Comparison and relative quality assessment of the GLC2000, GLOBCOVER,MODIS and ECOCLIMAP land cover data sets at the African continental scale

Land cover dynamics at the African continental scale is of great importance for global change studies. Actually, four satellite-derived land cover maps of Africa now available, e.g. ECOCLIMAP, GLC2000, MODIS and GLOBCOVER, are based on images acquired in the 2000s. This study aims at stressing the compliances and the discrepancies between these four land cover classifications systems. Each of them used different mapping initiatives and relies on different mapping standards, which supports the present investigation. In order to do a relative comparison of the four maps, a preamble was to reconcile their thematic legends into more aggregated categories after a projection into the same spatial resolution. Results show that the agreement between the four land cover products is between 56 and 69%. While all these land cover datasets show a reasonable agreement in terms of surface types and spatial distribution patterns, mapping of heterogeneous landscapes in the four products is not very successful. Land cover products based on remote sensing imagery can indeed significantly be improved by using smarter algorithms, better timing of image acquisition, improved class definitions. Either will help to improve the accuracy of future land cover maps at the African continental scale. Data producers may use the areas of spatial agreement for training area selection while users might need to verify the information in the areas of disagreement using additional data sources.     

Urban land cover thematic disaggregation,employing datasets from multiple sources and RandomForests modeling

Urban land cover mapping has lately attracted a vast amount of attention as it closely relates to a broad scope of scientific and management applications. Late methodological and technological advancements facilitate the development of datasets with improved accuracy. However, thematic resolution of urban land cover has received much less attention so far, a fact that hampers the produced datasets utility. This paper seeks to provide insights towards the improvement of thematic resolution of urban land cover classification. We integrate existing, readily available and with acceptable accuracies datasets from multiple sources, with remote sensing techniques. The study site is Greece and the urban land cover is classified nationwide into five classes, using the RandomForests algorithm. Results allowed us to quantify, for the first time with a good accuracy, the proportion that is occupied by each different urban land cover class. The total area covered by urban land cover is 2280 km² (1.76% of total terrestrial area), the dominant class is discontinuous dense urban fabric (50.71% of urban land cover) and the least occurring class is discontinuous very low density urban fabric (2.06% of urban land cover).     

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.     

遥感和GIS技术应用于伊拉克南部Basrah省土地利用/覆盖变化及城市扩张研究(英文)

In recent years, land use/cover dynamic change has become a key subject that needs to be dealt with in the study of global environmental change. In this paper, remote sensing and geographic information systems (GIS) are integrated to monitor, map, and quantify the land use/cover change in the southern part of Iraq (Basrah Province was taken as a case) by using a 1:250 000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation, sand, urban area, unused land, and water bodies. Supervised classification and normalized difference build-up index (NDBI) were used respectively to retrieve its urban boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. Results showed that the urban area had increased by the rate of 1.2% per year, with area expansion from 3 299.1 km² in 1990 to 3 794.9 km² in 2003. Large vegetation area in the north and southeast were converted into urban construction land. The land use/cover changes of Basrah Province were mainly caused by rapid development of the urban economy and population immigration from the countryside. In addition, the former government policy of “returning farmland to transportation and huge expansion in military camps” was the major driving force for vegetation land change. The paper concludes that remote sensing and GIS can be used to create LULC maps. It also notes that the maps generated can be used to delineate the changes that take place over time. Supported by the Al-Basrah University, Iraq, the Geo-information Science and Technology Program (No. IRT 0438)China).     

A History and Analysis of Mapping Urban Expansion in the Kathmandu Valley,Nepal

Abstract

There have been numerous efforts over many years to map or delineate urban locations and features in the Kathmandu Valley of Nepal. This study acquired 27 land useland cover maps for the Valley or the urban portions of the Valley. Those maps vary greatly in their mapping parameters. The objectives of this study were to first conduct a cartographic comparison of the differences in the creation and content of the maps and then do an analysis of the urban changes in the Valley based upon the maps. The maps for the Valley have differed in their source materials, the amount of field work involved, scale and minimum mapping units, classifications used, definitions of classes and coordinate systems. Source materials have included various scales and formats of aerial photography and different satellite systems. The most difficult issue in comparing the maps is the varied classification systems and definitions. The same feature will be classified differently from map to map. This is particularly an issue for institutional features such as temples, palaces, educational facilities, open public space and governmental sites. Definitions of residential areas are also not consistent. Even with the differences in mapping parameters, considerable useful information can be obtained by comparing these maps. These include a simply documentation of the urban extent and the generally resulting loss in agricultural lands. There was an increase in urban extent from 22 to 83km² between 1955 and 2000. Urban expansion has also changed from occurring on the upland river terraces or tars to the floodplains. Finally, while not directly documented in these maps, the tremendous pace of urban growth has resulted in multiple infrastructure and environmental challenges.     

