Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data

ABSTRACT

In recent years, the data science and remote sensing communities have started to align due to user-friendly programming tools, access to high-end consumer computing power, and the availability of free satellite data. In particular, publicly available data from the European Space Agency’s Sentinel missions have been used in various remote sensing applications. However, there is a lack of studies that utilize these data to assess the performance of machine learning algorithms in complex boreal landscapes. In this article, I compare the classification performance of four non-parametric algorithms: support vector machines (SVM), random forests (RF), extreme gradient boosting (Xgboost), and deep learning (DL). The study area chosen is a complex mixed-use landscape in south-central Sweden with eight land-cover and land-use (LCLU) classes. The satellite imagery used for the classification were multi-temporal scenes from Sentinel-2 covering spring, summer, autumn and winter conditions. Using stratified random sampling, each LCLU class was allocated 1477 samples, which were divided into training (70%) and evaluation (30%) subsets. Accuracy was assessed through metrics derived from an error matrix, but primarily overall accuracy was used in allocating algorithm hierarchy. A two-proportion Z-test was used to compare the proportions of correctly classified pixels of the algorithms and a McNemar’s chi-square test was used to compare class-wise predictions. The results show that the highest overall accuracy was produced by support vector machines (0.758 ± 0.017), closely followed by extreme gradient boosting (0.751 ± 0.017), random forests (0.739 ± 0.018), and finally deep learning (0.733 ± 0.0023). The Z-test comparison of classifiers showed that a third of algorithm pairings were statistically different. On a class-wise basis, McNemar’s test results showed that 62% of class-wise predictions were significant from one another at the 5% level or less. Variable importance metrics show that nearly half of the top twenty Sentinel-2 bands belonged to the red edge (25%) and shortwave infrared (23%) portions of the electromagnetic spectrum, and were dominated by scenes from spring (38%) and summer (40%). The results are discussed within the scope of recent studies involving machine learning and Sentinel-2 data and key knowledge gaps identified. The article concludes with recommendations for future research.     

Analysis of coastal land use/land cover changes in the Indian Sunderbans using remotely sensed data

The world’s largest mangrove ecosystem, the Sunderbans is experiencing multidimensional threats of degradation. The present study was aimed to understand these problems and search for proper remedies by applying suitable remote sensing technologies. South-western parts of Indian Sunderbans Biosphere Reserve had been chosen for assessment of land use/land cover changes in between 1975 and 2006 by using multitemporal Landsat data. Results indicated considerable reduction of open mangrove stands and associated biodiversity mainly in the forest-habitation interference zones of Sunderbans. On the contrary, increase in the coverage of dense mangroves in the reserved forests had been observed indicating the existence of proper centralized management regimes. Overall, a cumulative loss of approximately 0.42% of its original mangrove cover in between 1975 and 2006 had been estimated for this part of the Sunderbans which was at parity with the findings of other studies in the Sunderbans or similar mangrove ecosystems of the tropics. Expansion of non agricultural lands in the last two decades was found to be related with the growth of new settlements, tourism infrastructure, and facilities. This transformation was attributed to the shifting of local peoples’ interest from traditional forestry and subsistence farming towards alternative occupations like shrimp culture, coastal tourism, and commercial fishing although environmentally hazardous livelihood activities like collection of prawn seeds along the riverbanks were still persistent.     

Assessment of Nigeriasat-1 satellite data for urban land use/land cover analysis using object-based image analysis in Abuja,Nigeria

This study assesses the usefulness of Nigeriasat-1 satellite data for urban land cover analysis by comparing it with Landsat and SPOT data. The data-sets for Abuja were classified with pixel- and object-based methods. While the pixel-based method was classified with the spectral properties of the images, the object-based approach included an extra layer of land use cadastre data. The classification accuracy results for OBIA show that Landsat 7 ETM, Nigeriasat-1 SLIM and SPOT 5 HRG had overall accuracies of 92, 89 and 96%, respectively, while the classification accuracy for pixel-based classification were 88% for Landsat 7 ETM, 63% for Nigeriasat-1 SLIM and 89% for SPOT 5 HRG. The results indicate that given the right classification tools, the analysis of Nigeriasat-1 data can be compared with Landsat and SPOT data which are widely used for urban land use and land cover analysis.     

