Статті в журналах: "Monitoring of Land cover/Land use"

1

Guliyeva, S. H. "LAND COVER / LAND USE MONITORING FOR AGRICULTURE FEATURES CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 61–65. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-61-2020.

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Abstract. Remote sensing applications are directed to agricultural observation and monitoring. It has been huge of scientific papers are dedicated to the research of the contribution of remote sensing for agriculture studies. There are several global challenges needed to be considered within agriculture activities. It can be embraced by the main agriculture sector facing the obstacles impacting the production and productivity of the sector. These are the following options that can be pointed out: biomass and yield estimation; vegetation vigor and drought stress monitoring; assessment of crop phenological development; crop acreage estimation and cropland mapping; and mapping of disturbances and Land Use/Land Cover changes. In this study has been undertaken the realization of satellite-based Land Use/Land Cover monitoring based on various optical satellite data. It has been used satellite images taken from satellites AZERSKY, RapidEye, Sentinel-2B and further processed for Land Use/Land Cover classification. Following the complex approach of the supervised and unsupervised classification, the methodology has been used for satellite image processing. As the main satellite imagery for monitoring crop condition were AZERSKY taken during the growing season, from May to June of 2019 year. The study area was some part of the Sheki region, which covers the central part of the southern slope of the Greater Caucasus Mountain Range within Azerbaijan Republic. In this research work satellite imagery processing and mapping has been carried out on the basis of software application of ArcGIS Pro 2.5.

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2

Cieślak, Iwona, Karol Szuniewicz, Katarzyna Pawlewicz, and Szymon Czyża. "Land Use Changes Monitoring with CORINE Land Cover Data." IOP Conference Series: Materials Science and Engineering 245 (October 2017): 052049. http://dx.doi.org/10.1088/1757-899x/245/5/052049.

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3

Erickson, Donna L. "Rural land use and land cover change." Land Use Policy 12, no. 3 (July 1995): 223–36. http://dx.doi.org/10.1016/0264-8377(95)00005-x.

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4

Tol, Richard S. J. "Dynamic World: Land-Cover and Land-Use Change." Environmental Science & Policy 7, no. 1 (February 2004): 77. http://dx.doi.org/10.1016/j.envsci.2003.10.001.

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5

Bektas Balcik, F., and A. Karakacan Kuzucu. "DETERMINATION OF LAND COVER/LAND USE USING SPOT 7 DATA WITH SUPERVISED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W1 (October 26, 2016): 143–46. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w1-143-2016.

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Land use/ land cover (LULC) classification is a key research field in remote sensing. With recent developments of high-spatial-resolution sensors, Earth-observation technology offers a viable solution for land use/land cover identification and management in the rural part of the cities. There is a strong need to produce accurate, reliable, and up-to-date land use/land cover maps for sustainable monitoring and management. In this study, SPOT 7 imagery was used to test the potential of the data for land cover/land use mapping. Catalca is selected region located in the north west of the Istanbul in Turkey, which is mostly covered with agricultural fields and forest lands. The potentials of two classification algorithms maximum likelihood, and support vector machine, were tested, and accuracy assessment of the land cover maps was performed through error matrix and Kappa statistics. The results indicated that both of the selected classifiers were highly useful (over 83% accuracy) in the mapping of land use/cover in the study region. The support vector machine classification approach slightly outperformed the maximum likelihood classification in both overall accuracy and Kappa statistics.

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6

Hansen, Matthew C., Peter V. Potapov, Amy H. Pickens, Alexandra Tyukavina, Andres Hernandez-Serna, Viviana Zalles, Svetlana Turubanova, et al. "Global land use extent and dispersion within natural land cover using Landsat data." Environmental Research Letters 17, no. 3 (March 1, 2022): 034050. http://dx.doi.org/10.1088/1748-9326/ac46ec.

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Abstract The conversion of natural land cover into human-dominated land use systems has significant impacts on the environment. Global mapping and monitoring of human-dominated land use extent via satellites provides an empirical basis for assessing land use pressures. Here, we present a novel 2019 global land cover, land use, and ecozone map derived from Landsat satellite imagery and topographical data using derived image feature spaces and algorithms suited per theme. From the map, we estimate the spatial extent and dispersion of land use disaggregated by climate domain and ecozone, where dispersion is the mean distance of land use to all land within a subregion. We find that percent of area under land use and distance to land use follow a power law that depicts an increasingly random spatial distribution of land use as it extends across lands of comparable development potential. For highly developed climate/ecozones, such as temperate and sub-tropical terra firma vegetation on low slopes, area under land use is contiguous and remnant natural land cover have low areal extent and high fragmentation. The tropics generally have the greatest potential for land use expansion, particularly in South America. An exception is Asian humid tropical terra firma vegetated lowland, which has land use intensities comparable to that of temperate breadbaskets such as the United States’ corn belt. Wetland extent is inversely proportional to land use extent within climate domains, indicating historical wetland loss for temperate, sub-tropical, and dry tropical biomes. Results highlight the need for planning efforts to preserve natural systems and associated ecosystem services. The demonstrated methods will be implemented operationally in quantifying global land change, enabling a monitoring framework for systematic assessments of the appropriation and restoration of natural land cover.

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7

Y. Jamal, Assist Prof Dr Saleem. "Use of Remote Sensing and Geographic Information System for the Classification of Agricultural Land Uses and Land Cover in the Al-Sad Al-Adhim sub District – Iraq." ALUSTATH JOURNAL FOR HUMAN AND SOCIAL SCIENCES 225, no. 2 (September 1, 2018): 245–73. http://dx.doi.org/10.36473/ujhss.v225i2.151.

