Small Parcels Research Articles

Counting Crofters: Digitised Census Enumerators Books as a New Source for the Later Nineteenth Century

This paper explores some of the ways in which access to the census enumerators books for later nineteenth century censuses, now digitised by the ‘Integrated Census Microdata’ project, can throw new light on a range of socio-demographic developments in the ‘Crofting Counties’ between the early 1850s and the early 1880s. Collating and mapping occupational titles by parish reveals major changes in the titles recorded for those farming small land parcels and in the spatial distributions and age profiles or those heading such households. The numbers of household heads reported as ‘Crofter’ markedly increased between 1851 and 1881 (including outside the ‘Crofting Counties’), as use of terms like ‘Tenant’ and ‘Lotter’ declined. ‘Cottar’, even in 1851 used in very small numbers in most places, was reported in 1881 in very limited areas, even where the estates still used it extensively. The number of households headed by ‘Fishermen’ increased greatly in some areas, notably in Lewis. At the same time, the numbers of men becoming heads of small agriculturalist households at younger ages fell markedly, while rising numbers headed households as ‘Fishermen’. Comparative analysis of four case study areas suggests that, when fishing and other incomes fell markedly in the early 1880s, inter-generational differences in access to headship of a croft may have been a significant stimulus to localised outbreaks of disturbance pressing for land reform.

Design and Fabrication of Solar Power Tiller

The present research focuses on the development of a solar-powered rotary tiller designed for primary and secondary soil cultivation. It explores the disparities between portable weeders and power tillers available in the Indian market. Various approaches to weed removal in crops are also examined. This study demonstrates that the majority of Indian farmers, particularly those with small-scale operations, can only afford portable weeders. Soil tillers and weeders are among the many agricultural mechanizations aimed at enhancing soil cultivation and weed management, particularly given that most farmers own small parcels of land. These tools notably reduce manual labor and are largely self-guided. The project operates on solar panels, utilizing their energy to power the machine, which operates the cutter or tiller. This innovation significantly saves time and expenses in field tasks, proving highly efficient for both tilling and weeding duties. Additionally, proposing the creation of high-capacity, energy-saving, adaptable machines, and integrated equipment aims to enhance labor efficiency, lower operational costs, improve punctuality, and render them suitable for customized leasing. Key Words: solar, power, tiller, weeder, farming, soil

Land Fragmentation and Heirs Property: Current Issues and Policy Responses

Land fragmentation continues to be a challenge throughout the world, the United States, and particularly in the rapidly growing Southeast, as well as every state with a metropolitan area that abuts rural lands. With a United States population expected to grow to more than 500 million by 2060, it will present exceptional challenges for planners and policy makers to preserve important agricultural lands for farms and forests to provide both food and fiber, as well as to provide a host of ecosystem services and enhance the quality of life for our growing population. These issues of fragmentation are extremely substantial for African American, other minority, and limited-income landowners in the U.S. South, who often lack wills and have lands that are broken up into small parcels, or have divided ownership rights in one parcel, when passed on to heirs. Existing efforts can be expanded to provide tools and incentives for the owners of hiers property and other working lands to preserve them, and state and municipal planners will need to promote development plans and practices thoughtfully and strategically in order to prevent the projected loss of nearly 18 million acres of working lands by the year 2040.

Crop type classification and spatial mapping in River Nile and Northern State, Sudan, using Sentinel-2 satellite data and field observation