基于地表覆盖/土地利用数据的沁河源区生态保护红线边界处理

边界处理是指借助地理信息系统软件,将生态保护红线从栅格形式的生态评估结果落实到具有明确边界的地块上,也称为红线的“落图”过程,是生态保护红线划定的重要流程,也是红线精准落地和有效管控的必要条件。本文以沁河源区为例,利用地理信息系统软件,基于地表覆盖、土地利用两种数据分别对生态功能极重要区红线进行落图。结果表明：两种数据均适用于红线边界处理,并且联合应用时处理结果更佳;沁河源区生态功能极重要区栅格面积为877.87 km²,联合应用两种数据处理后的红线矢量面积为877.73 km²,占研究区总面积的64.51%;研究结果可为生态功能重要区域生态保护红线边界的优化、调整提供参考。     

Stylized environments and ABMs: educational tools for examining the causes and consequences of land use/land cover change

A challenge in land change science is to assess the causes and consequences of LULC change and associated pattern–process relations. Increasingly, land change organizations are examining land use at local to global scales for historical, contemporary and future periods through scenarios that assess population–environment interactions. Spatial analytical tools in GIScience are being used to link people and environment and to search for the distal and proximate factors that affect local to global land use patterns. Spatial simulation models that rely upon complexity theory as the framework and agent-based models as the analytical approach offer the capability to inform through experimentation about land issues important to science and society. Using a stylized landscape where a selected set of key social, geographical and ecological elements are spatially organized, we describe how land dynamics can be examined through agent-based models as educational tools that are useful in the classroom, boardroom and public forums.     

Remote sensing-derived national land cover land use maps: a comparison for Malawi

Reliable land cover land use (LCLU) information, and change over time, is important for Green House Gas (GHG) reporting for climate change documentation. Four different organizations have independently created LCLU maps from 2010 satellite imagery for Malawi for GHG reporting. This analysis compares the procedures and results for those four activities. Four different classification methods were employed; traditional visual interpretation, segmentation and visual labelling, digital clustering with visual identification and supervised signature extraction with application of a decision rule followed by analyst editing. One effort did not report classification accuracy and the other three had very similar and excellent overall thematic accuracies ranging from 85 to 89%. However, despite these high thematic accuracies there were very significant differences in results. National percentages for forest ranged from 18.2 to 28.7% and cropland from 40.5 to 53.7%. These significant differences are concerns for both remote-sensing scientists and decision-makers in Malawi.     

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.     

基于遥感技术的海岸带土地利用、覆被信息提取方法研究

本文以地处莱州湾东海岸的莱州市为研究区域,以TM影像为主要信息源,在分析各类地物分类效果的基础上,结合区域特点和地物光谱特征,综合利用两期典型季相的遥感图像,并针对每一种土地利用类型确定最佳信息提取影像,提出了基于光谱、地形等多种知识的分层信息提取方法.结果表明该方法分层目标明确,分类精度和计算效率都有明显提高,且在数...     

An exploratory analysis of usability of Flickr tags for land use/land cover attribution

This study explored the land use/land cover (LULC) separability by the machine-generated and user-generated Flickr photo tags (i.e. the auto-tags and the user-tags, respectively), based on an authoritative LULC dataset for San Diego County in the United States. Ten types of LULCs were derived from the authoritative dataset. It was observed that certain types of the reclassified LULCs had abundant tags (e.g. the parks) or a high tag density (e.g. the commercial lands), compared with the less populated ones (e.g. the agricultural lands). Certain highly weighted terms of the tags derived based on a term frequency–inverse document frequency weighting scheme were helpful for identifying specific types of the LULCs, especially for the commercial recreation lands (e.g. the zoos). However, given the 10 sets of tags retrieved from the corresponding 10 types of LULCs, one set of tags (all the tags located at one specific type of the LULCs) could not fully delineate the corresponding LULC due to semantic overlaps, according to a latent semantic analysis.     

