Land Grading Research Articles

Presenting of a New Method for Land Grading

Presenting of a New Method for Land Grading

Integration of multimodal data for large-scale rapid agricultural land evaluation using machine learning and deep learning approaches

Rapid and accurate agricultural land evaluation provides essential guidance for the supervision and allocation of agricultural land resources; it also helps to ensure food security. Previous work has mainly evaluated the land quality at the county level by using field sampling data and based on a factor approach. However, it is difficult to achieve uniform, large-scale agricultural land evaluation via conventional approaches because of its spatial heterogeneity, as well as the large temporal and economic costs associated with data acquisition. In this study, we integrated publicly available multimodal data (i.e., satellite remote sensing, environmental, and socioeconomic data) into the Google Earth Engine (GEE) platform, selected the best indicators from each modality using the geodetector, on the basis of which different combinations of input models were designed. And then we developed machine learning (random forest, RF) and deep learning (deep neural network, DNN) models to evaluate the land quality in paddy field and dry land systems in 2013 throughout Guangdong Province, China. The results showed that the performance of our combination of variables decreased in the following order: multimodal > bimodal > unimodal. With the best input combination, the RF model (R2 = 0.91, RMSE = 97.56, and CCC = 0.95) outperformed the DNN model (R2 = 0.89, RMSE = 108.72, and CCC = 0.94) in terms of predicting the quality of paddy field. The RF model (R2 = 0.90, RMSE = 104.27, and CCC = 0.95) also outperformed the DNN model (R2 = 0.86, RMSE = 124.38, and CCC = 0.93) in terms of predicting the quality of dry land. The agricultural land quality estimates obtained using the RF and DNN models were more accurate for paddy field than for dry land systems because of greater land quality homogeneity in paddy fields. This research proposed a simple, low-cost for rapid and accurate agricultural land evaluation at the provincial scale using publicly available multimodal data, which can help to achieve control of the agricultural land grade at multiple spatial and temporal scales.

Evaluation and Analysis of Grain Production Capacity of High-standard Farmland Construction

High-standard farmland construction is an important way to protect the quality of cultivated land and improve grain production capacity in the new era, and also an important starting point for the practical implementation of the new urbanization construction and rural revitalization strategy. The evaluation and identification of grain production capacity depends on the results of farmland grade evaluation and demonstration, while the grading factors selected for designated crops, such as effective soil layer thickness (cm), surface soil texture, soil salinization degree, soil organic matter content (g/kg), drainage conditions, terrain slope, irrigation guarantee rate and irrigation water source, have a significant impact on farmland grade evaluation. This paper analyzes the new grain production capacity generated by the construction of high standard farmland in a county. After the construction of high standard farmland project, the cultivated land grade in the project area can be increased by 1 grade, and the new grain production capacity is estimated to be 3000 tons. The research method can provide necessary reference for the implementation of similar projects.

Analysis of Temporal and Spatial Variation Characteristics of TPP and Yield Gap of Agricultural Land Grading Crops, Using Hohhot City in Inner Mongolia as an Example

Analysis of Temporal and Spatial Variation Characteristics of TPP and Yield Gap of Agricultural Land Grading Crops, Using Hohhot City in Inner Mongolia as an Example

Selection of Optimum Land Leveling Design Method for Surface Irrigation

Land leveling or land grading of surface irrigated fields improve irrigation water distribution and application efficiencies, conserve water and increases crop productivity. Land formation for irrigation face many constraints (ensuring proper slopes, prevention of flood, ensuring canal water command over the field, optimizing earthwork, minimizing truck travel distances, proper equipment utilization). Design engineers traditionally, perform site formation design manually by plane shape, least squire or linear programming methods. Such methods are with different characteristics. The main objective this study is to select and compare performance of these three design methods for proper land leveling design. Consequently, the basic theory of these alternative design methods are reviewed and their performance using data surveyed from five fields in Khartoum North-Sudan, each with different soil surface topographic configurations, is analyzed. The statistical analysis revealed that the linear programming method is the most appropriate design method. Employing the linear programming design method revealed that design slopes in row and cross row directions are within the acceptable range (0.1 to 0.5, the ratio of Cut/fill volumes is within the recommended range (1.1 to 1.3), uniformity of distribution of design elevations of grid points are acceptable and within the target limits (80%), while their deviation is at 80% of grid points around the mean before leveling.