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Land use refers to the human activity associated with a particular area of land. The land cover refers to the pattern of appearances located on the surface of the earth. Survey, inventory, monitoring and classification of land use and land cover are a fundamental step in the land use planning process, in evaluating and comparing alternatives and in choosing the best and sustainable use of land for development, accomplishment economic and social well-being. Remote sensing and Geographic Information System provided advantages that conventional methods could not provide for surveys and monitoring of natural and human resources, and classification of agricultural land uses and land cover in the area of the Al-Sad Al-Adhim sub District – Iraq. Depending on the Anderson system and others to classify land uses and land cover, through the integration of digital interpretation with the use of Digital Image Processing (ERDAS IMAGINE) software, and visual interpretation using ArcGIS software. Classification of agricultural land use and land cover up to the third level, with over all accuracy of the map 90%. the percentage distribution of the areas shows that the agricultural lands ranked first and occupy 52%, then grassland occupies 19%, barren land is occupied 17%, urban areas and built up occupy 9% water is ranked last occupy 3% of the total area of the study area.

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8

Dero, Kambo, Wakshum Shiferaw, and Biruk Zewde. "Urban induced land use land cover changes in upper Deme watershed, Southwest Ethiopia." Journal of Degraded and Mining Lands Management 9, no. 1 (October 1, 2021): 3045–53. http://dx.doi.org/10.15243/jdmlm.2021.091.3045.

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The study was aimed to assess urban induced land use land cover changes in the upper Deme watershed. Three satellite images of 1986, 2002, and 2019 were analyzed by ArcGIS and processed by supervised classification. Land use land cover change in the watershed increased for settlement, bare land, and croplands in the period 1986-2019 by 56.6%, 53%, and 0.25%, respectively. However, the land use land cover change in the watershed decreased for a water body, forest, and grassland by 65%, 57.7%, and 7%, respectively. These enforced to change the work habit and social bases. Out of converted lands, during 1986-2002, 34.9%, 53%, 18%, 40.9%, and 10.6% of bare land, cropland, forest land, grassland, and water bodies, respectively, in the upper Deme watershed were changed into settlement areas. During 2002-2019, 30.7%, 36.8%, 26.9%, 66%, and 33.3% of bare land, cropland, forest land, grassland, and water bodies, respectively, were changed into settlement areas. This shows urbanization results in a different change in economic, social, land use land cover, and watershed management activities in the upper Deme watershed.

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9

Mahmood, Rezaul, Roger A. Pielke, and Clive A. McAlpine. "Climate-Relevant Land Use and Land Cover Change Policies." Bulletin of the American Meteorological Society 97, no. 2 (February 1, 2016): 195–202. http://dx.doi.org/10.1175/bams-d-14-00221.1.

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Abstract Both observational and modeling studies clearly demonstrate that land-use and land-cover change (LULCC) play an important biogeophysical and biogeochemical role in the climate system from the landscape to regional and even continental scales. Without comprehensively considering these impacts, an adequate response to the threats posed by human intervention into the climate system will not be adequate. Public policy plays an important role in shaping local- to national-scale land-use practices. An array of national policies has been developed to influence the nature and spatial extent of LULCC. Observational evidence suggests that these policies, in addition to international trade treaties and protocols, have direct effects on LULCC and thus the climate system. However, these policies, agreements, and protocols fail to adequately recognize these impacts. To make these more effective and thus to minimize climatic impacts, we propose several recommendations: 1) translating international treaties and protocols into national policies and actions to ensure positive climate outcomes; 2) updating international protocols to reflect advancement in climate–LULCC science; 3) continuing to invest in the measurements, databases, reporting, and verification activities associated with LULCC and LULCC-relevant climate monitoring; and 4) reshaping Reducing Emissions from Deforestation and Forest Degradation+ (REDD+) to fully account for the multiscale biogeophysical and biogeochemical impacts of LULCC on the climate system.

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10

Treitz, Paul. "Remote sensing for mapping and monitoring land-cover and land-use change." Progress in Planning 61, no. 4 (May 2004): 267. http://dx.doi.org/10.1016/s0305-9006(03)00062-x.

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11

Tsai, Yu Hsin, Douglas Stow, Li An, Hsiang Ling Chen, Rebecca Lewison, and Lei Shi. "Monitoring land-cover and land-use dynamics in Fanjingshan National Nature Reserve." Applied Geography 111 (October 2019): 102077. http://dx.doi.org/10.1016/j.apgeog.2019.102077.

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12

Jamali, A., and A. Abdul Rahman. "EVALUATION OF ADVANCED DATA MINING ALGORITHMS IN LAND USE/LAND COVER MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 283–89. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-283-2019.

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Abstract. For environmental monitoring, land-cover mapping, and urban planning, remote sensing is an effective method. In this paper, firstly, for land use land cover mapping, Landsat 8 OLI image classification based on six advanced mathematical algorithms of machine learning including Random Forest, Decision Table, DTNB, Multilayer Perceptron, Non-Nested Generalized Exemplars (NN ge) and Simple Logistic is used. Then, results are compared in the terms of Overall Accuracy (OA), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for land use land cover (LULC) mapping. Based on the training and test datasets, Simple Logistic had the best performance in terms of OA, MAE and RMSE values of 99.9293, 0.0006 and 0.016 for training dataset and values of 99.9467, 0.0005 and 0.0153 for the test dataset.

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13

Guo, Andong, Yuqing Zhang, and Qing Hao. "Monitoring and Simulation of Dynamic Spatiotemporal Land Use/Cover Changes." Complexity 2020 (June 27, 2020): 1–12. http://dx.doi.org/10.1155/2020/3547323.