面向对象的沿海地区土地利用/覆被信息提取研究

土地利用/覆被(LUC)可为土地资源领域相关研究提供基础数据.本文构建了面向对象的LUC分类方法,并以沿海特殊土地类型区连云港市为例,应用Landsat 8影像开展了实证研究。结果表明:①总体分类精度达到85.06%,总体Kappa系数为0.83,超过了0.7的最低允许判别精度;②该方法可以有效地减少研究区因南北部区域耕地植被覆盖度不同导致的错分现象,并可以用于盐田与滩涂信息的提取工作;③该方法既可为研究区土地利用相关研究提供符合精度要求的数据.也可为其他沿海地区进行土地利用/覆被信息提取工作提供参考和借鉴。     

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.     

Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data

In this study, we explored the spatial and temporal patterns of land cover and land use (LCLU) and population change dynamics in the St. Louis Metropolitan Statistical Area. The goal of this paper was to quantify the drivers of LCLU using long-term Landsat data from 1972 to 2010. First, we produced LCLU maps by using Landsat images from 1972, 1982, 1990, 2000, and 2010. Next, tract level population data of 1970, 1980, 1990, 2000, and 2010 were converted to 1-km square grid cells. Then, the LCLU maps were integrated with basic grid cell data to represent the proportion of each land cover category within a grid cell area. Finally, the proportional land cover maps and population census data were combined to investigate the relationship between land cover and population change based on grid cells using Pearson's correlation coefficient, ordinary least square (OLS), and local level geographically weighted regression (GWR). Land cover changes in terms of the percentage of area affected and rates of change were compared with population census data with a focus on the analysis of the spatial-temporal dynamics of urban growth patterns. The correlation coefficients of land cover categories and population changes were calculated for two decadal intervals between 1970 and 2010. Our results showed a causal relationship between LCLU changes and population dynamics over the last 40 years. Urban sprawl was positively correlated with population change. However, the relationship was not linear over space and time. Spatial heterogeneity and variations in the relationship demonstrate that urban sprawl was positively correlated with population changes in suburban area and negatively correlated in urban core and inner suburban area of the St. Louis Metropolitan Statistical Area. These results suggest that the imagery reflects processes of urban growth, inner-city decline, population migration, and social spatial inequality. The implications provide guidance for sustainable urban planning and development. We also demonstrate that grid cells allow robust synthesis of remote sensing and socioeconomic data to advance our knowledge of urban growth dynamics from both spatial and temporal scales and its association with population change.     

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.     

Changing landscape in the Three Gorges Reservoir Area of Yangtze River from 1977 to 2005: Land use/land cover,vegetation cover changes estimated using multi-source satellite data

The eco-environment in the Three Gorges Reservoir Area (TGRA) in China has received much attention due to the construction of the Three Gorges Hydropower Station. Land use/land cover changes (LUCC) are a major cause of ecological environmental changes. In this paper, the spatial landscape dynamics from 1978 to 2005 in this area are monitored and recent changes are analyzed, using the Landsat TM (MSS) images of 1978, 1988, 1995, 2000 and 2005. Vegetation cover fractions for a vegetation cover analysis are retrieved from MODIS/Terra imagery from 2000 to 2006, being the period before and after the rising water level of the reservoir. Several analytical indices have been used to analyze spatial and temporal changes. Results indicate that cropland, woodland, and grassland areas reduced continuously over the past 30 years, while river and built-up area increased by 2.79% and 4.45% from 2000 to 2005, respectively. The built-up area increased at the cost of decreased cropland, woodland and grassland. The vegetation cover fraction increased slightly. We conclude that significant changes in land use/land cover have occurred in the Three Gorges Reservoir Area. The main cause is a continuous economic and urban/rural development, followed by environmental management policies after construction of the Three Gorges Dam.     

