Image segmentation for classification of vegetation using NOAA-AVHRR data

A classification procedure for National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) data based on image segmentation following supervised classification by regions is presented. The procedure was appliedto channel 1 (0.58-0.68 mu m) and channel 2 (0.72-1.10 mu m) AVHRR mosaics composed of images acquired between 13 and 26 June 1993 for the state of Mato Grosso, Brazil. Eight vegetation classes were identified using this procedure. The result was compared with an existing vegetation map of Mato Grosso state for reference. The quantitative evaluation yielded a kappa coefficient of 0.4. The result indicate that image segmentation and supervised classification by regions is a procedure that is useful for mapping vegetation classes on a regional scale.     

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

We have analysed monthly composites of normalized difference vegetation index (NDVI) calculated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) for the Amazonian region of northern Brazil across a decade (August 1981 to June 1991) to ascertain if the dominant vegetation types could be differentiated,and to seek inter-annual climatic variation due to changing environmental conditions. The vegetation types observed included dense forest ( submontana and terras baixas ), open forest ( submontana and terras baixas ), transitional forest, seasonal forest ( caatinga ), and two types of savanna ( cerrado ). We found that monthly NDVI composites revealed seasonality in cerrado and especially in caatinga cover types, which can be used in their identification, whilst the phenology of other forest cover types varies little throughout the year. Additionally, yearly composite NDVI values showed a clear and significant reduction ( p 0.95) in dry years, such as those with El Nino Southern Oscillation events. These results indicate the potential use of multi-temporal NDVI data for the environmental characterization and identification of forest ecosystems. Our research found NDVI images from NOAA AVHRR offer a long-term data set that is unequalled for monitoring terrestrial land cover. However, these data have to be used with a degree of caution, especially in regards to atmospheric interference, such as cloud contamination and volcanic eruptions, and post-launch changes in calibration.     

Synergism between NOAA-AVHRR and Meteosat data for studying vegetation development in semi-arid West Africa

Abstract

Rainfall estimates, based on cold cloud duration estimated from Meteosat data, are compared with vegetation development depicted by data of the normalized difference vegetation index (NDVI) from the National Oceanic and Atmospheric Administration's (NOAA) advanced very high resolution radiometer (AVHRR) for part of the Sahel. Decadal data from the 1985 and 1986 growing seasons are examined to determine the synergism of the datasets for rangeland monitoring. There is a general correspondence between the two datasets with a marked lag between rainfall and NDVI of between 10 and 20 days. This time lag is particularly noticeable at the beginning of the rainy season and in the more northern areas where rainfall is the limiting factor for growth. Principal component analysis was used to examine deviations from the general relationship between rainfall and the NDVI. Areas of low NDVI values for a given input of rainfall were identified: at a regional scale, they give an indication of areas of low production potential and possible degradation of ecosystems. This study demonstrates in a preliminary way the synergism of such datasets derived from satellite--borne sensors with coarse spatial resolution, which may provide new information for the improved management of the Sahelian grasslands.     

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.     

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the west African Sahel

Abstract

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.     

Fusion of multisensor multitemporal satellite data for land cover mapping

Efficient integration of remote sensing information with different temporal, spectral and spatial resolutions is important for accurate land cover mapping. A new temporal fusion classification (TFC) model is presented for land cover classification, based on statistical fusion of multitemporal satellite images. In the proposed model, the temporal dependence of multitemporal images is taken into account by estimating transition probabilities from the change pattern of a vegetation dynamics indicator (VDI). Extension of this model is applicable to Synthetic Aperture Radar (SAR) images and integration of multisensor multitemporal satellite images, concerning both temporal attributes and reliability of multiple data sources. The feasibility of the new method is verified using multitemporal Landsat Thematic Mapper (TM) and ERS SAR satellite images, and experimental results show improved performance over conventional methods.     

Multiscale comparison of remote-sensing data for linear woody vegetation mapping

Wooded hedgerows do not cover large areas but perform many functions that are beneficial to water quality and biodiversity. A broad range of remotely sensed data is available to map these small linear elements in rural landscapes, but only a few of them have been evaluated for this purpose. In this study, we evaluate and compare various optical remote-sensing data including high and very high spatial resolution, active and passive, and airborne and satellite data to produce quantitative information on the hedgerow network structure and to analyse qualitative information from the maps produced in order to estimate the true value of these maps. We used an object-based image analysis that proved to be efficient for detecting and mapping thin elements in complex landscapes. The analysis was performed at two scales, the hedgerow network scale and the tree canopy scale, on a study site that shows a strong landscape gradient of wooded hedgerow density. The results (1) highlight the key role of spectral resolution on the detection and mapping of wooded elements with remotely sensed data; (2) underline the fact that every satellite image provides relevant information on wooded network structures, even in closed landscape units, whatever the spatial resolution; and (3) indicate that light detection and ranging data offer important insights into future strategies for monitoring hedgerows.     

