Combination of the Normalized Difference Vegetation Index and surface temperature for regional scale European Forest cover mapping using AVHRR data

Forest and non-forest samples selected from an existing European forest map were classified using 8 months of cloud-screened European AVHRR data divided into 82 ecological/climatic strata. Consistently higher mean monthly forest/non-forest classification accuracies were found when the samples were classified using Normalized Difference Vegetation Index (NDVI) and surface temperature ( T ) data rather than using NDVI or T data alone.     

A new water-resistant snow index for the detection and mapping of snow cover on a global scale

An up-to-date spatio-temporal change analysis of global snow cover is essential for better understanding of climate–hydrological interactions. The normalized difference snow index (NDSI) is a widely used algorithm for the detection and estimation of snow cover. However, NDSI cannot discriminate between snow cover and water bodies without use of an external water mask. A stand-alone methodology for robust detection and mapping of global snow cover is presented by avoiding external dependency on the water mask. A new spectral index called water-resistant snow index (WSI) with the capability of exhibiting significant contrast between snow cover and other cover types, including water bodies, was developed. WSI uses the normalized difference between the value and hue obtained by transforming red, green, and blue, (RGB) colour composite images comprising red, green, and near-infrared bands into a hue, saturation, and value (HSV) colour model. The superiority of WSI over NDSI is confirmed by case studies conducted in major snow regions globally. Snow cover was mapped by considering monthly variation in snow cover and availability of satellite data at the global scale. A snow cover map for the year 2013 was produced at the global scale by applying the random walker algorithm in the WSI image supported by the reference data collected from permanent snow-covered and non-snow-covered areas. The resultant snow-cover map was compared to snow cover estimated by existing maps: MODIS Land Cover Type Product (MCD12Q1 v5.1, 2012), Global Land Cover by National Mapping Organizations (GLCNMO v2.0, 2008), and European Space Agency’s GlobCover 2009. A significant variation in snow cover as estimated by different maps was noted, and was was attributed to methodological differences rather than annual variation in snow cover. The resultant map was also validated with reference data, with 89.46% overall accuracy obtained. The WSI proposed in the research is expected to be suitable for seasonal and annual change analysis of global snow cover.     

High-resolution multispectral techniques for mapping former Little Ice Age terrestrial ice cover in the Canadian High Arctic

Multispectral classification approaches were applied to high-resolution ASTER (15 m) and ETM+ (30 m) imagery for the purpose of developing new techniques for mapping recently deglaciated LIA perennial ice cover in the Canadian Arctic. Four areas in the Queen Elizabeth Islands, with dissimilar surficial geology and diverse topographic complexity, were selected to test the efficacy of both sensors for mapping these subtle landscape features. Automated classification (band calculation) methods were found to be most effective on quartzitic sandstone and siliceous crystalline bedrock, whereas, semi-automated (supervised classification) techniques were most successful on substrates comprised primarily of carbonate lithologies. ASTER's superior spatial resolution yielded higher accuracies in topographically complex areas; however, ETM+ was more effective over a wider variety of substrate lithologies and topographic settings, with a mean overall accuracy of 91% (mean κ statistic = 0.71), compared to 87% (mean κ statistic = 0.60) for ASTER.     

A hybrid of deep learning and hand-crafted features based approach for snow cover mapping

Monitoring the extent of snow cover plays a vital role for a better understanding of current and future climatic, ecological, and water cycle conditions. Previously, several traditional machine learning models have been applied for accomplishing this while exploring a variety of feature extraction techniques on various information sources. However, the laborious process of any amount of hand-crafted feature extraction has not helped to obtain high accuracies. Recently, deep learning models have shown that feature extraction can be made automatic and that they can achieve the required high accuracies but at the cost of requiring a large amount of labelled data. Fortunately, despite the absence of such large amounts of labelled data for this task, we can rely on pre-trained models, which accept red-green-blue (RGB) information (or dimensions-reduced spectral data). However, it is always better to include a variety of information sources to solve any problem, especially with the availability of other important information sources like synthetic aperture radar (SAR) imagery and elevation. We propose a hybrid model where the deep learning is assisted by these information sources which have until now been left out. Particularly, our model learns from both the deep learning features (derived from spectral data) and the hand-crafted features (derived from SAR and elevation). Such an approach shows interesting performance-improvement from 96.02% (through deep learning alone) to 98.10% when experiments were conducted for Khiroi village of the Himalayan region in India.     

