Multiple factors influence the consistency of cropland datasets in Africa

Accurate geo-information of cropland is critical for food security strategy development and grain production management, especially in Africa continent where most countries are food-insecure. Over the past decades, a series of African cropland maps have been derived from remotely-sensed data, existing comparison studies have shown that inconsistencies with statistics and discrepancies among these products are considerable. Yet, there is a knowledge gap about the factors that influence their consistency. The aim of this study is thus to estimate the consistency of five widely-used cropland datasets (MODIS Collection 5, GlobCover 2009, GlobeLand30, CCI-LC 2010, and Unified Cropland Layer) in Africa, and to explore the effects of several limiting factors (landscape fragmentation, climate and agricultural management) on spatial consistency. The results show that total cropland area for Africa derived from GlobeLand30 has the best fitness with FAO statistics, followed by MODIS Collection 5. GlobCover 2009, CCI-LC 2010, and Unified Cropland Layer have poor performances as indicated by larger deviations from statistics. In terms of spatial consistency, disagreement is about 37.9 % at continental scale, and the disparate proportion even exceeds 50 % in approximately 1/3 of the countries at national scale. We further found that there is a strong and significant correlation between spatial agreement and cropland fragmentation, suggesting that regions with higher landscape fragmentation generally have larger disparities. It is also noticed that places with better consistency are mainly distributed in regions with favorable natural environments and sufficient agricultural management such as well-developed irrigated technology. Proportions of complete agreement are thus located in favorable climate zones including Hot-summer Mediterranean climate (Csa), Subtropical highland climate (Cwb), and Temperate Mediterranean climate (Csb). The level of complete agreement keeps rising as the proportion of irrigated cropland increases. Spatial agreement among these datasets has the most significant relationship with cropland fragmentation, and a relatively small association with irrigation area, followed by climate conditions. These results can provide some insights into understanding how different factors influence the consistency of cropland datasets, and making an appropriate selection when using these datasets in different regions. We suggest that future cropland mapping activities should put more effort in those regions with significant disagreement in Sub-Saharan Africa.     

基于空间抽样的上海市GlobeLand302020数据精度评估

针对上海GlobeLand302020土地覆盖产品,应用顾及空间异质性的类别-异质性分层抽样方法进行抽样,将研究区各地图类别划分为匀质区和异质区子类,按照内曼分配方法布设验证样本,在研究区域内抽取2500个验证像素,并参考同时期多源Google Earth高分遥感影像数据进行样本判读,获得参考分类即真实类别信息,对区域...     

Characterizing and monitoring global landscapes using GlobeLand30 datasets: the first decade of the twenty-first century

Global land cover maps are important sources of information for a wide range of studies including land change analysis and climate change research. While the global land cover maps attempt to present a consistent and homogenous data in terms of the production process, the existing datasets offer coarse resolution data, e.g. 1000 m for IGBP DISCover and 300 m for GlobeCover 2009 that is oftentimes challenging. Recently, GlobeLand30 data based on Landsat archive for two timestamps of 2000 and 2010 has been released. It presents a finer spatial resolution of 30 m, which provides numerous opportunities for a wide range of studies. The main objective of this study is to use this dataset for characterizing global land cover patterns, monitoring, and identifying extreme land change cases with their types and magnitude. The findings reveal massive land change patterns including deforestation, desertification, shrinkage of water bodies, and urbanization across the globe. The results and discussions of this research can help policy-makers, environmental planners, ecosystem services providers and climate change researchers to gain finer insights about the forms of global land change. Future research calls for further investigation of the underlying causes of the massive changes and their consequences on our ecosystems and human populations.     