Forecasting The Development of IKN (New National Capital) in Sustainable Development, Indonesia

This study aims to describe related forecasting of New National Capital (IKN) development in sustainable development. This research uses a literature study that focuses on media and reports. The following are CNN Indonesia, Tempo, Jawapos, and reports from Bappenas, State Secretariat, and DPR. Go.id. The results of this study indicate that the IKN development forecast is formulated for smart transportation, smart grid, and green building and the development process must meet the principles of justice, participation, and gender equality (especially for women) which are important foundations in sustainable development. In addition, there are several challenges faced by IKN in the future based on the findings in the mass media. First, integration is needed based on basic conditions where all design and construction must be based on current topographic and geological conditions. Second, land grading and all infrastructure plans must refer to drainage plans to prevent flood disasters. Third, coordination of underground infrastructure is required. According to Takehiro, one thing to pay attention to is securing the right of way (ROW) area, especially for manholes and larger structures such as sewer lift pumps. Fourth, it is necessary to prepare space to ensure the accessibility of future expansion. IKN development must also pay attention to economic, social, and ecological impacts. This is because development tends to only think about infrastructure and large-scale land clearing with economic interests, which fail.

Farmland quality assessment using deep fully convolutional neural networks

Farmland is the cornerstone of agriculture and is important for food security and social production. Farmland assessment is essential but traditional methods are usually expensive and slow. Deep learning methods have been developed and widely applied recently in image recognition, semantic understanding, and many other application domains. In this research, we used fully convolutional networks (FCN) as the deep learning model to evaluate farmland grades. Normalized difference vegetation index (NDVI) derived from Landsat images was used as the input data, and the China National Cultivated Land Grade Database within Jiangsu Province was used to train the model on cloud computing. We also applied an image segmentation method to improve the original results from the FCN and compared the results with classical machine learning (ML) methods. Our research found that the FCN can predict farmland grades with an overall F1 score (the harmonic mean of precision and recall) of 0.719 and F1 score of 0.909, 0.590, 0.740, 0.642, and 0.023 for non-farmland, level I, II, III, and IV farmland, respectively. Combining the FCN and image segmentation method can further improve prediction accuracy with results of fewer noise pixels and more realistic edges. Compared with conventional ML, at least in farmland evaluation, FCN provides better results with higher precision, recall, and F1 score. Our research indicates that by using remote sensing NDVI data, the deep learning method can provide acceptable farmland assessment without fieldwork and can be used as a novel supplement to traditional methods. The method used in this research will save a lot of time and cost compared with traditional means.

Multi-objective planning for food production in a mountainous region: Strategic land utilization for meeting food demand and economic revitalization

• Multi-objective planning leads to increased food production in a mountainous region • Land grading criteria are used to conserve crop diversity with limited land • Land accessibility analysis suggests potential for expandable crop production • Surplus horticultural production showed immense economic revitalization potential The mountainous landscapes and significant decline of predominate industries have made the North Central Appalachian (NCA) region vulnerable to economic shocks. Given the rugged terrain that hinders large-scale industrialization, community-supported food production can be a sustainable solution to help meet food demand and revitalize regional economy. This study evaluated the potential of strategic utilization of arable land in a mountainous region to increase niche crops production and revenue. Using West Virginia as a study area, current food production shortages were estimated to be -47% to -100% for all 30 crops. A novel multi-objective planning function that considers land slope, crop diversity, yield and sale price was developed and applied to reallocate existing and expandable arable lands for increased food production. Accessibility, a numeric measure determined by the land slope and traversable road networks, was used to delineate availability of arable lands, and the accessibility values (0.816-0.999) of the expandable arable land suggests high accessibility. Results showed that demand for horticultural crops can be extensively met (potential/demand = ∼39) by restructuring the cropland; whereas field crop gaps can be slightly ( i.e ., 0.6%) reduced with the addition of expandable land. The analyses yielded a surplus production of horticultural foods while increasing crop diversity, and showed immense potential for economic revitalization. The methodology developed in this study and the results could be used to leverage regional policymaking to significantly reduce food shortage and to enhance economic revival in the NCA and similar regions.