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Changes in land use/cover are among the most prominent impacts that humans have on the environment. Therefore, exploring land use/cover change is of great significance to urban planning and sustainable development. In this study, we preprocessed multiperiod land use and socioeconomic data, combined with spatial zoning, multilayer perception (MLP) artificial neural network, and Markov chain (MC), to construct a cellular automaton model of spatial zoning. Moreover, with the help of ArcGIS 10.2 and TerrSet 18.07 software, we explore the current status of land use and predict future changes. The results showed that drastic changes have occurred among different land use classes in Jinzhou District over the past 13 years owing to the impact of economic development and reclamation projects. Construction land, arable land, and waters have changed by +85.09, −24.42, and −23.62 km2, respectively. By comparing the FoM and Kappa coefficients, we concluded that the prediction accuracy of partitioned MLP-MC is better than that of unpartitioned MLP-MC. Therefore, using the spatial zoning approach to identify the conversion rules among land use classes in different zones can more effectively predict future land use changes and provide a reference for urban planning and policy making.

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14

Rogan, John, and DongMei Chen. "Remote sensing technology for mapping and monitoring land-cover and land-use change." Progress in Planning 61, no. 4 (May 2004): 301–25. http://dx.doi.org/10.1016/s0305-9006(03)00066-7.

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15

Esmail, Mohammed, ALI Masria, and Abdelazim Negm. "Monitoring Land Use/Land Cover Changes Around Damietta Promontory, Egypt, Using RS/GIS." Procedia Engineering 154 (2016): 936–42. http://dx.doi.org/10.1016/j.proeng.2016.07.515.

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16

Brown, D. G., B. C. Pijanowski, and J. D. Duh. "Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA." Journal of Environmental Management 59, no. 4 (August 2000): 247–63. http://dx.doi.org/10.1006/jema.2000.0369.

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17

Çağlıyan, Ayşe, and Dündar Dağlı. "Monitoring Land Use Land Cover Changes and Modelling of Urban Growth Using a Future Land Use Simulation Model (FLUS) in Diyarbakır, Turkey." Sustainability 14, no. 15 (July 27, 2022): 9180. http://dx.doi.org/10.3390/su14159180.

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Land use and land cover (LULC) change corresponds to the greatest transformations that occur on the earth’s surface under physical, human and socio-economic geographical conditions. Increasing demand for residential and agricultural lands has been transforming all land classes and this should be investigated in the long term. In this study, we aim to determine LULC change and land use simulation in Diyarbakır with Geographical Information System (GIS) and Remote Sensing (RS) techniques. For this purpose, satellite images from 1984, 2002, and 2020 were classified at different levels by an object-based classification method. Accuracy assessments of the classified images were made and change detection analyses were performed using TerrSet software. The LULC changes were also estimated in different scenarios using a future land use simulation model (FLUS). The results show that natural and semi-natural areas are rapidly disappearing due to urban growth between 1984 and 2020. The results of the land use simulation show that by 2038, while the agricultural, pasture and water bodies will decrease, the built-up areas will increase. It is estimated that the city, which has developed in a west-northwest direction, will expand in the future and grow between Elazığ and Şanlıurfa Boulevard.

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18

Potapov, Peter, Matthew C. Hansen, Indrani Kommareddy, Anil Kommareddy, Svetlana Turubanova, Amy Pickens, Bernard Adusei, Alexandra Tyukavina, and Qing Ying. "Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping." Remote Sensing 12, no. 3 (January 29, 2020): 426. http://dx.doi.org/10.3390/rs12030426.

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The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.

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19

Sang, Catherine C., Daniel O. Olago, Tobias O. Nyumba, Robert Marchant, and Jessica P. R. Thorn. "Assessing the Underlying Drivers of Change over Two Decades of Land Use and Land Cover Dynamics along the Standard Gauge Railway Corridor, Kenya." Sustainability 14, no. 10 (May 19, 2022): 6158. http://dx.doi.org/10.3390/su14106158.

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Land cover has been modified by anthropogenic activities for thousands of years, although the speed of change has increased in recent decades, particularly driven by socio-economic development. The development of transport infrastructure can accelerate land use land cover change, resulting in impacts on natural resources such as water, biodiversity, and food production. To understand the interaction between land cover and social–ecological drivers, changing land cover patterns and drivers of change must be identified and quantified. This study documents land cover dynamics along the Standard Gauge Railway (SGR) corridor in Kenya and evaluates the underlying drivers of this change from 2000 to 2019. The study utilised GIS and remote sensing techniques to assess the land use and land cover changes along the SGR corridor, while correlational and regression analyses were used to evaluate various drivers of the changes. Results showed that built-up areas, bare lands, water bodies, croplands and forests increased by 144.39%, 74.73%, 74.42%, 9.32% and 4.85%, respectively, while wetlands, grasslands and shrub lands reduced by 98.54%, 67.00% and 33.86%, respectively. The underlying drivers responsible for these land use and land cover dynamics are population growth, urbanisation, economic growth and agro-ecological factors. Such land cover changes affect environmental sustainability, and we stress the need to adequately identify and address the cumulative social and environmental impacts of mega-infrastructure projects and their interacting investments. The findings of this study provide an evidence base for the evaluation of the social–ecological impacts of the SGR and the implementation of best practices that will lead to enhanced sustainability in the development corridors in Kenya and beyond.

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20

Mugo, Robinson, Rose Waswa, James W. Nyaga, Antony Ndubi, Emily C. Adams, and Africa I. Flores-Anderson. "Quantifying Land Use Land Cover Changes in the Lake Victoria Basin Using Satellite Remote Sensing: The Trends and Drivers between 1985 and 2014." Remote Sensing 12, no. 17 (September 1, 2020): 2829. http://dx.doi.org/10.3390/rs12172829.