Determination of land degradation causes in Tongyu County,Northeast China via land cover change detection

Tongyu County in Northeast China is highly prone to land degradation due to its fragile physical settings characterized by a flat topography, a semi-arid climate, and a shallow groundwater table. This study aims to determine the causes of land degradation through detecting the long-term trend of land cover changes. Degraded lands were mapped from satellite images recorded in 1992 and 2002. These land cover maps revealed that the area subject to land degradation in the form of soil salinization, waterlogging and desertification increased from 2400 to 4214 km², in sharp contrast to most severely degraded land that decreased by 122.5 km². Newly degraded land stems from productive farmland (263 km²), harvested farmland (551 km²), and grassland (468 km²). Therefore, the worsened degradation situation is attributed to excessive reclamation of grassland for farming, over cultivation, overgrazing, and deforestation. Mechanical, biological, ecological and engineering means should be adopted to rehabilitate the degraded land.     

Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty

The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by considering both image texture and band ratio information in the classification procedure. For each land use class, those classifications with the highest class-accuracy were selected and combined into class-probability maps. By selecting the land use class with highest probability for each pixel, we created a hard classification. We stored the corresponding class probabilities in a separate map, indicating the spatial uncertainty in the hard classification. By combining the uncertainty map and the hard classification we created a probability-based land use map, containing spatial estimates of the uncertainty. The technique was tested for both ASTER and Landsat 5 satellite imagery of Gorizia, Italy, and resulted in a 34% and 31% increase, respectively, in the kappa coefficient of classification accuracy. We believe that geocomputational classification methods can be used generally to improve land use and land cover classification from imagery, and to help incorporate classification uncertainty into the resultant map themes.     

A spatial comparison of four satellite derived 1 km global land cover datasets

Global change issues are high on the current international political agenda. A variety of global protocols and conventions have been established aimed at mitigating global environmental risks. A system for monitoring, evaluation and compliance of these international agreements is needed, with each component requiring comprehensive analytical work based on consistent datasets. Consequently, scientists and policymakers have put faith in earth observation data for improved global analysis. Land cover provides in many aspects the foundation for environmental monitoring [FAO, 2002a. Proceedings of the FAO/UNEP Expert Consultation on Strategies for Global Land Cover Mapping and Monitoring. FAO, Rome, Italy, 38 pp.]. Despite the significance of land cover as an environmental variable, our knowledge of land cover and its dynamics is poor [Foody, G.M., 2002. Status of land cover classification accuracy assessment. Rem. Sens. Environ. 80, 185–201]. This study compares four satellite derived 1 km land cover datasets freely available from the internet and in wide use among the scientific community. Our analysis shows that while these datasets have in many cases reasonable agreement at a global level in terms of total area and general spatial pattern, there is limited agreement on the spatial distribution of the individual land classes. If global datasets are used at a continental or regional level, agreement in many cases decreases significantly. Reasons for these differences are many—ranging from the classes and thresholds applied, time of data collection, sensor type, classification techniques, use of in situ data, etc., and make comparison difficult. Results of studies based on global land cover datasets are likely influenced by the dataset chosen. Scientists and policymakers should be made aware of the inherent limitations in using current global land cover datasets, and would be wise to utilise multiple datasets for comparison.     

Application of Earth Remote Sensing and GIS in Mapping Land Cover Patterns in Kinangop Division, Kenya

<正>Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and global levels.Hence,it is imperative to map land cover at a range of spatial and temporal scales with a view to understanding the inherent patterns for effective characterization,prediction and management of the potential environmental impacts.This paper presents the results of an effort to map land cover patterns in Kinangop division,Kenya,using geospatial tools.This is a geographic locality that has experienced rapid land use transformations since Kenya's independence culminating in uncontrolled land cover changes and loss of biodiversity.The changes in land use/cover constrain the natural resource base and presuppose availability of quantitative and spatially explicit land cover data for understanding the inherent patterns and facilitating specific and multi-purpose land use planning and management.As such,the study had two objectives viz.(i) mapping the spatial patterns of land cover in Kinangop using remote sensing and GIS and;(ii) evaluating the quality of the resultant land cover map.ASTER satellite imagery acquired in January 23,2007 was procured and field data gathered between September l0 and October 16,2007.The latter were used for training the maximum likelihood classifier and validating the resultant land cover map.The land cover classification yielded 5 classes,overall accuracy of 83.5%and kappa statistic of 0.79,which conforms to the acceptable standards of land cover mapping. This qualifies its application in environmental decision-making and manifests the utility of geospatial techniques in mapping land resources.     

