Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the Random Forest algorithm

Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the Random Forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in structural characteristics between most crops were the largest. At the same time, the images in spring (April and May) and autumn (October) also contributed to the crop classification since they respectively provided unique information for discriminating fruit crops (almond and walnut) as well as summer crops (corn, sunflower, and tomato). As a result, the combined use of only four acquisitions (dated May, July, August, and October for 2011 and April, June, August, and October for 2014) was adequate to achieve a nearly-optimal overall accuracy. In light of the promising classification accuracies demonstrated in this research, it becomes increasingly viable to provide accurate and up-to-date crops inventories over large areas based solely on multitemporal polarimetric SAR.     

A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level

Optical Earth Observation data with moderate spatial resolutions, typically MODIS (Moderate Resolution Imaging Spectroradiometer), are of particular value to environmental applications due to their high temporal and spectral resolutions. Time-series of MODIS data capture dynamic phenomena of vegetation and its environment, and are considered as one of the most effective data sources for land cover mapping at a regional and national level. However, the time-series, multiple bands and their derivations such as NDVI constitute a large volume of data that poses a significant challenge for automated mapping of land cover while optimally utilizing the information it contains. In this study, time-series of 10-day cloud-free MODIS composites and its derivatives – NDVI and vegetation phenology information, are fully assessed to determine the optimal data sets for deriving land cover. Three groups of variable combinations of MODIS spectral information and its derived metrics are thoroughly explored to identify the optimal combinations for land cover identification using a data mining tool.The results, based on the assessment using time-series of MODIS data, show that in general using a longer time period of the time-series data and more spectral bands could lead to more accurate land cover identification than that of a shorter period of the time-series and fewer bands. However, we reveal that, with some optimal variable combinations of few bands and a shorter period of time-series data, the highest possible accuracy of land cover classification can be achieved.     

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing

New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods.     

MODIS土地覆盖分类的尺度不确定性研究

以空间异质性较强的枯水期鄱阳湖为研究区,以搭载于同一卫星平台、具有同一观测时间和较高空间分辨率的ASTER数据为参照,分析研究了MODIS数据在土地覆盖分类中由空间尺度带来的不确定性。首先基于MODIS三角权重函数,建立了从ASTER到MODIS的尺度转换方法;然后对不同空间分辨率的数据进行土地覆盖分类,并基于误差矩阵和线性模型分析了MODIS土地覆盖分类结果的误差来源。结果表明,空间分辨率和光谱分辨率与成像方式这两类因素对MODIS与ASTER分类结果差异的贡献比例约为(6.6—11.2):2;MODIS像元尺度对研究区水体的分类不确定性影响较低,而对森林的不确定性影响可达63%。由此可见,在基于MODIS数据的土地覆盖分类研究中,空间尺度所产生的不确定性是比较显著的。这些研究结果对于土地覆盖分类及变化检测、尺度效应和景观生态学不确定性研究,有积极的参考意义。     

Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification

LiDAR has been an effective technology for acquiring urban land cover data in recent decades. Previous studies indicate that geometric features have a strong impact on land cover classification. Here, we analyzed an urban LiDAR dataset to explore the optimal feature subset from 25 geometric features incorporating 25 scales under 6 definitions for urban land cover classification. We performed a feature selection strategy to remove irrelevant or redundant features based on the correlation coefficient between features and classification accuracy of each features. The neighborhood scales were divided into small (0.5–1.5 m), medium (1.5–6 m) and large (>6 m) scale. Combining features with lower correlation coefficient and better classification performance would improve classification accuracy. The feature depicting homogeneity or heterogeneity of points would be calculated at a small scale, and the features to smooth points at a medium scale and the features of height different at large scale. As to the neighborhood definition, cuboid and cylinder were recommended. This study can guide the selection of optimal geometric features with adaptive neighborhood scale for urban land cover classification.     