Maintaining productive farmland necessitates precise crop mapping and identification. While satellite remote sensing makes it possible to generate such maps, there are still issues to resolve, such as how to choose input data and the best classifier algorithm, especially in areas with scarce field data. Accurate assessments of the land used for farming are a crucial part of national food supply and production accounting in many African countries, and to this end, remote sensing tools are being increasingly put to use. The aim of this study was to assess the potentiality of Sentinel-2 to distinguish and discriminate crop species in the study area and constraints on accurately mapping cropping patterns in the winter season in River Nile and Northern State, Sudan. The research utilized Sentinel-2 Normalized Different Vegetation Index (NDVI) at 10 m resolution, unsupervised and supervised classification method with ground sample and accuracy assessment. The results of the study found that the signatures of grain sorghum, wheat, okra, Vicia faba, alfalfa, corn, haricot, onion, potato, tomato, lupine, tree cover, and garlic have clear distinctions, permitting an overall accuracy of 87.38%, with trees cover, onion, wheat, potato, garlic, alfalfa, tomato, lupine and Vicia faba achieving more than 87% accuracy. Major mislabeling problems occurred primarily in irrigated areas for grain sorghum, okra, corn, and haricot, in wooded areas comprised of small parcels of land. The research found that high-resolution temporal images combined with ground data had potential and utility for mapping cropland at the field scale in the winter.

Images of Resistance

Barrio Logan, San Diego, California, contains Chicano Park, a small parcel of land claimed in 1970 by community members. The park has since been recognized for its numerous Chicano murals. Our article analyzes two murals that speak on the issue of gentrification in the community and the dispossession that it leads to. We employ Alvesson and Willmott’s theory on emancipation, and specifically the term “microemancipations,” to examine how the Chicano community in Barrio Logan resists dispossession. We complicate the notion of emancipation, showing how murals sometimes facilitate resistance and, at other times, impede successful emancipation.

Optimization of trucks and drones in tandem delivery network with drone trajectory planning

Drones are widely recognized as an efficient method for delivering small and lightweight parcels, such as medicines, to customers who are difficult to reach by logistic vehicles. Various studies and projects have been conducted to integrate drones into the traditional logistic service. In this approach, drones stay on trucks, take off from trucks at specific locations to make deliveries to customers, and return to the trucks to replenish and recharge. However, drone trajectories could overlap when multiple drones are used for delivery, which can lead to the risk of collisions. Consequently, the need to address the trajectory planning of drones becomes essential. This work introduces a novel truck-drone routing problem. The novelty presented by this problem lies in the integration of drone trajectory planning problems into the truck-drone routing to generate collision-free routes for drones. This concept has not been previously explored in the existing body of literature. The problem is formulated as a mixed-integer programming problem, and a matheuristic is proposed to solve it. Computational experiments show that the proposed matheuristic outperforms the commercial solver and genetic algorithm in terms of solution quality and search efficiency. Results demonstrate that the integrated truck-drone delivery mode has a lower operating cost than the mere truck mode, and including trajectory planning can prevent potential collisions.

Empowering Young Entrepreneurs: Practical Learning Initiatives in Primary Education

The output of this service shows that the lecture session succeeded in increasing students' understanding of the world of entrepreneurship, while the practice of making small parcels provided direct experience in applying these concepts. The exhibition of student work reveals originality in selecting local materials, helping the local economy, and exploring creative potential in business procedures. Apart from having a good influence on children, strong collaboration between schools, teachers and the surrounding community has also been successful. The complete study shows that the success of this service has helped encourage students' enthusiasm to continue exploring their entrepreneurial potential. The results of this service show that a holistic approach through a combination of lectures and practice of making micro parcels has a good impact on students' understanding of entrepreneurship. However, to ensure sustainability and sustainable growth, follow-up action is needed in the form of building an entrepreneurship program that is integrated into the curriculum and forming small groups to encourage student interest. Thus, this abstract illustrates the effectiveness of this service in arousing curiosity and entrepreneurial creativity at SDN Kuala Geulumpang students, with the aim of being able to make a good contribution to the development of their character and talents in the future.