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The Lake Victoria Basin (LVB) is a significant resource for five states within East Africa, which faces major land use land cover changes that threaten ecosystem integrity and ecosystem services derived from the basin’s resources. To assess land use land cover changes between 1985 and 2014, and subsequently determine the trends and drivers of these changes, we used a series of Landsat images and field data obtained from the LVB. Landsat image pre-processing and band combinations were done in ENVI 5.1. A supervised classification was applied on 118 Landsat scenes using the maximum likelihood classifier in ENVI 5.1. The overall accuracy of classified images was computed for the 2014 images using 124 reference data points collected through stratified random sampling. Computations of area under various land cover classes were calculated between the 1985 and 2014 images. We also correlated the area from natural vegetation classes to farmlands and settlements (urban areas) to explore relationships between land use land cover conversions among these classes. Based on our land cover classifications, we obtained overall accuracy of 71% and a moderate Kappa statistic of 0.56. Our results indicate that the LVB has undergone drastic changes in land use land cover, mainly driven by human activities that led to the conversion of forests, woodlands, grasslands, and wetlands to either farmlands or settlements. We conclude that information from this work is useful not only for basin-scale assessments and monitoring of land cover changes but also for targeting, prioritizing, and monitoring of small scale, community led efforts to restore degraded and fragmented areas in the basin. Such efforts could mitigate the loss of ecosystem services previously derived from large contiguous land covers which are no longer tenable to restore. We recommend adoption of a basin scale, operational, Earth observation-based, land use change monitoring framework. Such a framework can facilitate rapid and frequent assessments of gains and losses in specific land cover classes and thus focus strategic interventions in areas experiencing major losses, through mitigation and compensatory approaches.

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21

Gibbes, Cerian, David G. Havlick, and Joseph R. Robb. "Land use and land cover in a transitioning militarized landscape." Journal of Land Use Science 12, no. 2-3 (April 11, 2017): 182–96. http://dx.doi.org/10.1080/1747423x.2017.1313325.

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22

Cowell, Sarah J. "Changes in Land Use and Land Cover: A Global Perspective." Global Environmental Change 5, no. 2 (May 1995): 161–62. http://dx.doi.org/10.1016/0959-3780(95)90051-9.

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23

Feizizadeh, Bakhtiar, Thomas Blaschke, Hossein Nazmfar, Elahe Akbari, and Hamid Reza Kohbanani. "Monitoring land surface temperature relationship to land use/land cover from satellite imagery in Maraqeh County, Iran." Journal of Environmental Planning and Management 56, no. 9 (November 2013): 1290–315. http://dx.doi.org/10.1080/09640568.2012.717888.

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24

Duong, Phan, Ta Trung, Kenlo Nasahara, and Takeo Tadono. "JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017." Remote Sensing 10, no. 9 (September 4, 2018): 1406. http://dx.doi.org/10.3390/rs10091406.

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Robust remote monitoring of land cover changes is essential for a range of studies such as climate modeling, ecosystems, and environmental protection. However, since each satellite data has its own effective features, it is difficult to obtain high accuracy land cover products derived from a single satellite’s data, perhaps because of cloud cover, suboptimal acquisition schedules, and the restriction of data accessibility. In this study, we integrated Landsat 5, 7, and 8, Sentinel-2, Advanced Land Observing Satellite Advanced Visual, and Near Infrared Radiometer type 2 (ALOS/AVNIR-2), ALOS Phased Array L-band Synthetic Aperture Radar (PALSAR) Mosaic, ALOS-2/PALSAR-2 Mosaic, Shuttle Radar Topography Mission (SRTM), and ancillary data, using kernel density estimation to map and analyze land use/cover change (LUCC) over Central Vietnam from 2007 to 2017. The region was classified into nine categories, i.e., water, urban, rice paddy, upland crops, grassland, orchard, forest, mangrove, and bare land by an automatic model which was trained and tested by 98,000 reference data collected from field surveys and visual interpretations. Results were the 2007 and 2017 classified maps with the same spatial resolutions of 10 m and the overall accuracies of 90.5% and 90.6%, respectively. They indicated that Central Vietnam experienced an extensive change in land cover (33 ± 18% of the total area) during the study period. Gross gains in forests (2680 km2) and water bodies (570 km2) were primarily from conversion of orchards, paddy fields, and crops. Total losses in bare land (495 km2) and paddy (485 km2) were largely to due transformation to croplands and urban & other infrastructure lands. In addition, the results demonstrated that using global land cover products for specific applications is impaired because of uncertainties and inconsistencies. These findings are essential for the development of resource management strategy and environmental studies.

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25

Venter, Zander S., David N. Barton, Tirthankar Chakraborty, Trond Simensen, and Geethen Singh. "Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover." Remote Sensing 14, no. 16 (August 21, 2022): 4101. http://dx.doi.org/10.3390/rs14164101.

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The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m2, we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of <100 m2, we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area.

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26

Bouchachi, B., and Y. Zhong. "MONITORING URBAN LAND COVER/LAND USE CHANGE IN ALGIERS CITY USING LANDSAT IMAGES (1987–2016)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1083–90. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1083-2017.

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Monitoring the Urban Land Cover/Land Use change detection is important as one of the main driving forces of environmental change because Urbanization is the biggest changes in form of Land, resulting in a decrease in cultivated areas. Using remote sensing ability to solve land resources problems. The purpose of this research is to map the urban areas at different times to monitor and predict possible urban changes, were studied the annual growth urban land during the last 29 years in Algiers City. Improving the productiveness of long-term training in land mapping, were have developed an approach by the following steps: 1) pre-processing for improvement of image characteristics; 2) extract training sample candidates based on the developed methods; and 3) Derive maps and analyzed of Algiers City on an annual basis from 1987 to 2016 using a Supervised Classifier Support Vector Machine (SVMs). Our result shows that the strategy of urban land followed in the region of Algiers City, developed areas mostly were extended to East, West, and South of Central Regions. The urban growth rate is linked with National Office of Statistics data. Future studies are required to understand the impact of urban rapid lands on social, economy and environmental sustainability, it will also close the gap in data of urbanism available, especially on the lack of reliable data, environmental and urban planning for each municipality in Algiers, develop experimental models to predict future land changes with statistically significant confidence.

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Banchero, S., D. de Abelleyra, S. R. Veron, M. J. Mosciaro, F. Arévalos, and J. N. Volante. "RECENT LAND USE AND LAND COVER CHANGE DYNAMICS IN THE GRAN CHACO AMERICANO." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 369–72. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-369-2020.