泽州猕猴自然保护区土地利用／覆被及景观格局变化研究

文中以2007年第二次全国土地调查和2015年地理国情普查所提供高分辨率地表覆盖数据为主要信息源,采用地理信息系统技术和景观生态学数量分析相结合的方法对2007—2015年泽州猕猴自然保护区土地利用/覆被变化(LUCC)、景观格局变化及其驱动力进行研究分析。结果表明,近8年来,受人为、经济发展和政策因素影响,泽州猕猴自然保护区整体景观破碎度增强,形状更加规律化、简单化,景观分布聚集度变大,生态连通性增强,多样性减少。研究结果为泽州猕猴自然保护区生态系统的健康发展、生态环境的改善与经济社会的和谐发展提供参考。     

Comparison of large-area land cover products with national forest inventories and CORINE land cover in the European Alps

Considerable efforts have recently resulted in the development of global land cover data at large spatial scales. The main objective of this study is a comparison of different AVHRR- and MODIS-based forest and land cover products at the scale of the European Alps: a large natural ecosystem that is exposed to both natural environmental threats and human impacts and exploitation. In a first test, the accuracy of land cover products in predicting the overall amount of forest across national boundaries was assessed using national forest inventory statistics. Both variants of forest class combinations resulted in a general overestimation of the forest area. The IGBP 2.0 cover performed best with an overall mean absolute error of 13% and a bias of 0%. In a second test, large-area land cover products were tested for accuracy in predicting 13 aggregated land cover types in a spatially explicit manner using CORINE land cover as reference dataset. Due to data inconsistencies, partly insufficient spatial resolution, steep terrain and land use heterogeneity of the European Alps, only partly satisfactory results were obtained.     

基于知识的分层综合分类法在土地利用/土地覆盖遥感信息提取中的应用

土地利用/土地覆盖数据的获取是研究LUCC的重要基础工作。随着遥感技术的飞速发展,通过遥感提取土地利用/土地覆盖专题信息已成为LUCC研究必不可少的一步。目前遥感专题信息提取水平相对滞后于遥感数据获取,为了提高遥感数据在土地利用/土地覆盖的应用,寻找一种较好的、具有相对适用性的方法是目前遥感应用的一个迫切要求。本文比较了目前比较常用的几种土地利用/土地覆盖遥感信息提取方法,分别以西部干旱区(柴达木盆地)和东部地区(鄱阳湖地区)为例,提出在GIS支持下基于知识的分层综合分类方法,并通过和其他几种常用方法进行比较分析,得到如下结果:在自然环境相差较大的柴达木盆地和鄱阳湖地区,采用了GIS支持下基于知识的分层综合分类方法的提取精度均要比单独采用最大似然法、纹理分析法、神经网络分类法等方法的总体精度高出25%,Kappa系数高出0.2。由此可以说明了该方法对于土地利用/土地覆盖专题信息的提取是可行的,同时它也具有一定的适用性。     

Spatiotemporal analysis of land use/cover changes in Nanchang area,China

Based on remote sensing and Geographic Information System techniques, multi-temporal Landsat data were used to monitor land use/cover changes (LUCC) in the Nanchang area during 1989–2010. Quantitative and spatiotemporal indicators derived from the transition matrix for overall changes, intensity analysis for inter-category transitions, and spatiotemporal analysis of intra-category variations were constructed to analyze the spatiotemporal LUCC evolution. The study results show that urban and industrial land rapidly and continually expanded. The spatial extent of rural settlements shrunk while their distribution density increased. Cultivated land showed a tendency of spatially continuous loss and fragmented distribution, whereas the spatial pattern of forest tended to concentrate. The extents of ponds and reservoirs remained stable, but spatial changes were obvious. Rivers and bottomlands decreased significantly and tended toward fragmentation. The overall LUCC pattern showed that construction land rapidly sprawled while cultivated land decreased substantially, indicating an accelerating stage of urbanization and industrialization. Although rigid land requirements for urbanization had to be satisfied, land development and consolidation across forests and water body areas, as well as adjustment of agricultural structure, laid the foundation for a transition toward intensive land use in the Nanchang area.