Allelopathic interactions of brown mustard (Brassica juncea L.) with other aromatic plants during germination and seedling development

Organic compounds originating from plant metabolism and reaching the environment may inhibit or stimulate germination and early developmental processes of other plants grown in the vicinity. The aim of this study was to investigate the allelopathic interactions of brown mustard with four other aromatic plants (summer savoury, coriander, caraway and common sage). While health-promoting metabolites of brown mustard are thoroughly investigated and the plant receives an increasing interest from cultivators of aromatic and medicinal plants, its allelopathic interactions with other aromatic plants are poorly documented. The experiments were performed in closed germination vessels, under controlled conditions. The influence of the presence or absence of light, as well as of a sufficient or reduced water supply on the co-germination of brown mustard seeds with each of the other four aromatic plants was also evaluated. The presence of brown mustard reduced the germination velocity of caraway, coriander, sage and savoury. It inhibited stem elongation, but stimulated root growth of savoury. Shoot growth of coriander seedlings was enhanced by the vicinity of brown mustard plantlets. Caraway slowed down the germination of brown mustard seeds, but it stimulated root and stem elongation of the seedlings. The presence of savoury enhanced the fresh biomass production of brown mustard. Common sage inhibited root growth, but stimulated stem elongation of brown mustard plantlets. Most of the allelopathic effects were more pronounced under water shortage than under sufficient water supply. Positive interspecific influences on biomass production were, in most cases, more pronounced in the presence of light. The findings may contribute to optimization of brown mustard cultivation in small parcels, in the vicinity of other aromatic plants, some allelopathic interactions being able to stimulate growth and developmental processes in an environmental-friendly and cost-effective manner.

Crop Classification in Mountainous Areas Using Object-Oriented Methods and Multi-Source Data: A Case Study of Xishui County, China

Accurate crop mapping can represent the fundamental data for digital agriculture and ecological security. However, current crop classification methods perform poorly in mountainous areas with small cropland field parcel areas and multiple crops under cultivation. This study proposed a new object-oriented classification method to address this issue, using multi-source data and object features to achieve multi-crop classification in mountainous areas. Firstly, a deep learning method was employed to extract cropland field parcels in mountainous areas. Subsequently, the fusion of multi-source data was carried out based on cropland field parcels, while object features tailored for mountainous crops were designed for crop classification. Comparative analysis indicates that the proposed classification method demonstrates exceptional performance, enabling accurate mapping of various crops in mountainous regions. The F1 score and overall accuracy (OA) of the proposed method are 0.8449 and 0.8502, representing a 10% improvement over the pixel-based random forest classification results. Furthermore, qualitative analysis reveals that the proposed method exhibits higher classification accuracy for smaller plots and more precise delineation of crop boundaries. Finally, meticulous crop mapping of corn, sorghum, rice, and other crops in Xishui County, Guizhou Province, demonstrates the significant potential of the proposed method in crop classification within mountainous scenarios.

Synergistic exploitation of localized spectral-spatial and temporal information with DNNs for multisensor-multitemporal image-based crop classification

The challenge of performing efficient and reliable crop classification with multisensor multitemporal (MSMT) images in mixed land cover scenarios i.e. presence of small land parcels (area < 20,000-meter square) of crops and other land covers such as built-up or grasslands, is significant. Specially in countries (ex. India) where diverse crops are practiced in small land parcels. This challenge can be addressed if deep neural network (DNN) based models can exploit all three i.e. spatial, spectral, and temporal information of a crop, present in the MSMT images, efficiently and effectively. Therefore, this study presents a novel DNN based model that exploits all three information in a synergistic fashion to achieve improved crop classification. At first, the model increases the significance of local spectral information via a strategy that creates versions of spectral band set wherein neighbourhood of spectral bands is permuted. Then, the model utilizes three-dimensional convolutions, in a time-distributed fashion, to extract local spectral-spatial features. Finally, the model utilizes bidirectional long short-term memory or LSTM-RNNs to extract the temporal information embedded in the time-distributed feature-space created after the convolutions. The developed model is trained and evaluated on Sentinel-1 and Sentinel-2 MSMT data to achieve a 6-class classification including two major crops grown in the region. One of the proposed models namely Perm-3D-CRNN-v1 showed a 97.77 % overall accuracy on test samples and reflected satisfactory on quantitative analysis. The localized spectral-spatial convolutions created prominent class-specific features whereas the bidirectional information flow in the recurrent layer improved the exploitation of crop-phenology type features making the model perform efficiently.