Accuracy assessment of MODIS,NOAA and IRS data in snow cover mapping under Himalayan conditions

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.     

Land cover characterization and mapping of continental Southeast Asia using multi-resolution satellite sensor data

Land use/land cover change, particularly that of tropical deforestation and forest degradation, has been occurring at an unprecedented rate and scale in Southeast Asia. The rapid rate of economic development, demographics and poverty are believed to be the underlying forces responsible for the change. Accurate and up-to-date information to support the above statement is, however, not available. The available data, if any, are outdated and are not comparable for various technical reasons. Time series analysis of land cover change and the identification of the driving forces responsible for these changes are needed for the sustainable management of natural resources and also for projecting future land cover trajectories. We analysed the multi-temporal and multi-seasonal NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data of 1985/86 and 1992 to (1) prepare historical land cover maps and (2) to identify areas undergoing major land cover transformations (called ‘hot spots’). The identified ‘hot spot’ areas were investigated in detail using high-resolution satellite sensor data such as Landsat and SPOT supplemented by intensive field surveys. Shifting cultivation, intensification of agricultural activities and change of cropping patterns, and conversion of forest to agricultural land were found to be the principal reasons for land use/land cover change in the Oudomxay province of Lao PDR, the Mekong Delta of Vietnam and the Loei province of Thailand, respectively. Moreover, typical land use/land cover change patterns of the ‘hot spot’ areas were also examined. In addition, we developed an operational methodology for land use/land cover change analysis at the national level with the help of national remote sensing institutions.     

NOAA-AVHRR data processing for the mapping of vegetation cover

The NOAA-AVHRR images have been widely used for global studies due to their low cost, suitable wavebands and high temporal resolution. Data from the AVHRR sensor (Bands 1 and 2) transformed to the Normalized Difference Vegetation Index (NDVI) are the most common product used in global land cover studies. The purpose of this Letter is to present the vegetation, soil, and shade fraction images derived from AVHRR, in addition to NDVI, to monitor land cover. Six AVHRR images from the period of 21 to 26 June 1993 were composed and used to obtain the above mentioned products over Sa o Paulo State, in the south-east of Brazil. Vegetation fraction component values were strongly correlated with NDVI values ( r 0.95; n 60). Also, the fraction image presented a good agreement with the available global vegetation map of Sao Paulo State derived from Landsat TM images.     

Comparison of methods of snow cover mapping by analysing the solar spectrum of satellite remote sensing data in China

Three methods, supervised classification (SC), digital number (DN) statistics and Normalized Difference Snow Index (NDSI), are used to map snow cover and then calculate snow cover area. Data sets from Landsat TM, Moderate Resolution Imaging Spectroradiometer (MODIS) and NOAA/AVHRR are selected because these sensors of different spatial resolution provide the most up to date remote sensing data for China. The results show that the best method for obtaining the snow index is different for each of these sensor products because of their different spatial and temporal resolutions and objectives of application. Reflectivity threshold statistics (RTs) should be used if the data series is incomplete; whereas SC needs a relatively accurate signature file for classification. A valid and rational method has been certified which selects NDSI for extracting snow pixels. Meanwhile, we introduce the brightness compensation method for decreasing the impact of topographic shading on distinguishing of snow pixels.     