Discussion on the Theory and Method of Agricultural Land Grading

Discussion on the Theory and Method of Agricultural Land Grading

Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction.

The integrated use of remote sensing technology and machine learning models to evaluate cultivated land quality (CLQ) quickly and efficiently is vital for protecting these lands. The effectiveness of machine-learning methods can be profoundly influenced by training samples. However, in the existing research, samples have mainly been constructed by random point (RPO). Little attention has been devoted to the optimization of sample construction, which may affect the accuracy of evaluation results. In this study, we present two optimization methods for sample construction of random patch (RPA) and area sequence patch (ASP). Differing from RPO samples, it aims to include cultivated land area and its size into sample construction. Based on landsat-8 Operational Land Manager images and agricultural land grading data, the proposed sample construction methods were applied to the machine learning model to predict the CLQ in Dongtai City, Jiangsu Province, China. Four machine learning models (the backpropagation neural network, decision tree, random forest (RF), and support vector machine) were compared based on RPO samples to determine the accurate evaluation model. The best machine learning model was selected to compare RPA and ASP samples with RPO samples. Results determined that the RF model generated the highest accuracy. Meanwhile, a high correlation was noted between the cultivated land area and CLQ. Thus, incorporating cultivated land area in the sample construction attributes can improve the prediction accuracy of the model. Among the three sample construction methods, the ASP yielded the highest prediction accuracy, indicating that the use of a large, cultivated land patch as the sample unit can further elevate the model performance. This study provides a new sample construction method for predicting CLQ using a machine learning model, as well as providing a reference for related research.

A new approach with AHP for land grading in land consolidation projects

A new approach with AHP for land grading in land consolidation projects

Evaluation of Flood Mitigation Effectiveness of Nature-Based Solutions Potential Cases with an Assessment Model for Flood Mitigation

In recent years, climate change has been widely discussed around the world. The Intergovernmental Panel on Climate Change (IPCC) published the Sixth Assessment Report (AR6) in 2021, which stated that with the intensification of global warming, heavy rainfalls are becoming more severe and frequent. Economic development in recent years has also caused the proportion of impervious areas in urban regions to increase with the advancement of urbanization. When the two aforementioned factors are coupled together, the result is faster surface runoff speeds and reduced infiltration rates, which in turn result in worse flooding. Thus, water disaster mitigation is becoming a topic of great importance to developed and developing countries. This study examined five Nature-based Solutions (NbS) cases (A, B C, D, E) for the Nangang river in Taiwan. Case A is to design levees with a 100-year return period flood design standard. Under steady flow conditions, floods can be smoothly discharged downstream without any significant inundation in most situations. Case B and C used gabions with a 10-year return period flood design standard and discontinuous levees with a 25-year return period flood design standard, respectively. Though neither case is as effective in flood mitigation, both cases B and C can still reduce inundation from the flooding disaster relatively well. Case D is to dredge local areas of the main channel, but the steady flow simulation showed little flood mitigation effect. Case E is the implementation of “Room for the River”, and employs main channel dredging and floodplain land grading to increase flood conveyance capacity. Case E provides good flood mitigation.