Evaluating the potential of multi-temporal Sentinel-1 and Sentinel-2 data for regional mapping of olive trees

ABSTRACT Olives are a crucial economic crop in Mediterranean countries. Detailed spatial information on the distribution and condition of crops at regional and national scales is essential to ensure the continuity of crop quality and yield efficiency. However, most earlier studies on olive tree mapping focused mainly on small parcels using single-sensor, very high resolution (VHR) data, which is time-consuming, expensive and cannot feasibly be scaled up to a larger area. Therefore, we evaluated the performance of Sentinel-1 and Sentinel-2 data fusion for the regional mapping of olive trees for the first time, using the Izmir Province of Türkiye, an ancient olive-growing region, as a case study. Three different monthly composite images reflecting the different phenological stages of olive trees were selected to separate olive trees from other land cover types. Seven land-cover classes, including olives, were mapped separately using a random forest classifier for each year between 2017 and 2021. The results were assessed using the k-fold cross-validation method, and the final olive tree map of Izmir was produced by combining the olive tree distribution over two consecutive years. District-level areas covered by olive trees were calculated and validated using official statistics from the Turkish Statistical Institute (TUIK). The K-fold cross-validation accuracy varied from 94% to 95% between 2017 and 2021, and the final olive map achieved 98% overall accuracy with 93% producer accuracy for the olive class. The district-level olive area was strongly related to the TUIK statistics (R2 = 0.60, NRMSE = 0.64). This study used Sentinel data and Google Earth Engine (GEE) to produce a regional-scale olive distribution map that can be scaled up to the entire country and replicated elsewhere. This map can, therefore, be used as a foundation for other scientific studies on olive trees, particularly for the development of effective management practices.

The 3D Elliptical Parcel-In-Cell (EPIC) method

We present the three-dimensional version of the Elliptical Parcel-In-Cell (EPIC) method for the simulation of fluid flows and analogous continuum systems. The method represents a flow using a space-filling set of ellipsoidal parcels, which move, rotate and deform in the flow field. Additionally, parcels may carry any number of attributes, such as vorticity, density, temperature, etc, which generally evolve in time on the moving parcels. An underlying grid is used for efficiency in computing the velocity field from the interpolated vorticity field, and in obtaining parcel attribute tendencies. Mixing is enabled by permitting parcels to split when excessively deformed, and by merging very small parcels with the nearest other parcel. Several tests are provided which illustrate the behaviour of the method and demonstrate its effectiveness in modelling complex, buoyancy-driven turbulent fluid flows. The results are compared with a large eddy simulation (LES) and a direct numerical simulation (DNS) model.