A statistical analysis of the relationship between climatic factors and the Normalized Difference Vegetation Index in China

Climate change has a large impact on vegetation dynamics. A series of statistical analyses were employed to demonstrate the relationship between Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) data with an 8?×?8 km resolution and meteorological data, during the period 1982–2005. Rainfall has a great impact on vegetation with varying time lags. The sensitivity of NDVI to the threshold of accumulated temperature varies regionally. To identify a ‘best factor’ for each meteorological station simple and partial correlation analyses were carried out. Multiple correlation analysis was used to validate the association between the two climatic factors and monthly maximum NDVI (MNDVI). This study led to the conclusion that good correlations between MNDVI and two climatic factors are prevalent in China. It also indicated that the ‘best factors’ for some regions identified by partial correlation analysis are better than those selected by simple correlation analysis. The partial correlation coefficients of MNDVI and each climate factor were calculated to describe the singular influence of each meteorological variable. The results indicated that the impact of other variables on vegetation should be considered in the ‘best factor’ selection for one climatic variable. Temperature has a significant positive influence on vegetation growth in China. Precipitation is the most important climatic factor that closely correlates with MNDVI, particularly in arid and semi-arid environments. However, in some wet regions, precipitation is not a limiting factor on vegetation growth. A trend analysis was carried out to study climate change and its impacts on vegetation. The annual accumulated temperature had an increasing trend in China during 1982–2005. Temperature increases had different influences on vegetation dynamics in different parts of China. The results coincided with those of the multiple and partial correlation analysis.     

Spatial variability of images and the monitoring of changes in the Normalized Difference Vegetation Index

Pairs of Landsat Multispectral Scanner System images from seven contrasting areas were analysed using scale variance analysis to determine the spatial frequencies present. Specifically images of the Normalized Difference Vegetation Index (NDVI) were analysed, which is sensitive to vegetation activity. Analyses were performed on images for each of the two dates and change images derived by subtracting the NDVI values of the first images from those of the second date. As expected the change images were characterized by higher spatial frequencies than the images of individual dates, but this was only marked for four of the seven areas. Contrary to initial expectations, knowledge of the spatial frequency content of the images from the two dates could not be used to infer the spatial frequencies present in the change images and hence the spatial resolutions needed for detecting change in the NDVI.     

基于FY-3A/VIRR数据的雪盖信息提取软件设计与实现

目前基于FY-3A/VIRR资料的处理研究较少且VIRR数据量庞大,一些商用遥感图像处理软件很难直接完成对图像的预处理工作,这样对后续的定量化反演以及对FY-3A/VIRR的推广使用带来了困难。为了解决业务化问题,运用改进的归一化积雪指数(NDSI)、综合阈值判别算法和IDL、VB混合编程技术相结合的方法设计了积雪信息批量提取软件,实现了针对FY-3A/VIRR数据的单幅图像或多幅图像的积雪信息提取以及精度验证。实验表明,该软件处理速度快、实时性好、可批量提取积雪信息,大大节省了人力资源,同时提高了VIRR数据的分发和共享能力,可以在今后的工业生产和自动化领域推广使用。     

Assessing the information content of multiangle satellite data for mapping biomes: II. Theory

The insights gained from present land cover classification activities suggest integration of multiangle data into classification attempts for future progress. Land cover types that exhibit distinct signatures in the space of remote sensing data facilitate unambiguous identification of cover types. In this two-part series, we develop a theme for consistency among cover type definitions, uniqueness of their signatures, and physics of the remote sensing data. In the first part, Zhang et al.'s [Remote Sens. Environ., in press.] empirical arguments in support of the consistency principle were presented. This part provides a theoretical justification of the consistency requirements. Radiative transfer best explains the physics of the processes operative in the generation of the signal in the optical remote sensing data. Biome definitions given in terms of variables that this theory admits and the use of the transport equation to interpret biome signatures guarantee the consistency requirements. It is shown in this paper that three metrics of the biome angular signature in the spectral space—location, angular signature slope (ASSI), and length (ASLI) indices—are related to eigenvalues and eigenvectors of the transport equation. These variables allow a novel parameterization of canopy structure based on the partitioning of the incident radiation among canopy absorption, transmission, and reflection. Consistency between cover type definitions and uniqueness of their signatures with the physics of the remote sensing data is required not only to reduce ambiguity in land cover identification, but also to directly relate land cover type to biophysical and biogeochemical processes in vegetation canopies.     