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Abstract. Land transformation is one of the most significant human changes on the Earth’s surface processes. Therefore, land use land cover time series are a key input for environmental monitoring, natural resources management, territorial planning enforcement at national scale. We here capitalize from the MapBiomas initiative to characterize land use land cover (LULC) change in the Gran Chaco between 2010 and 2017. Specifically we sought to a) quantify annual changes in the main LULC classes; b) identify the main LULC transitions and c) relate these transitions to current land use policies. Within the MapBiomas project, Landsat based annual maps depicting natural woody vegetation, natural herbaceous vegetation, dispersed natural vegetation, cropland, pastures, bare areas and water. We used Random Forest machine learning algorithms trained by samples produced by visual interpretation of high resolution images. Annual overall accuracy ranged from 0,73 to 0,74. Our results showed that, between 2010 and 2017, agriculture and pasture lands increased ca. 3.7 Mha while natural forestry decreased by 2.3 Mha. Transitions from forests to agriculture accounted for 1.14% of the overall deforestation while 86% was associated to pastures and natural herbaceous vegetation. In Argentina, forest loss occurred primarily (39%) on areas non considered by the territorial planning Law, followed by medium (33%), high (19%) and low (9%) conservation priority classes. These results illustrate the potential contribution of remote sensing to characterize complex human environmental interactions occurring over extended areas and timeframes.

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Lin, Lili, Zhenbang Hao, Christopher J. Post, Elena A. Mikhailova, Kunyong Yu, Liuqing Yang, and Jian Liu. "Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine." Applied Sciences 10, no. 20 (October 20, 2020): 7336. http://dx.doi.org/10.3390/app10207336.

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Island ecosystems are particularly susceptible to climate change and human activities. The change of land use and land cover (LULC) has considerable impacts on island ecosystems, and there is a critical need for a free and open-source tool for detecting land cover fluctuations and spatial distribution. This study used Google Earth Engine (GEE) to explore land cover classification and the spatial pattern of major land cover change from 1990 to 2019 on Haitan Island, China. The land cover classification was performed using multiple spectral bands (RGB, NIR, SWIR), vegetation indices (NDVI, NDBI, MNDWI), and tasseled cap transformation of Landsat images based on the random forest supervised algorithm. The major land cover conversion processes (transfer to and from) between 1990 and 2019 were analyzed in detail for the years of 1990, 2000, 2007, and 2019, and the overall accuracies ranged from 88.43% to 91.08%, while the Kappa coefficients varied from 0.86 to 0.90. During 1990–2019, other land, cultivated land, sandy land, and water area decreased by 30.70%, 13.63%, 3.76%, and 0.95%, respectively, while forest and built-up land increased by 30.94% and 16.20% of the study area, respectively. The predominant land cover was other land (34.49%) and cultivated land (26.80%) in 1990, which transitioned to forest land (53.57%) and built-up land (23.07%) in 2019. Reforestation, cultivated land reduction, and built-up land expansion were the major land cover change processes on Haitan Island. The spatial pattern of forest, cultivated land, and built-up land change is mainly explained by the implementation of a ‘Grain for Green Project’ and ‘Comprehensive Pilot Zone’ policy on Haitan Island. Policy and human activities are the major drivers for land use change, including reforestation, population growth, and economic development. This study is unique because it demonstrates the use of GEE for continuous monitoring of the impact of reforestation efforts and urbanization in an island environment.

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Harjadi, Beny. "Monitoring Penutupan Lahan di DAS Grindulu dengan Metode Penginderaan Jauh dan Sistem Informasi Geografis." Forum Geografi 24, no. 1 (July 20, 2010): 85. http://dx.doi.org/10.23917/forgeo.v24i1.5017.

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Catchments area can be analyzed as management system. Catchments area acquire input and it processed by the system to produce output. Land covers in catchments area are closely related to land use pattern and to management system. Land use changes to building area, agriculture and another activity are related to anthropological characters effected by change in function from vegetated land to unvegetated land. This condition have negative influences to the condition of carchment area. The damaged level of catchment area can be reflected by flood susceptibility, droughness, erosion and sedimentation, related impact onsite and offsite, so it is need a comprehensive management system from up land to low land river. To give information of land use in catchments area it needs accurate data about land cover in wide range. Remote sensing and Geographic Information System (GIS) are applicable to monitor land coverage of management catchments area. The aim of this paper is to analyze land cover using remote sensing and GIS to catchments area monitoring and evaluation. Land use in watershed connection with the pattern of nature resources by the community and the management of watershed. Total area of land use Grindulu watershed was 65.539 ha. From the map of land use could be seen that the spreading of the equitable meeting forest from the upstream to lower, and most property of the people. Land use became 8 classes, that is: Agroforestry (20%), Open Land (12%), Rare Forest (1%), Dense Forest (29%), Village (34%), Paddy (0.4%), River (0.2%), and Field (3%).

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Sun, Qiong, Chi Zhang, Min Liu, and Yongjing Zhang. "Land use and land cover change based on historical space–time model." Solid Earth 7, no. 5 (September 27, 2016): 1395–403. http://dx.doi.org/10.5194/se-7-1395-2016.

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Abstract. Land use and cover change is a leading edge topic in the current research field of global environmental changes and case study of typical areas is an important approach understanding global environmental changes. Taking the Qiantang River (Zhejiang, China) as an example, this study explores automatic classification of land use using remote sensing technology and analyzes historical space–time change by remote sensing monitoring. This study combines spectral angle mapping (SAM) with multi-source information and creates a convenient and efficient high-precision land use computer automatic classification method which meets the application requirements and is suitable for complex landform of the studied area. This work analyzes the histological space–time characteristics of land use and cover change in the Qiantang River basin in 2001, 2007 and 2014, in order to (i) verify the feasibility of studying land use change with remote sensing technology, (ii) accurately understand the change of land use and cover as well as historical space–time evolution trend, (iii) provide a realistic basis for the sustainable development of the Qiantang River basin and (iv) provide a strong information support and new research method for optimizing the Qiantang River land use structure and achieving optimal allocation of land resources and scientific management.