Tracking long-term floodplain wetland changes: A case study in the China side of the Amur River Basin

Floodplain wetlands in the China side of the Amur River Basin (CARB) undergone consistent decreases because of both natural and anthropogenic drivers. Monitoring floodplain wetlands dynamics and conversions over long-time periods is thus fundamental to sustainable management and protection. Due to complexity and heterogeneity of floodplain environments, however, it is difficult to map wetlands accurately over a large area as the CARB. To address this issue, we developed a novel and robust classification approach integrating image compositing algorithm, objected-based image analysis, and hierarchical random forest classification, named COHRF, to delineate floodplain wetlands and surrounding land covers. Based on the COHRF classification approach, 4622 Landsat images were applied to produce a 30-m resolution dataset characterizing dynamics and conversions of floodplain wetlands in the CARB during 1990–2018. Results show that (1) all floodplain land cover maps in 1990, 2000, 2010, and 2018 had high mapping accuracies (ranging from 90 %±0.001–97%±0.005), suggesting that COHRF is a robust classification approach; (2) CARB experienced an approximately 25 % net loss of floodplain wetlands with an area declined from 8867 km² to 6630 km² during 1990–2018; (3) the lost floodplain wetlands were mostly converted into croplands, while, there were 111 km² and 256 km² of wetlands rehabilitated from croplands during periods of 2000–2010 and 2010–2018, respectively. To our knowledge, this study is the first attempt that focus on delineating floodplain wetlands at a large-scale and produce the first 30-m spatial resolution dataset demonstrating long-term dynamics of floodplain wetlands in the CARB. The COHRF classification approach could be used to classify other ecosystems readily and robustly. The resultant dataset will contribute to sustainable use and conservation of wetlands in the Amur River Basin and provide essential information for related researches.     

Monitoring and analysing the Emirate of Dubai’s land use/land cover changes: an integrated,low-cost remote sensing approach

This study presents a modified low-cost approach, which integrates the spectral angle mapper and image difference algorithms in order to enhance classification maps for the purpose of monitoring and analysing land use/land cover change between 2000 and 2015 for the Emirate of Dubai. The approach was modified by collecting 320 training samples from QuickBird images with a spatial resolution of 0.6 m, as well as carrying out field observations, followed by the application of a 3?×?3 Soble filter, sieving classes, majority/minority analysis, and clump classes of the obtained classification maps. The accuracy assessment showed that the targeted 2000, 2005, 2010 and 2015 classification maps have 88.1252%, 89.0699%, 90.1225% and 96.0965% accuracy, respectively. The results showed that the built-up area increased by 233.721?km² (5.81%) between 2000 and 2005 and continues to increase even up and till the present time. The assessment of changes in the periods 2000–2005 and 2010–2015 confirmed that net vegetation area losses were more pronounced from 2000 to 2005 than from 2010 to 2015, dropping from 47,618 to 40,820?km², respectively. This study is aimed to assist urban planners and decision-makers, as well as research institutes.     

Assessing land use/cover changes: a nationwide multidate spatial database for Mexico

A nationwide multidate GIS database was generated in order to carry out the quantification and spatial characterization of land use/cover changes (LUCC) in Mexico. Existing cartography on land use/cover at a 1:250,000 scale was revised to select compatible inputs regarding the scale, the classification scheme and the mapping method. Digital maps from three different dates (the late 1970s, 1993 and 2000) were revised, evaluated, corrected and integrated into a GIS database. In order to improve the reliability of the database, an attempt was made to assess the accuracy of the digitalisation procedure and to detect and correct unlikely changes due to thematic errors in the maps. Digital maps were overlaid in order to generate LUCC maps, transition matrices and to calculate rates of conversion. Based upon this database, rates of deforestation between 1976 and 2000 were evaluated as 0.25 and 0.76% per year for temperate and tropical forests, respectively.     

Land use intensity trajectories on Amazonian pastures derived from Landsat time series

Monitoring changes in land use intensity of grazing systems in the Amazon is an important prerequisite to study the complex political and socio-economic forces driving Amazonian deforestation. Remote sensing offers the potential to map pasture vegetation over large areas, but mapping pasture conditions consistently through time is not a trivial task because of seasonal changes associated with phenology and data gaps from clouds and cloud shadows. In this study, we tested spectral-temporal metrics derived from intra-annual Landsat time series to distinguish between grass-dominated and woody pastures. The abundance of woody vegetation on pastures is an indicator for management intensity, since the duration and intensity of land use steer secondary succession rates, apart from climate and soil conditions. We used the developed Landsat-based metrics to analyze pasture intensity trajectories between 1985 and 2012 in Novo Progresso, Brazil, finding that woody vegetation cover generally decreased after four to ten years of grazing activity. Pastures established in the 80s and early 90s showed a higher fraction of woody vegetation during their initial land use history than pastures established in the early 2000s. Historic intensity trajectories suggested a trend towards more intensive land use in the last decade, which aligns well with regional environmental policies and market dynamics. This study demonstrates the potential of dense Landsat time series to monitor land-use intensification on Amazonian pastures.     