An algorithm for snow and ice detection using AVHRR data An extension to the APOLLO software package

Abstract

The AVHRR (Advanced Very High Resolution Radiometer) Processing scheme Over Land, cLbud and Ocean (APOLLO) is used to extract surface and cloud parameters from satellite data. Before these parameters can be computed, it is necessary to distinguish between land and ocean surfaces and to apply algorithms for the detection of partially cloudy and cloud-filled pixels. In regions with seasonal or permanent snow and ice coverage the separation of clouds becomes much more difficult or often impossible. For this reason, and to find cloud-free and partly cloudy snow and ice pixels,- a day-time algorithm has been developed which uses all five AVHRR channels as follows: The threshold testing of the reflected part of channel-3 radiance leads to a definite distinction between snow/ice and water clouds due, to the clouds much higher reflectivity at 3.7 μm. The detection; of sea ice is based on threshold tests of visible reflectances and, in particular, of the temperature difference between channels-4 and -5. Snow is identified if a high visible reflectance is combined with a low reflectance in channel-3 and with a ratio of channel-2 to channel-1 reflectances similar to that of a cloud. The latter criterion is also mostly suitable to distinguish between snow-covered and snow-free ice areas. Some tests of this algorithm applied to AVHRR data from the 1987 Baltic Sea ice season have shown reasonable classification results with the exception of a few areas with ice clouds or with ice topped water clouds.     

基于POLSAR极化散射特征与光学归一化指数的农村居民点用地提取

准确提取农村居民点用地规模及分布,对合理利用土地资源、改善农村生态环境及促进城市化发展具有重要意义。根据农村居民点用地的POLSAR散射特性及光谱特征,提出一种基于POLSAR极化散射特征与光学归一化差异指数的农村居民点用地提取方法,并结合实验分析了POLSAR极化相关系数在区分农村居民点用地与林地的不适用性。所述方法可有效解决单一数据源在农村居民点用地提取中裸地(光学数据)、林地(POLSAR数据)与农村居民点用地混分的问题,精确提取农村居民点用地(用户精度为91.7%,制图精度为95.2%,总体精度为95.9%)。相比基于POLSAR极化目标分解的H/α/Wishart迭代分类,该方法用户精度提高了34.9%,制图精度提高了14.4%,总体精度提高了16.2%;相比基于归一化植被指数和归一化建筑指数的监督分类,本文的用户精度提高了24.3%。     

A comparative study of the effects of data reduction on terrain cover mapping from LANDSAT

Abstract

Using LANDSAT-1 data for an area around Esfahan, central Iran, the effect which an hierarchical cascaded clustering algorithm has upon terrain cover classification is examined.     

A cartographic and database approach for land cover/use mapping and generalization from remotely sensed data

Classification of remotely sensed data involves a set of generalization processes, i.e. reality is simplified to a set of a few classes that are relevant to the application under consideration. This article introduces an approach to image classification that uses a class hierarchy structure for mapping unit definition at different generalization levels. This structure is implemented as an operational relational database and allows querying of more detailed land cover/use information from a higher abstraction level, which is that viewed by the map user. Elementary mapping units are defined on the basis of an unsupervised classification process in order to determine the land cover/use classes registered in the remotely sensed data. Mapping unit composition at different generalization levels is defined on the basis of membership values of sampled pixels to land cover/use classes. Unlike fuzzy classifications, membership values are presented to the user at mapping unit level.     

Assessing the information content of multiangle satellite data for mapping biomes: I. Statistical analysis

The insights gained from present land cover classification activities suggest integration of multiangle data into classification attempts for future progress. Land cover types that exhibit distinct signatures in the space of remote sensing data facilitate unambiguous identification of cover types. In this first part, we develop a theme for consistency between cover type definitions, uniqueness of their signatures, and physics of the remote sensing data. The idea of angular signatures in spectral space is proposed to provide a cogent synthesis of information from spectral and angular domains. Three new metrics, angular signature slope (ASSI), length (ASLI), and intercept indices, are introduced to characterize biome signatures. The statistical analyses with these indices confirm the idea that incorporation of the directional variable should improve biome classification result. The consistency principle is tested with the Multiangle Imaging SpectroRadiometer (MISR) leaf area index (LAI) algorithm by examining retrievals when both unique and nonunique signatures are input together with a land cover map. It is shown that this requirement guarantees valid retrievals. Part II provides a theoretical basis for these concepts [Zhang et al., Remote Sens. Environ., in press.].     