MAPPING OF CONSERVATION AREA PLAN IN NORTH POLOMBANGKENG DISTRICT, TAKALAR REGENCY

North Polombangkeng District is one of the districts in Takalar Regency, South Sulawesi, where 44% of the total land area of 9495 ha out of 21225 ha are steeply sloped areas. Therefore we need a policy direction on the arrangement of the use of conservation areas to support the sustainability of the existing environment in the form of mapping conservation area plans. This study uses a soil survey method and evaluation of land capability. Physical Factors the environment is a limiting factor in determining land use policy, namely slope factors, soil sensitivity to erosion, and rainfall intensity. The research shows that Barugayya Village, Towata Village, and Kampung Baru Village are included in class IV land grade classification and Ko'mara Village in Class VI. At the same time, the Scoring Assessment of Physical Environmental Factors in Forest Areas has a range of values ranging from 160 to 215, the area of which is planned to be a conservation area. Determination of Conservation Area refers to the function criteria of forest area based on the scoring System of the Minister of Agriculture Regulation in Minister of Agriculture Decree No. 837/Kits/Um/11/80.

The Potential Supply and Demand of Farmers’ Land Contract Rights-Based on 697 Households in Four Provinces of China

A new urbanization and rural revitalization strategy has been implemented in China over a number of years, under which farmers’ land contract rights (LCRs) flow inevitably through various means. The practice in reform pilot areas indicates that government funds cannot meet all the needs, so exploring market-based LCR payout paths is important for rural land tenure system reform. The purpose of this study is to answer questions such as the following: How would farmers respond if they were allowed to trade LCRs? Is there an equilibrium point between the potential supply and demand of LCRs? Which factors would affect the potential supply and demand of LCRs? In this study, 697 valid questionnaires from Ningxia, Hebei, Henan, and Shandong provinces, China, were used for analysis by the multiple bounded discrete choice (MBDC) method and MBDC-Tobit model. The results show that there is a potential market among rural collective households in China, with an equilibrium price of ¥27,800/mu ($59,714.4/ha), and a proportion of farmers who are willing to buy or sell LCRs is around 10.0%. The factors affecting the potential supply and demand of LCRs include land grade, average agricultural income per unit, total money to buy urban houses and cars, age, number of household members with a college education or above, and risk appetite. If the institutional barriers that hinder LCR transactions were eliminated, the potential supply and demand of LCRs would be matched, and the market would provide funds for next-stage reforms.

Comprehensive Evaluation of Cultivated Land Quality at County Scale: A Case Study of Shengzhou, Zhejiang Province, China.

The existing evaluation system of cultivated land quality mainly considers the natural quality and utilisation conditions, but without sufficient emphasis on ecological environment, which can’t meet the requirements of the trinity pattern protection policy. This study, using GIS spatial analysis and multifactor comprehensive evaluation method, constructed a comprehensive evaluation index system, and applied it in Shengzhou. The results show that: (1) under the comprehensive evaluation system, the quality of cultivated land was classified into five levels and revealed normal distribution with the third level cultivated land area as the peak, successive reduction to the two poles, and the overall quality was good relatively; (2) A close relationship was observed between the quality grade of cultivated land and the landform, the valley plain with highest cultivated land quality was the main grain-producing areas. However, the cultivated land in mountainous areas was poor relatively, and vulnerable to geological disasters; (3) The quality grade of cultivated land was related to economical activities directly. The high-quality cultivated land resources made these towns the main grain-producing areas with many agricultural industries distribution. This study provided a new assessment approach that can support cultivated land grading, quality improvement, and sustainable usage, as well as providing a reference for related research and application.

Research and architectural conservation in Marina el-Alamein in 2018 (Polish–Egyptian Conservation Mission)

Activities undertaken by the Polish–Egyptian Conservation Mission to Marina el-Alamein in 2018 comprised research and conservation mainly in the public district of the ancient town and, additionally, in private houses. Work focused foremost on research and presentation of remains of two streets, running east and south of the southeastern corner of the main town square, and the adjoining monuments. Research and conservation continued also on the remains of public Roman baths dating from the 1st to the 3rd century AD, located in the area south of the square. Maintenance conservation was carried out in Houses H21c and H1 and in the ancient town center. Land grading to enhance exhibition value and ensure rainwater drainage was carried out in some areas.