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Dong, Shi Wei, Hong Li, Dan Feng Sun, Wei Wei Zhang, and Lian Di Zhou. "Application Research of Remote Sensing in the Field of Land Use and Land Cover." Advanced Materials Research 765-767 (September 2013): 2374–78. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2374.

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As a new high-technology with large amount of information, strong temporal resolution, high efficiency and low cost, remote sensing provided a useful tool for related researches of land use and land cover in different spatial and temporal scales. Firstly, concepts and characteristics of the remote sensing technology were introduced. Secondly, its applications were elaborated in land use and land cover such as land resources survey, land resources change monitoring, land use evaluation, overall planning of land use and land consolidation aspects. At last, future application trends and several aspects noticed were pointed out.

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Iurist (Dumitraşcu), Nicoleta, Florian Stătescu, and Iustina Lateş. "Analysis of Land Cover and Land Use Changes Using Sentinel-2 Images." Present Environment and Sustainable Development 10, no. 2 (October 1, 2016): 161–72. http://dx.doi.org/10.1515/pesd-2016-0034.

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Abstract Earth observation and space analysis of land areas, oceanic and atmospheric phenomena is a necessity nowadays. European Space Agency (ESA) is developing a new family of satellites, called Sentinel, in order to perform the operational needs of the environmental monitoring program, Copernicus. Since 2014 until now ESA have successfully launched four satellites, which have a proven track record. This paper contains information about Sentinel constellation, features of the satellite images and also the applications of Sentinel satellite images. This paper also describes how to purchase satellite data and the software that can be used to view and analysis data are named. The aim of this paper is to analyze the changes of land cover and land use of study area, in two different periods, based on Sentinel satellite images.

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Siddiqui, Saima, Mirza Wajid Ali Safi, Naveed Ur Rehman, and Aqil Tariq. "Impact of Climate Change on Land use/Land cover of Chakwal District." International Journal of Economic and Environmental Geology 11, no. 2 (September 28, 2020): 65–68. http://dx.doi.org/10.46660/ijeeg.vol11.iss2.2020.449.

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Alterations in land use and land cover, either natural or anthropogenic can disturb the balance of fragile ecosystems. Climate change plays a unique role in governing the structure and state of land features and alters the structure of ecosystem as well as its services required by earth. Human health and environment are matter of concern due to changes induced by human in its natural environment (Jat et al., 2008). Human has an urge to remain near nature, for that they shift from dense urban areas to less dense areas (Western, 2001). So is the case of new housing societies where the land mafias intimate the people about new settlements (Zaman and Baloch, 2011), which are made by cutting the forests, removing trees and disturbing the ecosystem. For proper planning and management of natural resources, it is necessary to study the land cover and its associated changes (Asselman and Middelkoop, 1995). Modelling of changes within land cover to identify environmental trends on the local, national or regional level, have been realized in the scientific community (Nath et al., 2020). GIS/RS provides continuous change dynamics (Berlanga-Robles and Ruiz-Luna, 2011) of land cover/land use, i.e. by satellite monitoring (Ruiz-Ruano et al., 2016). The understanding of land cover changes is necessary for decision making (Lu et al., 2004) in the natural resource management (Seif et al., 2012). This study was carried out to identify the impact of changes in climate upon land use and land cover of Chakwal district from 1995 to 2020. Geospatial techniques were applied to estimate the differences in land features, using different time interval satellite datasets (Table 1). Six major classes of land features including, agriculture, bare land, built-up, forest, shrubs/grass and water were selected for this study, with respect to time.

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Treitz, Paul, and John Rogan. "Remote sensing for mapping and monitoring land-cover and land-use change—an introduction." Progress in Planning 61, no. 4 (May 2004): 269–79. http://dx.doi.org/10.1016/s0305-9006(03)00064-3.

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Agarwal, Apeksha, Krishna Kumar Soni, and MSS Rawat. "Monitoring Land Use Land Cover Change for Dehradun District of Uttarakhand from 2009-2019." International Journal of Advanced Remote Sensing and GIS 8, no. 1 (2019): 3106–13. http://dx.doi.org/10.23953/cloud.ijarsg.431.

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36

Sayão, Veridiana Maria, Natasha Valadares dos Santos, Wanderson de Sousa Mendes, Karina P. P. Marques, José Lucas Safanelli, Raul Roberto Poppiel, and José A. M. Demattê. "Land use/land cover changes and bare soil surface temperature monitoring in southeast Brazil." Geoderma Regional 22 (September 2020): e00313. http://dx.doi.org/10.1016/j.geodrs.2020.e00313.

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37

Folega, Fousseni, Chun-yu Zhang, Xiu-hai Zhao, Kperkouma Wala, Komlan Batawila, Hua-guo Huang, Marra Dourma, and Koffi Akpagana. "Satellite monitoring of land-use and land-cover changes in northern Togo protected areas." Journal of Forestry Research 25, no. 2 (April 30, 2014): 385–92. http://dx.doi.org/10.1007/s11676-014-0466-x.

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38

López, N., A. Márquez Romance, and E. Guevara Pérez. "Change dynamics of land-use and land-cover for tropical wetland management." Water Practice and Technology 15, no. 3 (June 12, 2020): 632–44. http://dx.doi.org/10.2166/wpt.2020.049.