Drone Stations-Aided Beyond-Battery-Lifetime Flight Planning for Parcel Delivery

This paper considers using drones to conduct the last-mile parcel delivery. To enable the beyond-battery-lifetime flight, drone stations are considered to replace or recharge the battery for drones. We focus on the flight planning problem with the goal of minimizing the total travel time from the depot to a customer, a key indicator of the quality of service. We investigate four typical ways for the drone to get extra energy at drone stations: 1) replacing the battery with a fresh one, 2) recharging the battery to the full capacity, 3) recharging the battery to the optimal level, and 4) recharging the battery to the optimal level accounting for the availability of drone stations (i.e., whether a drone station is occupied by other drones). While the first two scenarios can be formulated following the framework of integer linear programming, the last two scenarios turn into mixed-integer nonlinear programming problems. To address the later problems, we present a framework in which discretized state graphs are constructed first and then the optimal paths are found by graph searching algorithms. We propose a dynamic version of Dijkstra’s algorithm to deal with the unavailability issue of drone stations. The algorithm can quickly find the optimal flight path for a drone, and extensive computer-based experimental results have been presented to demonstrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Multi-rotary unmanned aerial vehicles (UAVs), also known as drones, have been regarded as a promising means to reshape future logistics. To save human labour and reduce cost, many giant logistics companies have been dedicated to developing various drones to deliver light and small parcels during the past decade. However, due to the limitation of payload, the battery capacity is constrained, which prevents drones from long-distance flights. Practitioners have tried the drone-vehicle collaboration method, but this still requires human labour to participate. In this paper, we present a framework where drones autonomously conduct long-distance delivery with the assistance of drone stations. It is worth pointing out that such a framework is not to replace the ground delivery method but to serve as an alternative to the ground counterpart for small and light parcels. A particular focus is on the flight planning from the depot to a destination, which includes not only a sequence of drone stations to stop at but also the corresponding rest time to recharge the battery. Several typical scenarios about battery recharging are discussed, and a dynamic version of Dijkstra’s algorithm is presented to deal with the challenging case where drone station resources are limited. The presented approach is able to find out the optimal flight plan quickly.

Assessing the Urban Vacant Land Potential for Infill Housing: A Case Study in Oklahoma City, USA

Vacant land in residual urban areas is a crucial resource to tackle the current climate and housing crises. In this study, we present the development of a geodatabase to determine the occurrence of vacant land in the urban core of Oklahoma City, USA (OKC), and assess its potential for infill housing. As a starting point, we define urban vacant land through a literature review. We present a description of the case study’s social and urbanistic context by highlighting its relevance to this study. We explain the methodology for the development of the geodatabase to quantify residual urban land in OKC’s urban core. We examine the spatial distribution and recurring characteristics of vacant parcels using QGIS, Python scripting for Rhinoceros 3D, and aerial imagery. We find that small parcels have higher vacancy rates than average-sized parcels and there is a correlation between higher vacancy rates and proximity to downtown and brownfields. Finally, we discuss the implications of the findings by assessing the urban vacant land potential for residential development and its contribution to OKC’s housing provision. Under all the proposed scenarios, the considered developable vacant land in the urban core could entirely fulfill the need for new housing units for the entire city.

The service points’ location and capacity problem

We study the design of a network of automatic parcel lockers to facilitate the last-mile delivery of small parcels where parcels are delivered via service points near their recipients’ home addresses. The recipients then pick up their parcels at convenient times. This method saves a substantial share of the handling and transportation costs associated with the parcel delivery process. The deployment of such a network requires decisions regarding the location and capacity of the service points. If a parcel has to be delivered through a service point with no remaining capacity, the parcel is sent directly to the recipient’s address at a higher cost, or its delivery is postponed. Hence, there is a trade-off between the fixed setup cost, the variable operational cost, and the quality of the service. In this study, we take a bottom-up approach to the problem. We start by analyzing the dynamics of a single service point and show how to calculate a function that maps the parameters of its environment to the expected number of parcels that will be rejected from service or postponed. We then embed these functions in a mathematical model that optimizes the configuration of the network while considering the trade-offs described above.

A continuum approximation approach to the depot location problem in a crowd-shipping system

Last-mile delivery in the logistics chain contributes to congestion in urban networks due to frequent stops. Crowd-shipping is a sustainable and low-cost alternative to traditional delivery but relies heavily on the availability of occasional couriers. In this work, we propose a crowd-shipping system that uses depots to improve accessibility for potential crowd-shippers to serve a large portion of the demand for small parcels. While small-scale versions of this problem have been recently addressed, scaling to larger instances significantly complexifies the problem. A heuristic approach based on continuum approximation is designed to evaluate the quality of a potential set of depots. By combining an efficient and accurate approximation method with a large neighborhood search heuristic, we can efficiently find a good set of depots, even for large-scale networks. The proposed methodology allows for heterogeneity among crowd-shippers and allows identifying the expected number of delivered parcels in every region, which can be used to enhance lower-level assignment decisions.A case study on the Washington DC network shows that depots are built at geographically central locations but most importantly at locations around popular origins for crowd-shippers. The optimal number of depots is mainly dependent on the marginal number of parcels that can be served by crowd-shippers from a specific depot, relative to the costs involved in opening that depot. The operational costs approximated by our continuum approximation approach deviate on average 2% from the actual operational costs using dynamic assignment strategies. For small instances, our algorithm finds better solutions than solving a discrete formulation using CPLEX, while being almost 200 times faster. For large instances, the discrete formulation cannot be constructed by CPLEX, whereas the CA-based approach finds good solutions within minutes. Finally, the results show that using CA-based strategies in all three layers of decision-making can improve overall performance by 15% compared to non-predictive strategies.