Open land cover from OpenStreetMap and remote sensing

OpenStreetMap (OSM) tags were used to produce a global Open Land Cover (OLC) product with fractional data gaps available at osmlanduse.org. Data gaps in the global OLC map were filled for a case study in Heidelberg, Germany using free remote sensing data, which resulted in a land cover (LC) prototype with complete coverage in this area. Sixty tags in the OSM were used to allocate a Corine Land Cover (CLC) level 2 land use classification to 91.8% of the study area, and the remaining gaps were filled with remote sensing data. For this case study, complete are coverage OLC overall accuracy was estimated 87%, which performed better than the CLC product (81% overall accuracy) of 2012. Spatial thematic overlap for the two products was 84%. OLC was in large parts found to be more detailed than CLC, particularly when LC patterns were heterogeneous, and outperformed CLC in the classification of 12 of the 14 classes. Our OLC product represented data created in different periods; 53% of the area was 2011–2016, and 46% of the area was representative of 2016–2017.     

Tree cover mapping based on Sentinel-2 images demonstrate high thematic accuracy in Europe

The spatial and temporal distribution of trees has a large impact on human health and the environment through contributions to important climate mechanisms as well as commercial, recreational and social activities in society. A range of tree mapping methodologies has been presented in the literature, but tree cover estimates still differ widely between the individual datasets, and comparisons of the thematic accuracy of the resulting tree maps are rather scarce. The Copernicus Sentinel-2 satellites, which were launched in 2015 and 2017, have a combination of high spatial and temporal resolution. Given that this is a new satellite, a substantial amount of research on development of tree mapping algorithms as well as accuracy assessment of said algorithms have to be done in the years to come. To contribute to this process, a tree map produced through unsupervised classification was created for six Sentinel-2 tiles. The agreement between the tree map and the corresponding national forest inventory, as a function of the band combination chosen, was analysed and the thematic accuracy was assessed for two out of the six tiles. The results show that the highest agreement between the present tree map and the national forest inventory was found for bands 2, 3, 6 and 12. The present tree map has a relative difference in tree cover between 8% and 79% compared to previous estimates, but results are characterised by large scatter. Lastly, it is shown that the overall thematic accuracy of the present map is up to 90%, with the user’s accuracy ranging from 34.85% to 92.10%, and the producer’s accuracy ranging from 23.80% to 97.60% for the various thematic classes. This demonstrates that tree maps with high thematic accuracy can be produced from Sentinel-2. In the future the thematic accuracy can be increased even more through the use of temporal averaging in the mapping procedure, which will enable an accurate estimate of the European tree cover.     

Collaborative validation of GlobeLand30:Methodology and practices

30-m Global Land Cover(GLC)data products permit the detection of land cover changes at the scale of most human land activities,and are therefore used as fundamental information for sustainable development,environmental change studies,and many other societal benefit areas.In the past few years,increasing efforts have been devoted to the accuracy assessment of GlobeLand30 and other finer-resolution GLC data products.However,most of them were conducted either within a limited percentage of map sheets selected from a global scale or in some individual countries(areas),and there are still many areas where the uncertainty of 30-m resolution GLC data products remains to be validated and documented.In order to promote a comprehensive and collaborative validation of 30-m GLC data products,the GEO Global Land Cover Community Activity had organized a project from 2015 to 2017,to examine and explore its major problems,including the lack of international agreed validation guidelines and on-line tools for facilitating collaborative validation activities.With the joint effort of experts and users from 30 GEO member countries or participating organizations,a technical specification for 30-m GLC validation was developed based on the findings and experiences.An on-line validation tool,GLCVal,was developed by integrating land cover validation procedures with the service computing technologies.About 20 countries(regions)have completed the accuracy assess-ment of GlobeLand30 for their territories with the guidance of the technical specification and the support of GLCVal.     

顾及栅格空间拓扑关系的地表覆盖时空目标不一致性探测方法

地表覆盖与更新是地理国情监测、环境变化评估、生态系统保护等不可或缺的基础地理信息。遥感制图技术已成为地表覆盖信息提取的重要手段,但因地物光谱、纹理及时相等特征复杂性,地表覆盖更新数据往往存在错分、漏分,从而导致地表覆盖时空目标不一致。现有地表覆盖更新数据不一致性探测主要以人工检查为主、部分自动化为辅的方式,生产实践中需要大量的作业人员与时间,缺乏行之有效的不一致性自动化探测工具。本文研究分析了栅格地表覆盖更新数据不一致性检查面临的挑战,提出了基于复合逻辑量词的栅格空间拓扑关系计算方法、基于置信区间的更新期地表覆盖错分目标初判规则构建,以及利用空间约束多重匹配的更新期错分目标后验判断,形成了“关系-规则-判断”的地表覆盖时空目标不一致性探测体系。试验以山东临朐、垦利GlobeLand30数据为研究对象,经与统计一致性检核方法对比分析、参照真实地表影像数据,实现了地表覆盖时空目标不一致性探测与有效性检验,验证了探测方法可行性。     

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.     

Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data

In this study, we test the use of Land Use and Coverage Area frame Survey (LUCAS) in-situ reference data for classifying high-resolution Sentinel-2 imagery at a large scale. We compare several pre-processing schemes (PS) for LUCAS data and propose a new PS for a fully automated classification of satellite imagery on the national level. The image data utilizes a high-dimensional Sentinel-2-based image feature space. Key elements of LUCAS data pre-processing include two positioning approaches and three semantic selection approaches. The latter approaches differ in the applied quality measures for identifying valid reference points and by the number of LU/LC classes (7–12). In an iterative training process, the impact of the chosen PS on a Random Forest image classifier is evaluated. The results are compared to LUCAS reference points that are not pre-processed, which act as a benchmark, and the classification quality is evaluated by independent sets of validation points. The classification results show that the positional correction of LUCAS points has an especially positive effect on the overall classification accuracy. On average, this improves the accuracy by 3.7%. This improvement is lowest for the most rigid sample selection approach, PS₂, and highest for the benchmark data set, PS₀. The highest overall accuracy is 93.1% which is achieved by using the newly developed PS₃; all PS achieve overall accuracies of 80% and higher on average. While the difference in overall accuracy between the PS is likely to be influenced by the respective number of LU/LC classes, we conclude that, overall, LUCAS in-situ data is a suitable source for reference information for large scale high resolution LC mapping using Sentinel-2 imagery. Existing sample selection approaches developed for Landsat imagery can be transferred to Sentinel-2 imagery, achieving comparable semantic accuracies while increasing the spatial resolution. The resulting LC classification product that uses the newly developed PS is available for Germany via DOI: https://doi.org/10.15489/1ccmlap3mn39.     

顾及城乡差异的大区域人口密度估算——以山东省为例

现有大区域人口密度估算结果大多是在千米级尺度上,仅能宏观地反映城乡人口分布的范围,无法准确地刻画城乡人口空间分布的细节特征。本文将首套30m全球地表覆盖数据(GlobeLand30)引入城乡人口密度估算中,基于实现城乡划分的GlobeLand30人造地表数据,在城镇区域运用夜间灯光强度与人口的相关性将城镇人口细划到30m尺度上来估算城镇人口密度;在乡村区域引入样方估算的方法修正乡村居民地面积以估算乡村人口密度。以山东省为试验区的研究表明,本文方法无论在城乡居民地刻画还是人口空间分布的表达上均优于参考数据,所使用的GlobeLand30的全球性也保证了该方法推广的可行性。     

Development of large-area land cover and forest change indicators using multi-sensor Landsat imagery: Application to the Humber River Basin,Canada

Monitoring ecological indicators is important for assessing impacts of human activities on ecosystems. A means of identifying and applying appropriate indicators is a prerequisite for: environmental assessment; better assessment and understanding of ecosystem health; elucidation of biogeochemical trends; and more accurate predictions of future responses to global change, particularly those due to anthropogenic disturbance. The challenge is to derive meaningful indicators of change that capture the complexities of ecosystems yet can be monitored consistently over large areas and across time. In this study, methods for monitoring indicators of land cover (LC) and forest change were developed using multi-sensor Landsat imagery. Mapping and updating procedures were applied to the Humber River Basin (HRB) in Newfoundland and Labrador, one of four test sites in Canada selected for testing the development of national-scale methods. Procedures involved unsupervised clustering and labeling of baseline imagery, followed by image-to-image spectral clustering to derive binary change masks within which new LC types were classified for non-baseline imagery. Updated maps were compatible with the baseline map and reflected change in LC for three time periods: 1976–1990, 1990–2001, and 2001–2007. From the LC products, several change indicators were quantified including: forest depletion, forest regeneration, forest change, net forest change, and annual rates of change. The procedures were validated using field plots to assess the accuracy of the 2007 LC product (74.2% for 10 LC classes) and change classes observed from 2001 to 2007 (87.8% for four change classes: depletion, regeneration, non-treed class no change, and treed class no change). Methods were considered to be highly efficient and operationally feasible over large areas spanning multiple Landsat scenes. Specific results for the test site provided trend information supporting land and resource management in the HRB region.     