Use of 3D Total Station and Autocad Civil for Grading Planning

This study was structured with the aim of carrying out topographic measurements and analyzing grading planning in the API Madiun campus area. The study was conducted using a Total Station measurement tool and a 3D Autocad Civil 3D imaging tool. Surveyor personnel used in carrying out measurements are surveyors who are trained using Total Station equipment. The study produced ten benchmark point (BM) locations as a measurement reference in areas within the Madiun API land. The topographic analysis results from the contour map identified the Madiun API land area as relatively flat with the difference between the lowest and highest elevation of 5.132 m with an average slope of 3.32%. The grading plan shows that there are two main areas that require landfills with the total pile volume requirement of 8,281.4 m3. Evaluation of the implementation of measurements shows the need for supervision and evaluation so as not to make mistakes that can occur by the measurement implementer. The use of Autocad Civil 3D software tools facilitates and accelerates the process of depicting engineering and land grading planning. Measurement with Total Station and depiction with Autocad Civil 3D needs to be optimized by integration into the lecture syllabus in order to become one of the soft skill competencies possessed by graduates of Building Engineering and Railways of API Madiun.

Characterization of the 3D structure of a cultivated land surface and its influence on wheat seedlings growth using Kinect

The quality of wheat emergence has a significant impact on the subsequent growth and development of seedlings. The structure of cultivated land surfaces is an important factor influencing wheat seedlings growth. However, few studies have investigated this. In this study, three-dimensional structural parameters of cultivated land surfaces were collected using 3D imaging equipment, and the effects of different grades of lands on the emergence and growth of wheat were evaluated. The evaluation criteria for the soil blocks was designed according to the ISO-25178-2 standard, and the wheat emergence rate, speed, tillering capacity per plant, dry weight per plant, and final yields of different grades of cultivated land were investigated. The results indicated that the three-dimensional information obtained through the Kinect sensors was reliable. The deviation of measured values from the factual values was trivial. The value of R2 was greater than 0.99**. The value of RMSE was less than 2 mm. These results describe a method for obtaining three-dimensional structures of land surfaces using 3D cameras and the evaluation of wheat emergence capacity. It can be used as a reference for obtaining three-dimensional cultivated land structures or other similar objects.

A large-scale environmental flow experiment for riparian restoration in the Colorado River Delta

Managing streamflow is a widely-advocated approach to provide conditions necessary for seed germination and seedling establishment of trees in the willow family (Salicaceae). Experimental flow releases to the Colorado River delta in 2014 had a primary objective of promoting seedling establishment of Fremont cottonwood (Populus fremontii) and Goodding's willow (Salix gooddingii). We assessed seed germination and seedling establishment of these taxa as well as the non-native tamarisk (Tamarix spp.) and native seepwillow shrubs (Baccharis spp.) in the context of seedling requirements and active land management (land grading, vegetation removal) at 23 study sites along 87 river km. In the absence of associated active land management, experimental flows to the Colorado River delta were minimally successful at promoting establishment of new woody riparian seedlings, except for non-native Tamarix. Our results suggest that the primary factors contributing to low seedling establishment varied across space, but included low or no seed availability in some locations for some taxa, insufficient soil moisture availability during the growing season indicated by deep groundwater tables, and competition from adjacent vegetation (and, conversely, availability of bare ground). Active land management to create bare ground and favorable land grades contributed to significantly higher rates of Salicaceae seedling establishment in a river reach with high groundwater tables. Our results provide insights that can inform future environmental flow deliveries to the Colorado River delta and its ecosystems and other similar efforts to restore Salicaceae taxa around the world.

A Simple Method for Land Grading Computations and its Comparison with Genetic Algorithm (GA) Method

A Simple Method for Land Grading Computations and its Comparison with Genetic Algorithm (GA) Method