Investigation of digital landsat data for mapping soils under range vegetation

Computer-pattern-recognition techniques were used to investigate relationships of Landsat multispectral data to soil patterns under range vegetation. A semiarid region in Park County, Colorado, was selected for study. Landsat imagery was analyzed and class statistics developed by three methods: a conventional supervised method, a “cleaned” supervised method, and an unsupervised (clustering) method. Landsat classification maps displaying eleven different soil units were produced by each of the three analysis methods. The Landsat classification maps produced by the unsupervised method had very low agreement with ground information. The “cleaned” supervised method produced maps that agreed with soil survey information developed by conventional means 47% of the time, while those maps produced using the conventional supervised method agreed with ground information only 33% of the time. Boundaries on Landsat computer classification maps produced by the “cleaned” supervised method compared favorably with soil boundaries on soil maps produced by conventional soil survey techniques.     

Reducing the effects of vegetation cover on airborne radiometric data using Landsat TM data

The gamma-ray spectrometry responses from bedrock in Canadian Shield areas are substantially masked by overburden and vegetation. Proper interpretation of airborne gamma-ray spectrometry data is dependent on accounting for the interference provided by surface cover. In this paper, a method is tested to correct airborne gamma-ray spectrometry, acquired over the Canadian Shield of northeastern Alberta, for vegetation screening by estimating the proportions of bedrock and vegetation cover from Landsat TM data. TM pixel values, due to the patchy network of bedrock and vegetation, result from a spectral mixture of these ground cover classes. Linear unmixing was implemented to deconvolve TM bands in abundance images to estimate proportions of bedrock and vegetation for each pixel. The outcrop abundance image, representing spatial variation in area percentage of bedrock, is used in linear regression analysis to calibrate co-registered K, eTh, and eU gamma-ray spectrometry channels to 40 per cent bedrock endmember images.     

Land cover mapping for tropical forest rehabilitation planning using remotely-sensed data

Lack of reliable and up-to-date maps relating to land cover (among other themes) constitute a weakness in land resource surveys and cause costly failures to many forest rehabilitation projects in the tropics. This study evaluated the utility of satellite imagery for land cover mapping for forest rehabilitation planning in a case study in Mindoro, Philippines. Using Landsat TM data, visual and digital image processing techniques were performed using the GRID module of ARC/INFO and the microBRIAN image processing software. Crown cover density is found as the most useful and the most important detail of information the image could provide. Detailed mapping at the species and forest type levels is unreliable, as is the delineation of water bodies and some cultural features in rugged terrain. Clustering of the NDVI image is found more applicable in producing land cover maps depicting crown cover classes than classifying raw TM-3, -4, and-5.     

Sampling design and uncertainty based on spatial variability of spectral variables for mapping vegetation cover

Optimal sampling design for collecting ground data is critical in order to accurately map vegetation cover using remotely sensed data. Traditional simple random sampling often leads to a duplication of information and to a larger sample than is required. An optimal sampling grid spacing based on regionalized variable theory can greatly reduce the number of sample plots needed given a precision level for a study area. However, this method requires a set of ground data that exists or can be obtained via a pilot survey in order to derive a semivariogram for measuring the spatial variability of the variable of interest. In this study, we first developed a method to estimate the semivariogram of a ground or primary variable—vegetation cover from remotely sensed data instead of ground data—and then used it for determining optimal grid spacing for sampling the primary variable. The method developed can avoid the need for a pilot survey to obtain a ground dataset that has a good spatial distribution of plots and can be used to calculate the unbiased semivariogram of the ground variable when unbiased historical data are not available. This can reduce the total cost of collection of ground data. The accuracy of mapping vegetation cover based on this approach was compared to that generated with simple random sampling. A simple sensitivity analysis was conducted. The results show that this new method is very promising for determining optimal sampling grid spacing for estimating regional averages. When it is applied to determining sampling grid spacing for local estimation, a high correlation between vegetation cover and spectral variables is required.     

Thermal inertia mapping over the Brahmaputra basin,India using NOAA-AVHRR data and its possible geological applications

Thermal inertia is a volume property and shows the resistance power of the material against changes in its temperature. The thermal inertia of a surficial feature of interest cannot be directly measured. Hence, a proper modelling is required for its estimation. The objective of the project is to develop a technique to generate thermal inertia images using available National Oceanic and Atmospheric Administration (NOAA) satellite data to detect thermal anomalies and oilfield signature over a known producing basin. The Brahmaputra valley in Upper Assam is selected for this study.