Fusion of PolSAR and PolInSAR data for land cover classification

The main research goal of this study is to investigate the complementarity and fusion of different frequencies (L- and P-band), polarimetric SAR (PolSAR) and polarimetric interferometric (PolInSAR) data for land cover classification. A large feature set was derived from each of these four modalities and a two-level fusion method was developed: Logistic regression (LR) as ‘feature-level fusion’ and the neural-network (NN) method for higher level fusion. For comparison, a support vector machine (SVM) was also applied. NN and SVM were applied on various combinations of the feature sets.     

The analysis of SPOT-5 characteristics on land cover Classification

<正>KnowledgeaboutlandcoverandlandusehasbecomeincreasinglyimportantastheNationplanstoovercometheproblemsofuncontrolleddevelopment,deterioratingenvironmentalquality,lossofprimeagriculturallandsetc.Landuseandlandcoverdataareneededintheanalysisofenvironmentalprocessesandproblemstoknowiflivingconditionsandstandardsaretobeimprovedormaintainedatcurrentlevels.     

The Rotation Forest algorithm and object-based classification method for land use mapping through UAV images

This study aims to test the performance of the Rotation Forest (RTF) algorithm in urban and rural areas that have similar characteristics using unmanned aerial vehicle (UAV) images to produce the most up-to-date and accurate land use maps. The performance of the RTF algorithm was compared to other ensemble methods such as Random Forest (RF) and Gentle AdaBoost (GAB) for object-based classification. RGB bands and other variables (i.e. ratio, mean, standard deviation, ... etc.) were also used in classification. The accuracy assessments showed that the RTF method, with 92.52 and 91.29% accuracies, performed better than the RF (2 and 4%) and GAB (5 and 8%) methods in urban and rural areas, respectively. The significance of differences in classification methods was also analysed using the McNemar test. Consequently, this study shows the success of the RTF algorithm in the object-based classification of UAV images for land use mapping.     

Classifier ensembles for land cover mapping using multitemporal SAR imagery

SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.     

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.     

Object-based classification of hyperspectral data using Random Forest algorithm

This paper presents a new framework for object-based classification of high-resolution hyperspectral data. This multi-step framework is based on multi-resolution segmentation (MRS) and Random Forest classifier (RFC) algorithms. The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images. Given the high number of input features, an automatic method is needed for estimation of this parameter. Moreover, we used the Variable Importance (VI), one of the outputs of the RFC, to determine the importance of each image band. Then, based on this parameter and other required parameters, the image is segmented into some homogenous regions. Finally, the RFC is carried out based on the characteristics of segments for converting them into meaningful objects. The proposed method, as well as, the conventional pixel-based RFC and Support Vector Machine (SVM) method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics. These data were acquired by the HyMap, the Airborne Prism Experiment (APEX), and the Compact Airborne Spectrographic Imager (CASI) hyperspectral sensors. The experimental results show that the proposed method is more consistent for land cover mapping in various areas. The overall classification accuracy (OA), obtained by the proposed method was 95.48, 86.57, and 84.29% for the HyMap, the APEX, and the CASI data-sets, respectively. Moreover, this method showed better efficiency in comparison to the spectral-based classifications because the OAs of the proposed method was 5.67 and 3.75% higher than the conventional RFC and SVM classifiers, respectively.     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

遥感图像土地覆盖分类中多源特征数据选择研究

多源特征数据可以提高遥感图像的分类精度,选择合适的特征数据十分重要。利用基尼指数对多尺度纹理信息、主成分变换前三分量、地形数据等特征进行选择,选出最佳特征子集。利用支持向量机、神经网络分类法、最大似然法分别对全部特征数据和最佳特征子集结合多光谱数据进行分类。实验结果表明:基尼指数可以有效地对多源特征数据进行选择,特征选择可以提高分类器效率,提高分类精度。     

Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods

Image classification from remote sensing is becoming increasingly urgent for monitoring environmental changes. Exploring effective algorithms to increase classification accuracy is critical. This paper explores the use of multispectral HJ1B and ALOS (Advanced Land Observing Satellite) PALSAR L-band (Phased Array type L-band Synthetic Aperture Radar) for land cover classification using learning-based algorithms. Pixel-based and object-based image analysis approaches for classifying HJ1B data and the HJ1B and ALOS/PALSAR fused-images were compared using two machine learning algorithms, support vector machine (SVM) and random forest (RF), to test which algorithm can achieve the best classification accuracy in arid and semiarid regions. The overall accuracies of the pixel-based (Fused data: 79.0%; HJ1B data: 81.46%) and object-based classifications (Fused data: 80.0%; HJ1B data: 76.9%) were relatively close when using the SVM classifier. The pixel-based classification achieved a high overall accuracy (85.5%) using the RF algorithm for classifying the fused data, whereas the RF classifier using the object-based image analysis produced a lower overall accuracy (70.2%). The study demonstrates that the pixel-based classification utilized fewer variables and performed relatively better than the object-based classification using HJ1B imagery and the fused data. Generally, the integration of the HJ1B and ALOS/PALSAR imagery can improve the overall accuracy of 5.7% using the pixel-based image analysis and RF classifier.     

Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping

With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images.     

Utility of image point cloud data towards generating enhanced multitemporal multisensor land cover maps

Multitemporal land cover classification over urban areas is challenging, especially when using heterogeneous data sources with variable quality attributes. A prominent challenge is that classes with similar spectral signatures (such as trees and grass) tend to be confused with one another. In this paper, we evaluate the efficacy of image point cloud (IPC) data combined with suitable Bayesian analysis based time-series rectification techniques to improve the classification accuracy in a multitemporal context. The proposed method uses hidden Markov models (HMMs) to rectify land covers that are initially classified by a random forest (RF) algorithm. This land cover classification method is tested using time series of remote sensing data from a heterogeneous and rapidly changing urban landscape (Kuopio city, Finland) observed from 2006 to 2014. The data consisted of aerial images (5 years), Landsat data (all 9 years) and airborne laser scanning data (1 year). The results of the study demonstrate that the addition of three-dimensional image point cloud data derived from aerial stereo images as predictor variables improved overall classification accuracy, around three percentage points. Additionally, HMM-based post processing reduces significantly the number of spurious year-to-year changes. Using a set of 240 validation points, we estimated that this step improved overall classification accuracy by around 3.0 percentage points, and up to 6 to 10 percentage points for some classes. The overall accuracy of the final product was 91% (kappa = 0.88). Our analysis shows that around 1.9% of the area around Kuopio city, representing a total area of approximately 0.61 km², experienced changes in land cover over the nine years considered.     

Classifiers vs. input variables—The drivers in image classification for land cover mapping

The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification.     

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.     

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.     

多源数据的土地覆被样本自动提取

随着遥感数据获取能力的不断增强,自动化程度已经成为大尺度遥感土地覆被分类面临的关键问题。然而,现有训练样本的人工选取方法成为制约土地覆被分类自动化的瓶颈。本文以河南、贵州两省为研究区,提出一种基于多源数据的土地覆被样本自动提取方法,以构建适用于大尺度的土地覆被自动分类。首先,以2010年1∶10万土地利用数据CHINALC和30 m分辨率全球土地覆被数据Globle Land30为样本数据源;然后,利用空间一致性分析及异质性分析确定样本初选区域;最后,通过样本提纯去除无效样本。结果表明:(1)应用多源数据的土地覆被样本自动提取方法获得的分类产品总体分类精度高于人工样本提取方法制作的全球土地覆被产品MCD12Q1。(2)与单源样本自动提取方法相比,应用多源数据的土地覆被样本自动提取方法,可获得更好的分类稳定性。综上,多源数据的土地覆被样本自动提取方法可在保证精度的同时,提升土地覆被分类的自动化程度。