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Abstract In hydrographic basins with wetlands, changes in land use (LU) and land cover (LC) impact the conservation of natural resources, leading to dynamics analysis for integral management. A method is proposed offering greater accuracy in determining the LU and LC bi-temporal and spatial change dynamics in tropical wetlands. LU and LC monitoring is based on Landsat images from 1986 to 2017. ‘Pre-classification’ and ‘post-classification’ methods are applied. In the former, reflectance image differencing and principal component N° 1 image differencing are analyzed to estimate the rate of change/no change area. In the latter, supervised classification is carried out of image pairs from different dates. The principal components method shows that principal component N° 1 collects between 88 and 93% of the reflectance variance in n spectral bands of each satellite image, which improves accuracy in determining LU and LC change dynamics.

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Yılmaz, Rüya. "Monitoring land use/land cover changes using CORINE land cover data: a case study of Silivri coastal zone in Metropolitan Istanbul." Environmental Monitoring and Assessment 165, no. 1-4 (June 3, 2009): 603–15. http://dx.doi.org/10.1007/s10661-009-0972-z.

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Guo, Liang, Xiaohuan Xi, Weijun Yang, and Lei Liang. "Monitoring Land Use/Cover Change Using Remotely Sensed Data in Guangzhou of China." Sustainability 13, no. 5 (March 8, 2021): 2944. http://dx.doi.org/10.3390/su13052944.

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Land use/cover change (LUCC) has a crucial influence on ecosystem function, environmental change and decision support. Rapid and precise monitoring of land use/cover change information is essential for utilization and management of land resources. The objectives of this study were to monitor land use/cover change of Guangzhou of China from 1986 to 2018 using remotely sensed data, and analyze the correlation between artificial surface expansion and the gross domestic product (GDP) growth. Supervised classification was performed using Random Forest classifier, and the overall accuracy (OA) ranged from 86.42% to 96.58% and kappa coefficient (K) ranged from 0.8079 to 0.9499. The results show that the built-up area of Guangzhou of China from 1986 to 2018 continued to increase. However, the vegetation area continued to decrease during 32 years. The built-up area increased by 1315.56 km2 (increased by 439.34%) with an average growth of 41.11 km2/year. The vegetation area reduced by 1290.78 km2 (reduced by 19.99%) with an average reduction of 40.34 km2/year. Research has shown that the reduced vegetation area was mainly converted into built-up area. The area of water bodies and bare lands was relatively stable and had a little change. The results indicate that the GDP had a strong positive correlation with built-up area (R2 = 0.98). However, there is a strong negative correlation between the GDP and vegetation area (R2 = 0.97) in Guangzhou City, China. As a consequence, the increase of built-up area was at the cost of the reduction of vegetation area.

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Münch, Zahn, Lesley Gibson, and Anthony Palmer. "Monitoring Effects of Land Cover Change on Biophysical Drivers in Rangelands Using Albedo." Land 8, no. 2 (February 9, 2019): 33. http://dx.doi.org/10.3390/land8020033.

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This paper explores the relationship between land cover change and albedo, recognized as a regulating ecosystems service. Trends and relationships between land cover change and surface albedo were quantified to characterise catchment water and carbon fluxes, through respectively evapotranspiration (ET) and net primary production (NPP). Moderate resolution imaging spectroradiometer (MODIS) and Landsat satellite data were used to describe trends at catchment and land cover change trajectory level. Peak season albedo was computed to reduce seasonal effects. Different trends were found depending on catchment land management practices, and satellite data used. Although not statistically significant, albedo, NPP, ET and normalised difference vegetation index (NDVI) were all correlated with rainfall. In both catchments, NPP, ET and NDVI showed a weak negative trend, while albedo showed a weak positive trend. Modelled land cover change was used to calculate future carbon storage and water use, with a decrease in catchment carbon storage and water use computed. Grassland, a dominant dormant land cover class, was targeted for land cover change by woody encroachment and afforestation, causing a decrease in albedo, while urbanisation and cultivation caused an increase in albedo. Land cover map error of fragmented transition classes and the mixed pixel effect, affected results, suggesting use of higher-resolution imagery for NPP and ET and albedo as a proxy for land cover.

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42

Lin, Yi, Bing Liu, Feng Xie, and Wen Wei Ren. "Land Use/Land Cover Dynamic Monitoring and Analysis Using Remote Sensing and GIS Techniques: Case Study of Qingpu District, Shanghai." Advanced Materials Research 518-523 (May 2012): 5704–9. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5704.

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This paper illustrates almost twenty years (1986～2007) of Land use/land cover change (LULCC) in Qingpu-one district of Shanghai. Qingpu District is an area of Upper Huangpu Catchment for fresh water supply with considerable ecological value, but it is also experiencing urban sprawl from development. To reveal the trends underlie LULCC, we propose a novel procedure to quantify different land use/land covers and implement it in the case study. In this procedure, we first collect historical remote-sensing data and co-registered or corrected them to the same spatial resolution and radioactive level. Based upon preliminary interpretation or investigation, land use/land cover types in study area can be included in 5 categories, i.e. Water, Agricultural Land, Urban or Built-up Land, Forest Land, and Barren Land or others. Moreover, data is clipped via boundary of study area for reducing computation load, followed by FPCR-ISODATA classification to divide the data into k groups (k>the number of land types). After postprocessing, e.g., merge the same connoted subgroups and correct misclassified units accompany with validation and verification, the detailed land use/land cover results can be achieved accurately. The quantitative and regression analysis indicate that during the past twenty years the area of agricultural land of Qingpu decreased coupled with urban or built-up area increased linearly. The water area had the minimum change during the decades. Forests had the smallest average proportion (9.6%) of the total area. It occupied so small proportion of land that we can only find points of it in the maps. Barren land can be an indicator for monitoring uncompleted redevelopment or transition of land.

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Soulard, Christopher E., and Tamara S. Wilson. "Recent land-use/land-cover change in the Central California Valley." Journal of Land Use Science 10, no. 1 (September 25, 2013): 59–80. http://dx.doi.org/10.1080/1747423x.2013.841297.