Digital piracy and counterfeiting in the conditions of digital transformation: analysis of the situation, trends, defense mechanisms

In the work, based on the analysis of statistical data, the study of international sources, the economic and legal problems of digital piracy and counterfeiting in the world, the EU and Ukraine in the conditions of digital transformation are investigated:state and dynamics, impact on business and economy, causes and main factors, problematic issues that arise in connection with this phenomenon, the practice and the organizational and economic mechanism of countermeasures in this area. The sale of counterfeit goods causes millions of jobs and lost profits and is currently the world's largest criminal enterprise. It is expected that by 2024, in the US alone, the volume of sales of counterfeit goods in electronic commerce will increase to 6 trillion dollars. The rating of the most active pirate countries is given, Ukraine is the place. Criminal networks take advantage of the lack of law enforcement in some states and adapt the distribution of their products using new means of communication (Internet, social networks, etc.), distribution channels (transit, free zones, small parcels) and consumer requests. In addition, the return on investment is high, and the imposed fines are low and do not have a deterrent effect on counterfeiters and sellers. It is concluded that counterfeiting and piracy are global problems that know no borders. They are too big to be solved by individual companies, sectors of the economy or governments. Therefore, it is important to develop and strengthen cooperation between these subjects. To ensure a successful fight against digital piracy, counterfeiting and money laundering, coordination between governments at the international, national and regional levels is essential. The private and public sectors should join forces in the fight against the trade in counterfeit and pirated products by organizing special cooperation units that will be responsible for collecting information on the movement of funds obtained from criminal activities and for exchanging information in the field of combating money laundering. The protection of IP rights in the conditions of martial law is extremely important, as these are Ukraine's international obligations regarding EU accession and the vital need to protect Ukraine's economic interests.

Mapping corpus callosum surface reduction in fetal alcohol spectrum disorders with sulci and connectivity-based parcellation.

Fetal alcohol spectrum disorders (FASD) range from fetal alcohol syndrome (FAS) to non-syndromic non-specific forms (NS-FASD) that are still underdiagnosed and could benefit from new neuroanatomical markers. The main neuroanatomical manifestation of prenatal alcohol exposure on developmental toxicity is the reduction in brain size, but repeated imaging observations have long driven the attention on the corpus callosum (CC), without being all convergent. Our study proposed a new segmentation of the CC that relies on both a sulci-based cortical segmentation and the "hemispherotopic" organization of the transcallosal fibers. We collected a monocentric series of 37 subjects with FAS, 28 with NS-FASD, and 38 with typical development (6 to 25 years old) using brain MRI (1.5T). Associating T1- and diffusion-weighted imaging, we projected a sulci-based cortical segmentation of the hemispheres on the midsagittal section of the CC, resulting in seven homologous anterior-posterior parcels (frontopolar, anterior and posterior prefrontal, precentral, postcentral, parietal, and occipital). We measured the effect of FASD on the area of callosal and cortical parcels by considering age, sex, and brain size as linear covariates. The surface proportion of the corresponding cortical parcel was introduced as an additional covariate. We performed a normative analysis to identify subjects with an abnormally small parcel. All callosal and cortical parcels were smaller in the FASD group compared with controls. When accounting for age, sex, and brain size, only the postcentral (η2 = 6.5%, pFDR = 0.032) callosal parcel and % of the cortical parcel (η2 = 8.9%, pFDR = 0.007) were still smaller. Adding the surface proportion (%) of the corresponding cortical parcel to the model, only the occipital parcel was persistently reduced in the FASD group (η2 = 5.7%, pFDR = 0.014). In the normative analysis, we found an excess of subjects with FASD with abnormally small precentral and postcentral (peri-isthmic) and posterior-splenial parcels (pFDR < 0.05). The objective sulcal and connectivity-based method of CC parcellation proved to be useful not only in confirming posterior-splenial damage in FASD but also in the narrowing of the peri-isthmic region strongly associated with a specific size reduction in the corresponding postcentral cortical region (postcentral gyrus). The normative analysis showed that this type of callosal segmentation could provide a clinically relevant neuroanatomical endophenotype, even in NS-FASD.

