Changes in the methodology used in the production of the Spanish CORINE: Uncertainty analysis of the new maps

Since 2012 CORINE has been obtained in Spain from the generalization of a more detailed land cover map (SIOSE). This methodological change has meant the production of a new CORINE map, which is different from the existing ones. To analyze how different the new maps are from the previous ones, as well as the advantages and disadvantages of the new methodology, we carried out a comparison of the CORINE obtained from both methods (traditional and generalization) for the year 2006. The new CORINE is more detailed and it is more coherent with the rest of Spanish Land Use Land Cover (LULC) maps. However, problems have been encountered with regard to the meaning of its classes, the fragmentation of patches and the complexity of its perimeters.     

Urbanization in China from the end of 1980s until 2010 – spatial dynamics and patterns of growth using EO-data

Urbanization in China has been experiencing a remarkable dynamism in the past 40 years. The most evident implication of urbanization is the physical growth of cities. We analyze urban land growth rates and changes in spatial urban forms from the end of the 1980s to 2010 based on the authoritative National Land Use/Cover Database of China. We present new spatial measures that describe ‘urban land growth types’ and ‘fluctuations in urban land growth’ within the monitoring time span with a temporal interval of five-year steps. We evaluate the correlations between urban land growth rates and socioeconomic data. Results show that (1) distinct characteristics exist on the spatiotemporal evolutions of urban land growth rates in terms of area and perimeter, e.g. coastal areas exhibit the most dramatic growth rates; (2) the spatial distribution characteristics of ‘urban land growth types’ and ‘fluctuations in urban land growth’ follow similar spatial patterns across China, e.g. significant differences exist between the eastern region and other regions; and (3) a moderate correlation exists between urban area growth rate and urban population growth rate at an R² of 0.37. By contrast, we determine no significant correlation between urban area growth rate and tertiary industry value growth rate.     

A twenty year GIS-based assessment of environmental sustainability of land use changes in and around protected areas of a fast developing country: Spain

Spain has experienced massive recent socioeconomic changes that have had an influence on biodiversity and landscapes through land use-land cover (LULC) changes. Protected areas (PAs) seek to conserve biodiversity by establishing a legal and, sometimes, managerial regime that forbids or restricts LULC changes that are damaging to biodiversity. Here, we used CORINE Land Cover (CLC) data between 1987 and 2006 to assess differences in LULC changes and processes of change as metrics of effectiveness in four PA networks of clear legal and managerial characteristics in Spain: Nature reserves (NRs), Nature parks (NPs), Sites of Community Importance (SCIs) and Special Protection Areas (SPAs). We also compared LULC changes and processes of change around each PA network applying a modified Before-After-Control-Impact (BACI) research design with two increasingly distant control areas and two models of increased validity. The four PA networks were more environmentally sustainable than their surrounding areas although an effectiveness gradient was shown: NRs > SCIs > SPAs > NPs, suggesting little influence of PA management on LULC changes overall. Another gradient of environmental sustainability of control areas was evident: SCIs > SPAs > NPs > NRs. Proximal controls were more sustainable than distant ones. The main LULC increases inside PAs affected agro-forestry areas and transitional woodland-shrub, whereas artificial surfaces, permanently irrigated lands and burned areas prevailed in the proximal and distant controls. Three main LULC processes of change inside and around Spanish PAs outstood: forest succession, land development, and new irrigated areas, the two former chiefly affecting surrounding areas and posing serious threats to effective biodiversity conservation.     

Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®

This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS^® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS^® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA’s HyspIRI sensor and EnMAP.     

Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products,using Landsat data and C-CAP products

This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.     

Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis

Abstract

This study proposes the development of a multi-sensor, multi-spectral composite from Landsat-8 and Sentinel-2A imagery referred to as ‘LSC’ for land use land cover (LULC) characterisation and compared with respect to the hyperspectral imagery of the EO1: Hyperion sensor. A three-stage evaluation was implemented based on the similarity observed in the spectral response, supervised classification results and endmember abundance information obtained using linear spectral unmixing. The study was conducted for two areas located around Dhundi and Rohtak in Himachal Pradesh and Haryana, respectively. According to the analysis of the spectral reflectance curves, the spectral response of the LSC is capable of identifying major LULC classes. The kappa accuracy of 0.85 and 0.66 was observed for the classification results from LSC and Hyperion data for Dhundi and Rohtak datasets, respectively. The coefficient of determination was found to be above 0.9 for the LULC classes in both the datasets as compared to Hyperion, indicating a good agreement. Thus, these three-stage results indicated the significant potential of a composite derived from freely available multi-sensor multi-spectral imagery as an alternative to hyperspectral imagery for LULC studies.     

基于遥感技术的祖厉河流域LUCC与蒸散发研究

本文以陇中黄土高原的祖厉河流域为研究区,基于Landsat系列遥感影像,利用主成分变换方法,与地面温度和植被指数相结合,进行土地利用/土地覆盖提取研究,在此基础上,探讨祖厉河流域土地利用/土地覆盖变化(LUCC)及其驱动因素。利用基于TM影像的SEBAL(Surface Energy Balance Algorithm for Land)模型,从祖厉河流域三期影像提取了区域蒸散发通量。首先进行了NDVI、比辐射率和地面温度等地表参数的计算,然后反演了地表的净辐射、土壤热通量和感热通量,根据能量守恒最终获得日蒸散发量。结合气象观测数据反演作物系数,最后计算月蒸散发量。对同期的土地利用/土地覆盖数据和陆面蒸散发量进行对比分析,探讨了土地利用类型的变化对流域内能量和水分时空分异的影响。     

Cartographic Aspects of Land Cover Change Detection (Over- and Underestimation in the I&CORINE Land Cover 2000 Project)

Abstract

This paper presents the results of analysis of the data obtained by the method of computer-aided visual interpretation of satellite images used for identification of changes in land cover within the framework of the Image and CORINE Land Cover 2000 (I&CLC2000) Project (jointly managed by the European Environment Agency in Copenhagen, Denmark and the Joint Research Centre of the European Commission in Ispra, Italy). These data are also relevant in cartography. Land cover changes identified by the method mentioned may contain mistakes caused by over- or underestimation. The paper describes these mistakes. Overestimation (technical change) of the extent of land cover change is caused by adding the residual polygons (smaller than 25 ha) to neighbouring polygons. Underestimation is caused by the fact that discernible changes concerning areas larger than 5 ha which showed up in objects with areas smaller than 25 ha were not identified and, consequently, not included in either CLC90 or CLC2000 data layers; e.g. Dutch CLC_change database users' accuracy indicates an overestimation of 8.8% whereas the comparison of net change indicates a small, insignificant underestimation. In spite of the problems referred to, caused by overestimation or underestimation, the datasets on land cover changes in Europe for the 1990s and the year 2000 (± one year) can also be used for the compilation of land cover change maps at the regional, national and European levels.     

Land cover mapping applications with MODIS: a literature review

Abstract

Land use/land cover monitoring and mapping is crucial to efficient management of the land and its resources. Since the late 1980s increased attention has been paid to the use of coarse resolution optical data. The Moderate Resolution Imaging Spectroradiometer (MODIS) has features, which make it particularly suitable to earth characterization purposes. MODIS has 10 products dedicated mainly to land cover characterization and provides three kinds of data: angular, spectral and temporal. MODIS data also includes information about the data quality through the ‘Quality Assessment’ product. In this paper, we review how MODIS data are used to map land cover including the preferred MODIS products, the preprocessing and classification approaches, the accuracy assessment, and the results obtained.     

Utilization of Landsat data to quantify land-use and land-cover changes related to oil and gas activities in West-Central Alberta from 2005 to 2013

Land use and land cover (LULC) change detection associated with oil and gas activities plays an important role in effective sustainable management practices, compliance monitoring, and reclamation assessment. In this study, a mapping methodology is presented for quantifying pre- and post-disturbance LULC types with annual Landsat Best-Available-Pixel multispectral data from 2005 to 2013. Annual LULC and land disturbance maps were produced for one of the major conventional oil and gas production areas in West-Central Alberta with an accuracy of 78% and 87%, respectively. The highest rate of vegetation loss (178 km²/year) was observed in coniferous forest compared to broadleaf forest, mixed forest, and native vegetation. Integration of ancillary oil and gas geospatial data with annual land disturbances indicated that less than 20% of the total land disturbances were attributable to oil and gas activities. In 2013, approximately 44% of oil and gas disturbances from 2005 to 2013 showed evidence of vegetation recovery. In the future, geospatial data related to wildfire, logging activities, insect defoliation, and other natural and anthropogenic factors can be integrated to quantify other causes of land disturbances.     