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Shapero, Matthew, Katherine Siegel, John A. Gallo, Justin Brice, and Van Butsic. "Land cover conversion and land use change combine to reduce grazing." Journal of Land Use Science 17, no. 1 (January 2, 2022): 339–50. http://dx.doi.org/10.1080/1747423x.2022.2086311.

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45

McMichael, Christine E., David P. Smith, and Robert C. Johnson. "Landscape indicators of stream water quality in central Appalachia (USA): Land use/land cover or land surface condition?" Aquatic Ecosystem Health & Management 16, no. 3 (July 2013): 329–37. http://dx.doi.org/10.1080/14634988.2013.822285.

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46

Alturk, Bahadir, and Fatih Konukcu. "Modeling land use/land cover change and mapping morphological fragmentation of agricultural lands in Thrace Region/Turkey." Environment, Development and Sustainability 22, no. 7 (October 3, 2019): 6379–404. http://dx.doi.org/10.1007/s10668-019-00485-3.

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47

Phiri, Darius, Matamyo Simwanda, Serajis Salekin, Vincent R. Nyirenda, Yuji Murayama, and Manjula Ranagalage. "Sentinel-2 Data for Land Cover/Use Mapping: A Review." Remote Sensing 12, no. 14 (July 16, 2020): 2291. http://dx.doi.org/10.3390/rs12142291.

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The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

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Tiwari, Awantika, Sateesh Kumar Karwariya, and Shashikant Tripathi. "Monitoring Land Use/Cover Change Using Digital Classification Techniques: A Case Study of Sadhera Mines, Satna, Madhya Pradesh, India." European Journal of Engineering and Technology Research 1, no. 1 (July 27, 2018): 34–38. http://dx.doi.org/10.24018/ejeng.2016.1.1.118.

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Land use land cover is an important part to understand present as well as past status of the earth’s surface. Land use and land cover are two different terminologies. Land use and land cover is dynamic in nature and provides a comprehensive perceptive of human activities with the environment. As land is becoming a scarce resource due to immense agricultural and demographic pressure, therefore the information on land use land cover and possibilities for their optimal use is essential for the selection, planning and implementation of land use schemes to meet the increasing demands for basic human needs and welfare. The study area is in the limelight due to continuous mining leading to large scale reduction in dense forest. The main objective of this study is to monitor change in landuse and land cover in sadhera and its surrounding mines of Katni district by using of remote sensing and geographic information system technique. The land use land cover change detection has been performed based on the analysis of the digital data of landsat TM (30 Meter) and landsat 8 (OLI)+ Pan (15 Meter) pertaining to 2009-10 and 2015-16. It has been observed that there has been a significant change in the land use pattern with an increase of 287.46 % in the mining area whereas the area under dense forest has decrease drastically by - 60.21 %. Hence, the information obtained from landuse landcover change detection map help to provide optimal solutions for the selection, planning, implementation and monitoring of mining areas.

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Maggiore, Giuseppe, Teodoro Semeraro, Roberta Aretano, Luigi De Bellis, and Andrea Luvisi. "GIS Analysis of Land-Use Change in Threatened Landscapes by Xylella fastidiosa." Sustainability 11, no. 1 (January 7, 2019): 253. http://dx.doi.org/10.3390/su11010253.

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Land-use/land-cover analysis using Geographic Information System (GIS) application can describe and quantify the transformation of the landscape, evaluating the effectiveness of municipal planning in driving urban expansion. This approach was applied in the municipality of Spongano (Salento, South Italy) in order to evaluate the spatial heterogeneity and the transformations of the land use/land cover from 1988 to 2016. This approach was also used to examine the spread of Xylella fastidiosa, which is a plant pathogen of global importance that is reshaping the Salento landscape. The land-use maps are based on the CORINE Land Cover project classification, while the topological consistency was verified through field surveys. A change detection analysis was carried out using the land-use maps of 1988 and 2016. The most extensive land-use class is olive groves (34–36%), followed by non-irrigated arable lands and shrub and/or herbaceous vegetation associations. The main transition of land involved non-irrigated arable lands, which lost 76 ha and 23 ha to shrub and olive areas, respectively. Meanwhile, the artificial surfaces class doubled its extension, which involved mainly the transition from shrub and heterogeneous agricultural areas. However, the olive groves class is threatened by the dramatic phytosanitary condition of the area, indicating a compromised agroecosystem, which is causing a de facto transition into unproductive areas. The results highlight the inconsistency between what was planned by the urban plan in the past and how the landscape of Spongano has been changed over time. This evidence suggests that it is necessary to develop a plan based on learning by doing, in order to shape and adapt the processes of territorial transformation to the unpredictability of the ecologic, social, and economic systems, as well as ensure that these processes are always focused on environmental issues.

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Sang, H., L. Zhai, J. Zhang та F. An. "An object-oriented approach for agrivultural land classiﬁcation using rapideye imagery". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (26 червня 2015): 145–48. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-145-2015.

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With the improvement of remote sensing technology, the spatial, structural and texture information of land covers are present clearly in high resolution imagery, which enhances the ability of crop mapping. Since the satellite RapidEye was launched in 2009, high resolution multispectral imagery together with wide red edge band has been utilized in vegetation monitoring. Broad red edge band related vegetation indices improved land use classification and vegetation studies. RapidEye high resolution imagery acquired on May 29 and August 9th of 2012 was used in this study to evaluate the potential of red edge band in agricultural land cover/use mapping using an objected-oriented classification approach. A new object-oriented decision tree classifier was introduced in this study to map agricultural lands in the study area. Besides the five bands of RapidEye image, the vegetation indexes derived from spectral bands and the structural and texture features are utilized as inputs for agricultural land cover/use mapping in the study. The optimization of input features for classification by reducing redundant information improves the mapping precision over 9% for AdaTree. WL, and 5% for SVM, the accuracy is over 90% for both approaches. Time phase characteristic is much important in different agricultural lands, and it improves the classification accuracy 7% for AdaTree.WL and 6% for SVM.

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