Potensoft: MATLAB-based software for potential field data processing, modeling and mapping

An open-source software including an easy-to-use graphical user interface (GUI) has been developed for processing, modeling and mapping of gravity and magnetic data. The program, called Potensoft, is a set of functions written in MATLAB. The most common application of Potensoft is spatial and frequency domain filtering of gravity and magnetic data. The GUI helps the user easily change all the required parameters. One of the major advantages of the program is to display the input and processed maps in a preview window, thereby allowing the user to track the results during the ongoing process. Source codes can be modified depending on the users' goals. This paper discusses the main features of the program and its capabilities are demonstrated by means of illustrative examples. The main objective is to introduce and ensure usage of the developed package for academic, teaching and professional purposes.     

A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series

There is a significant need to provide nationwide consistent information for land managers and scientists to assist with property planning, vegetation monitoring applications, risk assessment, and conservation activities at an appropriate spatial scale. We created maps of woody vegetation cover of Australia using a consistent method applied across the continent, and made them accessible. We classified pixels as woody or not woody, quantified their foliage projective cover, and classed them as forest or other wooded lands based on their cover density. The maps provide, for the first time, cover density estimates of Australian forests and other wooded lands with the spatial detail required for local-scale studies. The maps were created by linking field data, collected by a network of collaborators across the continent, to a time series of Landsat-5 TM and Landsat-7 ETM+ images for the period 2000–2010. The fractions of green vegetation cover, non-green vegetation cover, and bare ground were calculated for each pixel using a previously developed spectral unmixing approach. Time series statistics, for the green vegetation cover, were used to classify each pixel as either woody or not using a random forest classifier. An estimate of woody foliage projective cover was made by calibration with field measurements, and woody pixels classified as forest where the foliage cover was at least 0.1. Validation of the foliage projective cover with field measurements gave a coefficient of determination, R²,of 0.918 and root mean square error of 0.070. The user’s and producer’s accuracies for areas mapped as forest were high at 92.2% and 95.9%, respectively. The user’s and producers’s accuracies were lower for other wooded lands at 75.7% and 61.3%, respectively. Further research into methods to better separate areas with sparse woody vegetation from those without woody vegetation is needed. The maps provide information that will assist in gaining a better understanding of our natural environment. Applications range from the continental-scale activity of estimating national carbon stocks, to the local scale activities of assessing habitat suitability and property planning.     

Assessing the feasibility of a global model for multi-temporal burned area mapping using SPOT-VEGETATION data

The possibility of using the Syst@me Probatoire de l'Observation de la Terre (SPOT)-VEGETATION (VGT) data for global burned area mapping with a single algorithm was investigated. Using VGT images from south-eastern Africa, the Iberian Peninsula and south-eastern Siberia/north-eastern China, we analysed the variability of the spectral signature of burned areas and its relationship with land cover, and performed the selection of the best variables for burned area mapping. The results show that in grasslands and croplands, near-infrared (NIR) and short-wave infrared (SWIR) reflectance always decreases as a result of fire. In forests and woodlands, there may occur a simultaneous decrease of SWIR and NIR or an increase of SWIR and a decrease of NIR. Burning of green vegetation (high values of the Normalized Difference Vegetation Index (NDVI)) tends to result in an increase of the SWIR. The best variables for burned area mapping are different in each region. Only the NIR allows a good discrimination of burned areas in all study areas. We derived a logistic regression model for multi-temporal burned area mapping in tropical, temperate and boreal regions, which handles the spectral variability of burned areas dependent on the type of vegetation. The results underline the feasibility of a single model for global burned area mapping.