Integrating global land cover products for improved forest cover characterization: an application in North America

Six widely used coarse-resolution global land cover data-sets – Global Land Cover Characterization (GLCC), Global Land Cover 2000 (GLC2000), GlobCover land cover product (GlobCover), MODIS land cover product (MODIS LC), the University of Maryland land cover product (UMD LC), and the MODIS Vegetation Continuous Fields tree cover layer (MODIS VCF) disagree substantially in their estimates of forest cover. Employing a regression tree model trained on higher-resolution, Landsat-based data, these multisource multiresolution maps were integrated for an improved characterization of forest cover over North America. Evaluated using a withheld test sample, the integrated percent forest cover (IPFC) data-set has a root mean square error of 11.75% – substantially better than the 17.37% of GLCC, 17.61% of GLC2000, 17.96% of GlobCover, 15.23% of MODIS LC, 19.25% of MODIS VCF, and 15.15% of UMD LC, respectively. Although demonstrated for forest, this approach based on integration of multiple products has potential for improved characterization of other land cover types as well.     

What is the difference between two maps? A remote senser’s view

In remote sensing, thematic map comparison is often undertaken on a per-pixel basis and based upon measures of classification agreement. Here, the degree of agreement between two thematic maps, and so the difference between the pair, was evaluated through visual and quantitative analyses for two scenarios. Quantitative assessments were based on basic site-specific measures of agreement that are used widely in accuracy assessment (e.g. the overall percentage of pixels with the same class label in each of the two maps and the kappa coefficient of agreement) as well as an information theory based approach that allows the degree of mutual or shared information to be assessed even if different classification schemes have been used to produce the maps. The results indicated that in the first map comparison scenario, focused on labelling, there was a fair degree of correspondence between the maps but with an overall difference in information content of ∼42%. In the second comparison scenario, focused on change in time, considerable change had occurred with a change in class label for ∼42% of the pixels. It was also apparent that global assessments masked local scale changes.     

Accuracy assessment of MODIS/Terra snow cover product for parts of Indian Himalayas

Snow cover mapping is important for snow and glacier-related research. The spatial and temporal distribution of snow cover area is a fundamental input to the atmospheric models, snowmelt runoff models and climate models, as well as other applications. Daily snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite were retrieved for the period between 2004 and 2007, and pixels in these images were classified as cloud, snow or snow-free. These images have then been compared with ground snow depth (SD) measurements from the four observatories located at different parts of Himalayas. Comparison of snow maps with in situ data showed good agreement with overall accuracies in between 78.15 and 95.60%. When snow cover was less, MODIS data were found to be less accurate in mapping snow cover region. As the SD increases, the accuracy of MODIS snow cover maps also increases.     

Development of S-NPP VIIRS global surface type classification map using support vector machines

With the launch of the Joint Polar Satellite System (JPSS)/Suomi National Polar-orbiting Partnership (S-NPP) satellite in October 2011, many of the terrestrial remote sensing products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), such as the global land cover map, have been inherited and expanded into the JPSS/S-NPP mission using the new Visible Infrared Imaging Radiometer Suite (VIIRS) data. In this study, an improved algorithm including the use of a new classifier support vector machines (SVM) classifier was proposed to produce VIIRS surface type maps. In addition to the new classification algorithm, a new post-processing strategy involving the use of new ancillary data to refine the classification output is implemented. As a result, the new global International Geosphere-Biosphere Programme (IGBP) map based on the 2014 VIIRS surface reflectance data was generated with a 78.5 ± 0.6% overall classification accuracy. The new map was compared to a previously delivered VIIRS surface type map, and to the MODIS land cover product. Validation results including the error matrix, overall accuracy, and the user’s and producer’s accuracy suggest the new global surface type map provides similar classification accuracy compared to the old VIIRS surface type map, with higher accuracy achieved in agricultural types.     

