Tanah Datar Regency.