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The current issues that provide a scientific basis for improving land management are: determining the relationship between the concepts of land protection and environmental protection, which also includes land as a component of the natural environment; substantiating the differentiation of norms of land and environmental law in a narrow sense with regard to land protection; establishing the limits of differentiation of legal norms governing land protection, depending on their legal regime. Purpose: to substantiate the differentiation of norms of land and environmental law in a narrow sense with regard to land protection; to identify the specifics of the methods of legal impact used in the legal regulation of land protection; to establish the relationship between the legal regime of land and obligations for its protection. Methods: empirical methods of comparison, description, interpretation; theoretical methods of formal and dialectical logic; specific scientific methods: legal-dogmatic and interpretation of legal norms. Results: the article reveals that the protection of lands from negative impact on the environment should constitute a subject of environmental law in a narrow sense, since in this case the negative impact is carried out on lands as a component of a complex environmental object, and the relevant legal requirements are conditioned by the type of activity resulting in negative impact, as well as by the nature of this negative impact. Rational use is ensured by compliance with the legal regime of a land parcel. The main elements of the legal regime of a land parcel are a meaningful reference point for determining measures aimed at restoring the disturbed state of lands.

The addition of transit routes transforms urban development by disrupting the existing equilibrium that land parcels have achieved over time and promotes revitalization. It is based on the relationships between land parcel variables and transit route characteristics, including feeder routes and road infrastructure. Traditional parametric methods for explaining this relationship have problems with multicollinearity and generalizability while non-parametric methods are not used with the multiple variables of both transit route and land parcel changes over time. This study applies the C5.0 decision tree algorithm, a non-parametric model that creates a decision tree with leaf nodes that predict the relationship. Using the BRT Lahore case study, the time series data of parcel variables in the 2 km circle of five transit stations before BRT 2010 and after BRT 2018, as well as transit route characteristics including feeder routes and road infrastructure, were collected and analyzed. The model identified eight important predictors and explained the relationship in the form of a flowchart. Property condition emerged as the strongest predictor, followed by property value, parking, population density, land use, building height, access routes, and distance from transit stations, in that order. The results show that well-developed transport infrastructure, parking spaces, and feeder routes enable sustainable urban transformation.

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an F1-score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework’s ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land.

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an F 1 -score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework's ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land. [ABSTRACT FROM AUTHOR]

Depending on the development of remote sensing technologies, data at various spatial, temporal and spectral resolutions are obtained and play a key role in Earth investigation. Land cover/use maps are also created using these data. These data play an important role as a base for determining land cover and land use classes, detecting changes, monitoring forest, agriculture and wetlands, as well as updating the LPIS data used by countries. As well as being reference data in the LPIS update, current land cover and land use classification results with satellite images also provide statistical information about which class has changed over the years and how much the class or classes need to be updated, and will also provide the planning of the update to be made on a national scale. In this study, land cover classification for 2020 covering Çivril-Baklan Plain, which is between the borders of Denizli Province (Turkey), Baklan, Çal and Çivril Districts, was performed. The LPIS classes of the study area drawn in 2015 and the land cover classes as a result of the 2020 classification were compared as a result of the classification process, and particularly the areas with change were referenced in the LPIS update. In the classification process, the open source Eo-Learn library, which uses machine learning and deep learning algorithms in remote sensing studies and Sentinel-2 images that can be accessed directly in this library were used. The open source Eo-Learn library has been preferred because it provides great convenience to users in the classification process with ready-made machine learning models and easy access to satellite images, image processing steps (cloud masking, calculate index, feature extraction, temporal interpolation) are carried out in a certain workflow. Instead of land cover classification from a single image, 17 different time (multi-temporal) images between 01.02.2020 and 31.11.2020 were used. In the classification process, 12 different features were used for the image taken on each date, including NDVI, NDWI, NDBI indices and Tasseled Cap transformations, as well as (Blue) B02, (Green) B03, (Red) B04, (NIR) B08, (SWIR-1) B11, (SWIR-2) B12 bands. In the classification process, the Light Gradient Boosting Machines (LightGBM) algorithm in the Eo-Learn library and the physical blocks produced within the scope of the Land Parcel Identification System (LPIS) project were used as ground truth data; arable land, bare land, forest, artificial surface, shrubland, tree crops and water classes were created. The classification results were evaluated using the K fold (k = 5) cross validation method, with F1 score, recall, and precision values calculated for each class, and the overall accuracy %92.2. When the classification result is compared with the LPIS data, it is seen that the arable land, shrub land, and water classes have changed. This study determined that these classes, in particular, need to be updated.

The addition of transit routes transforms urban development by disrupting the existing equilibrium that land parcels have achieved over time and promotes revitalization. It is based on the relationships between land parcel variables and transit route characteristics, including feeder routes and road infrastructure. Traditional parametric methods for explaining this relationship have problems with multicollinearity and generalizability while non-parametric methods are not used with the multiple variables of both transit route and land parcel changes over time. This study applies the C5.0 decision tree algorithm, a non-parametric model that creates a decision tree with leaf nodes that predict the relationship. Using the BRT Lahore case study, the time series data of parcel variables in the 2 km circle of five transit stations before BRT 2010 and after BRT 2018, as well as transit route characteristics including feeder routes and road infrastructure, were collected and analyzed. The model identified eight important predictors and explained the relationship in the form of a flowchart. Property condition emerged as the strongest predictor, followed by property value, parking, population density, land use, building height, access routes, and distance from transit stations, in that order. The results show that well-developed transport infrastructure, parking spaces, and feeder routes enable sustainable urban transformation. [ABSTRACT FROM AUTHOR]

The purpose of this study was to statistically test the effect of a parcel index - consisting of a combination of a soil index, a fertility index and a location index - intended to be used as a price-determining indicator for the sale of agricultural land at farmland markets. In the hedonic price model, the coefficients of the variables representing parcel index, population, gross return and parcel irrigation investment status were positive and statistically significant at a significance level of 0.01. There was a negative relationship between parcel size and sale price, which implied that the selling price per decare tends to decrease as the parcel size increases. In the study area, the prices of farmland with large parcel sizes and irrigation efficiency investments were higher. The population density in the region and gross income from farmlands were the major factors that generated demand for the land. The hedonic price model establishes an important link between the parcel index and the sale price of farmland. Based on this link, parcel index-based pricing can contribute significantly to the creation of a farmland market in Turkey.