Comparative assessment of CORINE2000 and GLC2000: Spatial analysis of land cover data for Europe

Given the current lack of interoperability between global and regional land cover products, efforts are underway to link the new European global land cover map (GLOBCOVER) with the existing global land cover 2000 map (GLC2000) and European CORINE mapping initiative. Since both datasets apply different mapping standards, key for a successful implementation is a thorough understanding of the heterogeneities among both datasets. Thus, this paper provides an assessment of compatibilities and differences between the CORINE2000 and GLC2000 datasets. The comparative assessment considers inconsistencies between the thematic legends (using the UN land cover classification system-LCCS), class specific accuracies, and the spatial resolution and heterogeneity of the datasets. The results are summarized with implications for the development of the new GLOBCOVER datasets.     

Combining AVHRR imagery with CORINE Land Cover data to observe forest fires and to assess their consequences

This paper describes an operational application of AVHRR satellite imagery in combination with the satellite-based land cover database CORINE Land Cover (CLC) for the comprehensive observation and follow-up of 10000 fire outbreaks and of their consequences in Greece during summer 2000. In the first stage, we acquired and processed satellite images on a daily basis with the aim of smoke-plume tracking and fire-core detection at the national level. Imagery was acquired eight times per day and derived from all AVHRR spectral channels. In the second stage, we assessed the consequences of fire on vegetation by producing a burnt-area map on the basis of multi-annual normalised vegetation indices using AVHRR data in combination with CLC. In the third stage we used again CLC to assess the land cover of burnt areas in the entire country. The results compared successfully to available inventories for that year. Burnt area was estimated with an accuracy ranging from 88% to 95%, depending on the predominant land cover type. These results, along with the low cost and high temporal resolution of AVHRR satellite imagery, suggest that the combination of low-resolution satellite data with harmonised CLC data can be applied operationally for forest fire and post-fire assessments at the national and at a pan-European level.     

Integrating Remote Sensing Techniques,Geographical Information Systems (GIS), and Stochastic Models for Monitoring Land Use and Land Cover (LULC) Changes in the Northern Coastal Region of Nayarit,Mexico

Land use and land cover (LULC) changes in northern Nayarit, Mexico were estimated using post-classification change detection methods and a Markov chain model. Three thematic maps were generated by classifying Landsat images from 1973, 1900, and 2000, which were then overlaid to generate three change-detection matrices to assess the intensity and direction of changes. Between 25% and 30% of the region displayed LULC changes, attributable to a stochastic behavior that can be modeled with a first-order Markov chain. The steady-state distribution estimates indicate that the LULC patterns in the region have not yet reached equilibrium and predict the expansion of the agricultural boundaries.     

Multiple band digital orthophoto quadrangle data: A source for generating land use maps

Abstract

The purpose of this study was to investigate the use of color infrared‐digital orthophoto quadrangle (CIR‐DOQ) data to generate land use/land cover (LULC) maps and to incorporate them as data layers in geographic information systems (GIS) involving various resource management scenarios. The Danville 7.5‐minute quadrangle located in the southern part of Limestone and Morgan counties, Alabama, was used as the study site. Data for the special CIR‐DOQ were generated by scanning four 9x9 inch CIR aerial photographs at a uniform pixel sample grid of 25 microns resulting in 2 meters ground sample resolution. One‐half of the quadrangle was used to identify training sites for performing a supervised classification of the data and the other half to verify the accuracy of the classification. The CIR‐DOQ data were found to be adequate for using a supervised classification algorithm to differentiate major LULC classes, resulting in a classification accuracy of 93 percent. The superior spatial quality of the data over commençai satellite data affords resource managers an opportunity to more effectively study land cover and surface hydrological properties of an area, soil moisture and surface soil textures, as well as differentiate among vegetation species, using remote sensing techniques. However, caution must be exercised when using multispectral classification techniques to classify mosaicked CIRDOQ data because of the image enhancements used to generate the final product. In its present form, there are some limitations to the use of the data for performing spectral classifications. Hozvever, the high spatial resolution of the data enables even the novice resource planner to effectively use the data in visual interpretations of major LULC classes.     