大尺度土地覆盖数据集在中国及周边区域的精度评价

大尺度土地覆盖数据是全球陆地表层过程研究、生态系统评估、环境建模等科学研究的重要基础,研究现有数据集的特点对数据使用者及生产新的数据集都具有指导意义。本研究以中国及周边区域为研究区,根据不同分类体系对地物的定义,研究不同分类体系中对应地物的相关系数,并将所有分类体系转换为IGBP分类体系;然后,从定性和定量两方面分析现有5种土地覆盖数据集(IGBP DISCover、UMD、GLC2000、MOD12Q1和GlobCover 2005)的空间一致性;并利用Google Earth高分影像选取两期验证样本评价5种土地覆盖数据集的精度。结果表明:同种地物在不同土地覆盖数据集之间的空间分布格局差异较大,且不同土地覆盖数据集之间的总体一致性系数较低;5种土地覆盖数据集中,GLC2000的总体精度和Kappa系数均最高,GlobCover 2005的总体精度和Kappa系数均最低。     

Integrating multiple land cover maps through a multi-criteria analysis to improve agricultural monitoring in Africa

Monitoring agricultural land cover is highly relevant for global early warning systems such as ASAP (Anomaly hot Spots of Agricultural Production), because it represents the basis for detecting production deficits in food security assessment. Given the significant inconsistencies among existing land cover datasets, there is a need to obtain a more accurate representation of the spatial distribution and extent of agricultural area in Africa. In this research, we explore a fusion approach that combines the strength of individual datasets and minimises their limitations. Specifically, a semi-automatic method is developed, relying on multi-criteria analysis (MCA) complemented with manual fine-tuning using the best-rated datasets, to generate two hybrid and static agricultural masks – one for cropland and another for grassland. Following a comprehensive selection of land cover maps, each dataset is evaluated at country level according to five criteria: timeliness, spatial resolution, comparison with FAO statistics, accuracy assessment and expert evaluation. A sensitivity analysis is performed, based on an evaluation of the impact of weight settings on the resulting land cover. The proposed methodology is capable of improving agricultural characterisation in Africa. As a result, two static masks at 250 m spatial resolution for the nominal year 2016 are provided.     

Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial–temporal variability is a challenging task.We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain.The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.     

Measuring land-use and land-cover change using the U.S. department of agriculture’s cropland data layer: Cautions and recommendations

Monitoring agricultural land is important for understanding and managing food production, environmental conservation efforts, and climate change. The United States Department of Agriculture's Cropland Data Layer (CDL), an annual satellite imagery-derived land cover map, has been increasingly used for this application since complete coverage of the conterminous United States became available in 2008. However, the CDL is designed and produced with the intent of mapping annual land cover rather than tracking changes over time, and as a result certain precautions are needed in multi-year change analyses to minimize error and misapplication. We highlight scenarios that require special considerations, suggest solutions to key challenges, and propose a set of recommended good practices and general guidelines for CDL-based land change estimation. We also characterize a problematic issue of crop area underestimation bias within the CDL that needs to be accounted for and corrected when calculating changes to crop and cropland areas. When used appropriately and in conjunction with related information, the CDL is a valuable and effective tool for detecting diverse trends in agriculture. By explicitly discussing the methods and techniques for post-classification measurement of land-cover and land-use change using the CDL, we aim to further stimulate the discourse and continued development of suitable methodologies. Recommendations generated here are intended specifically for the CDL but may be broadly applicable to additional remotely-sensed land cover datasets including the National Land Cover Database (NLCD), Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover products, and other regional, national, and global land cover classification maps.     

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.     

Change detection and classification of land cover at Hustai National Park in Mongolia

Land cover types of Hustai National Park (HNP) in Mongolia, a hotspot area with rare species, were classified and their temporal changes were evaluated using Landsat MSS TM/ETM data between 1994 and 2000. Maximum-likelihood classification analysis showed an overall accuracy of 88.0% and 85.0% for the 1994 and 2000 images, respectively. Kappa coefficients associated with the classification were resulted to 0.85 for 1994 and 0.82 for 2000 image. Land cover types revealed significant temporal changes in the classification maps between 1994 and 2000. The area has increased considerably by 166.5 km² for mountain steppe and by 12 km² for a sand dune. By contrast, agricultural areas and degraded areas affected by human being activity were decreased by 46.1 km² and 194.8 km² over the 6-year span, respectively. These areas were replaced by mountain steppe area. Specifically, forest area was noticeably fragmented, accompanied by the decrease of ∼400 ha. The forest area revealed a pattern with systematic gain and loss associated with the specific phenomenon called as ‘forest free-south slope’. We discussed the potential environmental conditions responsible for the systematic pattern and addressed other biological impacts by outbreaks of forest pests and ungulates.