Crop type mapping using LiDAR,Sentinel-2 and aerial imagery with machine learning algorithms

LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field - often informed by data collected during ground and aerial surveys. However, manual digitizing and labeling is time-consuming, expensive and subject to human error. Automated remote sensing methods is a cost-effective alternative, with machine learning gaining popularity for classifying crop types. This study evaluated the use of LiDAR data, Sentinel-2 imagery, aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area. Different combinations of the three datasets were evaluated along with ten machine learning. The classification results were interpreted by comparing overall accuracies, kappa, standard deviation and f-score. It was found that LiDAR data successfully differentiated between different crop types, with XGBoost providing the highest overall accuracy of 87.8％. Furthermore, the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data, with LiDAR obtaining a mean overall accuracy of 84.3％ and Sentinel-2 a mean overall accuracy of 83.6％. However, the combination of all three datasets proved to be the most effective at differentiating between the crop types, with RF providing the highest overall accuracy of 94.4％. These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.     

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。     

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.     

基于GlobeLand30的全球城乡建设用地空间分布与变化统计分析

城乡建设用地分布与变化是人类活动的直观标志和生态足迹,在环境变化研究、地理国(世)情监测和可持续发展研究等方面发挥着重要作用。以往人们对一些城市、区域或国家的城乡建设用地分布与变化进行过较为深入系统的研究,但在全球尺度上,这方面研究尚为空白。本文是利用我国自主研制的世界上首套30m空间分辨率全球地表覆盖数据集GlobeLand30的人造地表数据层,首次开展了全球城乡建设用地的空间分布及变化的统计分析。它采用用地面积、构成占比和增量占比等主要指标,统计全球范围内城乡建设用地的空间分布及2000年至2010年10年间的变化,重点分析了2010年全球、各大洲及主要国家的城乡建设用地分布现状与地域差异,2000年至2010年全球、主要国家的建设用地变化以及其主要土地来源。研究结果表明,2010年全球城乡建设用地总面积为118.75×104km2,占全球陆表面积的0.88%;2000年至2010年全球城乡建设用地面积增加了5.74×104 km2,变化率为5.08%,其中,中国和美国新增城乡建设用地约占全球的一半;新增城乡建设用地占用最多的是耕地,占总量的50.26%。这些为研究全球陆表人类活动的空间分布特征与变化趋势提供了翔实的信息和知识。     

MTSAT-2静止气象卫星中国区域雪盖监测

利用MTSAT-2静止气象卫星数据开展了中国区域的雪盖监测研究,结合MODIS雪盖产品及站点雪深观测数据对判识结果进行对比分析和验证。首先,根据MTSAT-2静止气象卫星数据特点,进行角度效应校正及多时相数据合成,以减少云对图像的影响;其次,根据多个雪盖判识因子建立中国区域雪盖判识算法;最后,对比分析2011年1月份MTSAT-2和MODIS雪盖判识结果,并使用站点观测数据进行精度验证。研究表明:(1)MTSAT-2雪盖判识受云影响比例约30%,MODIS雪盖产品受云影响比例约60%,MTSAT-2去云效果明显。(2)无云情况下,MTSAT-2雪盖判识和MODIS雪盖产品判识精度均高于92%;有云覆盖时,MTSAT-2判识精度约65%,优于MODIS雪盖产品35%的判识精度。(3)MTSAT-2静止气象卫星在保持高积雪判识精度的前提下,可以更有效减少云对雪盖判识影响,实时获取更多地表真实信息。该研究对中国区域雪盖信息准确监测、气候变化研究以及防灾减灾等具有重要意义。