Modelling the impacts of civil war on land use and land cover change within Kono District,Sierra Leone: a socio-geospatial approach

Capturing the scope and trajectory of changes in land use and land cover (LULC) is critical to urban and regional planning, natural resource sustainability and the overall information needs of policy makers. Studies on LULC change are generally conducted within peaceful environments and seldom incorporate areas that are politically volatile. Consequently, the role of civil conflict on LULC change remains elusive. Using a dense time stack of Landsat Thematic Mapper images and a hybrid classification approach, this study analysed LULC changes in Kono District between 1986–1991, 1991–2002 and 2002–2007 with the overarching goal of elucidating deviations from typical changes in LULC caused by Sierra Leone's civil war (1991–2002). Informed by social survey and secondary data, this study engaged the drivers that facilitated LULC changes during war and non-war periods in a series of spatial regression models in exploring the interface between civil conflict and LULC change.     

Production of global land cover data – GLCNMO

Abstract

Global land cover is one of the fundamental contents of Digital Earth. The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset – Global Land Cover by National Mapping Organizations. It has 20 land cover classes defined using the Land Cover Classification System. Of them, 14 classes were derived using supervised classification. The remaining six were classified independently: urban, tree open, mangrove, wetland, snow/ice, and water. Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003. Training data for supervised classification were collected using Landsat images, MODIS NDVI seasonal change patterns, Google Earth, Virtual Earth, existing regional maps, and expert's comments. The overall accuracy is 76.5% and the overall accuracy with the weight of the mapped area coverage is 81.2%. The data are available from the Global Mapping project website (http://www.iscgm.org/). The MODIS data used, land cover training data, and a list of existing regional maps are also available from the CEReS website. This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.     

Using OpenStreetMap to improve population grids in Europe

ABSTRACT

OpenStreetMap (OSM) database has previously been used to support spatial disaggregation of population data by partly masking out non-residential impervious areas in the European Copernicus imperviousness layer (IL). However, the exact procedure of OSM data incorporation is unknown, and its contribution to the improvement of estimation accuracy has never been studied. In this article, we present a sensitivity study to find out which road categories should be used for masking of IL and how the linear features might be transformed to raster representation. Using Austria and Slovenia as a study area, 2006 commune population counts are disaggregated into 100 m grid cells using 12 versions of modified IL. Further tuning of estimates is performed using CORINE Land Cover (CLC) data in an iterative algorithm. Disaggregated grids are then validated against reference 1 km census-based data. The results show that overall error was reduced thanks to OSM incorporation in all tested scenarios, although the relative improvement varies between as well as within the two countries. The best result (5.3% reduction) was achieved using railways and three major road categories (motorway, trunk, and primary) with double exaggeration of width.     

徐州市土地利用CLUE-S模型变化模拟

对徐州市1987年、1994年和2000年3期遥感影像图进行分析,利用面向对象的思想,采用多尺度分割法对影像进行分类,通过遥感影像的光谱特征,确定分类目标,获取该区过去13年间的土地利用/覆被时空变化特征。运用CLUE-S模型以1994年土地利用数据模拟了2000年的土地利用空间变化状况,并通过2000年实际遥感影像分类数据加以验证,结果计算出Kappa指数为0.846,达到精度要求,实例证明CLUE-S模型较好地模拟了徐州市的土地利用/覆被变化。最后运用CLUE-S模型以6年为1个时空尺度模拟该区未来12年的自然状态和生态保护状态下的土地利用/覆被变化特征。     

Next generation of global land cover characterization,mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).