Romanya’da tarım sektörü ve arazi mülkiyet durumu (1990-2020).

Romanya 13,8 milyon hektar tarım arazisiyle Avrupa Birliği Ülkelerinin toplam tarım alanın % 7,2’sine sahiptir. Ülkede yaşanan siyasi rejim değişikleri ve Avrupa Birliği üyeliği, arazi mülkiyetinde yapısal değişime sebep oldu. 1990 sonrasında büyük çiftlik arazilerinin halka dağıtılması sonucu tarlalar küçük parsellere bölündü. Romanya’nın AB’ye katılım sürecinde ve 2007’de gerçekleşen tam üyelik sonrasında yapılan yasal düzenlemeler, çok uluslu şirketlerin kiralama ve satın almalar yoluyla büyük arazilere sahip olmalarını sağladı. 1990 ile 2020 yılları arasını kapsayan bu çalışmada, Romanya’daki arazi mülkiyetinin devlet, bireysel üretici ve tüzel kişiliğe sahip işletmeler şeklinde yıllara göre değişimi ortaya konulmaktadır. Mısır, buğday ve ayçiçeğinin ekim alanı, üretim miktarı ve verimliliğin yıllara göre değişimi, örnek teşkil etmesi adına tablolar şeklinde gösterilmektedir. AB ülkelerinde kullanılan NUTS sınıflandırması ile Romanya 8 bölgeye ayrılarak incelenmiştir. Kaynak olarak, FAOSTAT ve Romanya Ulusal İstatistik Ajansı esas alınmıştır. Arazi kullanımı verileri CORINE tarafından sağlanmış ve ARCMap ortamında oluşturulmuştur.

Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain)

Agricultural land abandonment (ALA) is becoming a growing phenomenon around the world that needs to be monitored and quantified. A massive abandonment of citrus orchards has been experienced in the last years in the Comunitat Valenciana (CV) region (Spain) driven by different socio-economic factors. Therefore, developing time and cost-efficient methods for monitoring ALA is a priority. Citrus are a perennial crop trees which make orchards have low spectral variation during the year. In the CV region, they are planted in relatively small parcels, thus creating a highly fragmented and heterogeneous landscape. This study proposes a machine learning-based classification framework that uses annual time series of spectral indices extracted from Sentinel-2 images to identify crop status at parcel level. The method is based on features extracted from the reconstructed OSAVI and NDMI time series used to train a Random Forest classifier. Then, a parcel-based classification is performed using the parcel boundaries and the probabilities of belonging to each category for the full pixels inside the boundaries. The research assessed the potential to identify three statuses of crops (non-productive, productive, and abandoned). Results on three different temporal and spatial datasets provided an overall accuracy ranging from 89 to 92 %, demonstrating the importance of multi-temporal data to identify the abandonment of perennial crops. Furthermore, we studied the ability of the model to be spatially and temporally transferred. Limitations to recall the abandoned parcels when using models trained in other areas or time periods are exposed, opening the way to model improvements.