NOAA-Advanced Very High Resolution Radiometer (AVHRR) thermal data were converted to temperature, based on the look-up table (LUT) given in the NOAA-AVHRR CD and by using split-window atmospheric attenuation correction models. The thermal inertia imagery is constructed with the help of the albedo imagery generated from the daytime and with the knowledge of the surface temperature change between the daytime and night-time data. The thermal inertia values are computed for all pixels common to both daytime and night-time and the thermal inertia imagery generated for the study area. The thermal inertia of a surface cannot be measured directly; so another model is also used to estimate apparent thermal inertia (ATI). The images from both the models have shown similar results.

The geological map when draped over the ATI image shows good correlation of gross lithology and thermal inertia. The metamorphics/basement and the sediments are well differentiated by their tonal and textural characters. The Mikir massif shows conspicuously brighter signature than the featureless darker signatures of the surrounding valley. Within the valley, the river water exhibits bright tone, whereas the present-day sandbars within the river exhibit darker tone than the alluvial plains of the valley. This is in agreement with the available published data. Major thrusts can be mapped as bright linear tone, and their geometry coincides well with those mapped in the field. Exposed cross faults can also be mapped in Arunachal foothills and faults in Mikir massif. The isoneotectonic map when draped over the ATI image shows that the identified isoneotectonic units can be well differentiated in the image on the basis of tonal characters. The prominent lineaments mapped in Mikir massif can be traced in the valley part also.

The producing and dry structures in the valley show very few signatures on the thermal inertia images, possibly due to poor spectral and spatial resolution of the NOAA data. It is planned to use the developed technique to generate thermal inertia maps using higher spatial and spectral resolution satellite data (e.g. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)), which may provide better oilfield signatures.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

Eco-climatic classification of Tuscany through NOAA-AVHRR data

Eco-climatic classifications have proved to be of great utility for the planning and management of various agricultural and forestry activities. Since the usual methods are often expensive and not easy to apply, remote sensing data processing has recently been proposed to identify ecologically homogeneous land units. NOAA AVHRR data in particular have been demonstrated to be suitable for this purpose thanks to their spatial resolution and frequent acquisition. While most works in this field have dealt only with NDVI imagery, it has been suggested that thermal and ancillary data could also bring significant information. These considerations have been kept in mind during the present study, which concerns the eco-climatic classification of a complex Italian Region by the use of NOAA NDVI and thermal images from two years in addition to ancillary data. A complete methodology was developed for the processing of this integrated data set with particular reference to the identification of the suitable numbers of main and sub-classes. The final output was compared to existing climatic and land use information about the region. The substantial agreement between these sources testifies to the eco-climatic value of the product obtained.     

Estimation of the vapour pressure deficit using NOAA-AVHRR data

In this study, the calculation of vapour pressure deficit (VPD) using the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite data set is shown. Twenty-four NOAA/AVHRR data images were arranged and turned to account for both VPD and land surface temperature (LST), which was necessary to calculate the VPD. The most accurate LST values were obtained from the Ulivieri et al. split-window algorithm with a root mean square error (RMSE) of 2.7 K, whereas the VPD values were retrieved with an RMSE of 6 mb. Furthermore, the VPD value was calculated on an average monthly basis and its correlation coefficient was found to be 0.991, while the RMSE value was calculated to be 2.67 mb. As a result, VPD can be used in studies that examine plants (germination, growth, and harvest), controlling illness outbreak, drought determination, and evapotranspiration.     

Winter wheat mapping using temporal signatures of MODIS vegetation index data

Because most land-cover types have distinct seasonal changes and corresponding reflectance characteristics in remotely sensed images, the signatures in time-series data are useful for discriminating different land covers. Although temporal signatures have been used to classify different land-cover types, they have not been fully exploited to classify specific crops, and the influence of low resolution should be evaluated. The aims of this study were to seek an effective method to classify specific crops using the temporal signatures in coarse time-series data and to examine the applicability of the data for crop classification as well. A winter wheat-producing region in China was selected for this case study. Moderate-Resolution Imaging Spectroradiometer (MODIS) 8-day composite land surface reflectance product (MOD09Q1) data with a 250 m spatial resolution were used to calculate the vegetation index data, which was applied to detect the properties of live green plants. The noise in the time series was filtered to minimize the classification uncertainties. The curve shape in the time-series vegetation index profile was used as the major metric to classify winter wheat, and other phenological metrics extracted from the data were used conjunctly as auxiliary functions to improve the separability. The metrics for winter wheat classification were quantified in the large fields with relatively pure pixels. Winter wheat was successfully extracted from the MODIS vegetation index data, and the MODIS-derived result was validated with a fine-resolution (19.5 m) thematic map derived from images collected by the charge-coupled device sensor on board the China–Brazil Earth Resources Satellite (CBERS). It showed that the MODIS-derived result had inevitable low-resolution bias, and the errors of commission and omission were 32.3 and 33.8%, respectively. The overall classification effect of the MODIS-derived result relied upon the distribution of pixel purity in the study area.     

Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America

While mapping vegetation and land cover using remotely sensed data has a rich history of application at local scales, it is only recently that the capability has evolved to allow the application of classification models at regional, continental and global scales. The development of a comprehensive training, testing and validation site network for the globe to support supervised and unsupervised classification models is fraught with problems imposed by scale, bioclimatic representativeness of the sites, availability of ancillary map and high spatial resolution remote sensing data, landscape heterogeneity, and vegetation variability. The System for Terrestrial Ecosystem Parameterization (STEP) - a model for characterizing site biophysical, vegetation and landscape parameters to be used for algorithm training and testing and validation - has been developed to support supervised land cover mapping. This system was applied in Central America using two classification systems based on 428 sites. The results indicate that: (1) it is possible to generate site data efficiently at the regional scale; (2) implementation of a supervised model using artificial neural network and decision tree classification algorithms is feasible at the regional level with classification accuracies of 75-88%; and (3) the STEP site parameter model is effective for generating multiple classification systems and thus supporting the development of global surface biophysical parameters.     

Deriving terrain and textural information from stereo RADARSAT data for mountainous land cover mapping

A new procedure is proposed for land cover classification in a mountainous area using stereo RADARSAT-1 data. The method integrates a few types of information that can be extracted from the same stereo RADARSAT images: (1) the Digital Elevation Model (DEM) generated from the stereo RADARSAT images; (2) terrain information (elevation, slope and aspect) extracted from the derived DEM; and (3) textural information derived from the same RADARSAT images. An Artificial Neural Network (ANN) classifier is applied for the land cover classification. Performance of the proposed method is evaluated using a mountainous study area in Southern Argentina, where there is a lack of up-to-date information for environmental monitoring. The results show that the integration of textural and terrain information can greatly improve the accuracy of the classification using the ANN classifier. It demonstrates that stereo RADARSAT images provide valuable data sources for land cover mapping, especially in mountainous areas where cloud cover is a problem for optical data collection and topographical data are not always available.     

Land cover mapping and monitoring from NOAA AVHRR data in Bangladesh

NOAA AVHRR HRPT data consisting of two time frames i.e., 1985–86 and 1992–93 were analysed to determine the status of major land cover types of Bangladesh and to monitor change. The data were radiometrically corrected to spectral reflectance and mapped to a consistent Plate Caree projection followed by cloud masking and country masking. The satellite data and the methodology adopted was found to be useful for assessment and monitoring of major land cover types and their dynamics at small scale. The nature and pattern of land cover change derived from the analysis forms a valuable resource for planners and decision-makers in formulating policies, allocating scarce resources and in evaluation of the practical effects of land use policies.     

Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia

Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates. However, irregular image acquisition within parts of the boreal biome coupled with an absence of atmospherically corrected data hamper regional-scale monitoring efforts using Landsat imagery. A method of boreal forest cover and change mapping using Landsat imagery has been developed and tested within European Russia between circa year 2000 and 2005. The approach employs a multi-year compositing methodology adapted for incomplete annual data availability, within-region variation in growing season length and frequent cloud cover. Relative radiometric normalization and cloud/shadow data screening algorithms were employed to create seamless image composites with remaining cloud/shadow contamination of less than 0.5% of the total composite area. Supervised classification tree algorithms were applied to the time-sequential image composites to characterize forest cover and gross forest loss over the study period. Forest cover results when compared to independently-derived samples of Landsat data have high agreement (overall accuracy of 89%, Kappa of 0.78), and conform with official forest cover statistics of the Russian government. Gross forest cover loss regional-scale mapping results are comparable with individual Landsat image pair change detection (overall accuracy of 98%, Kappa of 0.71). The gross forest cover loss within European Russia 2000-2005 is estimated to be 2210 thousand hectares, and constitutes a 1.5% reduction of year 2000 forest cover. At the regional scale, the highest proportional forest cover loss is estimated for the most populated regions (Leningradskaya and Moskovskaya Oblast). Our results highlight the forest cover depletion around large industrial cities as the hotspot of forest cover change in European Russia.