Transformed Normalized Difference Vegetation Index Research Articles

Soil Aggregate Stability Mapping Using Remote Sensing and GIS-Based Machine Learning Technique

Soil aggregate stability (SAS) is a critical parameter of soil quality and its mapping can help determine erosion hotspots. Despite this importance, SAS is less documented in available literature due to limited number of analyzes besides being a time consuming. For this reason, many researchers have turned to alternative methods that often use readily available variables such as soil parameters or remote sensing indices to estimate this variable. In that framework, the aim of the present study focused on the investigation of the feasibile use of adapted Leo Breiman’s random forest algorithm (RF) to mapping different mean weight diameter (MWD) tests as an index of SAS (mechanical breakdown (MWDmb), slow wetting (MWDsw), fast wetting (MWDfw) and the mean of the three tests (MWDmean)). The model was built with 77 samples distributed in the three watersheds of the study area located at Settat Ben-Ahmed, in Morocco and with the use of several environmental variables such as soil parameters (organic matter and clay), remote sensing indices (band 2, band 3, band 4, band 5, normalized difference vegetation index (NDVI) and transformed normalized difference vegetation index (TNDVI)), topography (elevation, slope, curvature plane and the topographic wetness index (TWI)) along with additional categorical variables as geological maps, land use and soil classes. The results showed a good level of accuracy for the training phase (75% of samples) for the different tests (R2 > 0.92, RMSE and MAE < 0.15) and were satisfactory for the testing phase (25% of samples, R2 > 0.65, RMSE and MAE < 0.31). Also, organic matter, topography and geology were the most important parameters in the spatial prediction of SAS. Finally, the maps build during this study could be of great use to identify areas of less stable soils in the perspective for taking the necessary measures to improve their quality.

Estimation of nitrogen status and yield of rice crop using unmanned aerial vehicle equipped with multispectral camera

Nitrogen is one of the essential nutrients required for crop growth, and hence should be applied efficiently for attaining optimum yield. To fulfil nitrogen need, absorbed nitrogen in the plant is required to be estimated. Various methods are available to estimate crop nitrogen such as tissue analysis using the methods of Kjeldahl and Dumas, which are accurate, but time-consuming and destructive. Satellite imagery provides a more extensive field view. However, they are limited to their spatial and temporal resolution. Unmanned aerial vehicle (UAV) is emerging as a promising tool that can provide the status of crop nitrogen rapidly with high spatial and temporal resolution. The objective of the study was to evaluate UAV-based imageries to show nitrogen status and predict rice yield at different growth stages. The experiments were conducted using two rice cultivars, six nitrogen applications, two water management practices, and with three replications. Soil plant analysis development (SPAD) meter readings were collected at various growth stages. First, aerial imageries of experimental site were collected using an octocopter UAV equipped with a multispectral sensor that provides reflectance values in four different bands (red, green, red edge, and near-infrared) along with SPAD values for respective flight. Second, aerial images were processed in pix4D software, to identify the most appropriate vegetation index that shows nitrogen status variation in the field and to predict yield using different vegetation indices. Nine vegetation indices were considered: ratio vegetation index, normalized difference vegetation index, normalized green red difference index, red edge difference vegetation index, green ratio vegetation index, green normalized difference vegetation index (GNDVI), wide dynamic range vegetation index, transformed normalized difference vegetation index (TNDVI), and normalized difference red edge. After that, a linear regression model was developed between the representative index and SPAD values. Finally, linear regression models developed by using VI and SPAD values were evaluated and results revealed that GNDVI-based model simulates SPAD values with R2 of 0.49, 0.49, and 0.74 at panicle, milky, and booting stages, respectively. It is also found that TNDVI-based linear regression model predicts yield with R2 of 0.71 at milky stage.

Above-ground biomass estimation of Eucalyptus plantation using remotely sensed data and field measurements

Biomass has had an essential role in the energy sector of the world due to applications in bioenergy. Stand level biomass is frequently calculated from allometric models with field measurements, which is usually time-consuming and costly. They are limited because of the consideration of spatial pattern analysis of above-ground biomass (AGB) across the landscape. Therefore, the development of reliability and low-cost methods is necessary for AGB estimations in landscape level. This study aims to develop a model for estimating AGB for Eucalyptus plantation located in the Sahacogen Green Co., Ltd., in Lampang province, Thailand using remotely sensed data. The AGB value was coupled which calculated from field measurement (tree height, H and diameter at breast height, DBH) using the allometric equation with various vegetation indices. The 55 sample plots and 5 vegetation indices derived from Thailand Earth Observation System (THEOS) were used to develop a model for estimating AGB of Eucalyptus plantation. After discussing the results of the investigation, the Transformed Normalized Difference Vegetation Index (TNDVI) showed a robust correlation with AGB compared to other indices (r = 0.833). Based on stepwise linear regression between AGB and 5 vegetation indices demonstrated TNDVI was only selected while the other indices were eliminated because their relationship was not significant. The developed model R2 was 0.693, adjusted R2 was 0.684 and SEE was 12.41 Mg ha−1. The relationship between observed AGB and predicted AGB from the THEOS model of Eucalyptus plantation with R2 of 0.742 and RMSE of 9.63 Mg ha−1 indicated that remotely sensed data from THEOS can be useful for AGB estimation with high accuracy.

Spatio-temporal Analysis of Aridity Over Punjab Province, Pakistan using Remote Sensing Techniques

Aridity is a severe threat to the ecological environment and it leads to desertification. Aridity has become a more serious hazard to agricultural countries like Pakistan, followed by socio-economic problems. Pakistan is an agrarian country and Punjab province of Pakistan is known as the basket of grain for its population due to its fertile lands and lush green fields. Less or no rainfall can convert any land or region from humid to semi-arid and semi-arid to arid land. Deficiency of moisture also defines arid conditions of any region. Hence, in case of Punjab, aridity is a severe threat to halt the use of full potential of its agricultural land resources. There is an irresistible need to comprehensively assess aridity in Punjab at different time scales and to formulate necessary arrangements and action plans to face this issue on sound footing. Remote sensing can be used to accurately measure aridity on local, regional and global scales. Multi-temporal images of Moderate-resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MOD11A1 of Punjab province are used for aridity assessment. In this study attempt was made to demarcate arid areas of Punjab in the simplest possible form using different vegetation indices and land surface temperature. Maps are developed by using normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil adjusted vegetation index (SAVI) and land surface temperature (LST). A weighted overlay analysis of these indices was also done for further comprehensive analysis of aridity. The results indicate that aridity is more in southern Punjab due to increased temperature and reduced precipitation and in northern regions of the province, aridity is developing especially in those areas, which were semi humid or semi-arid in the past.

Estimating oak forest parameters in the western mountains of Iran using satellite-based vegetation indices

Remote sensing is an important tool for studying and modeling forest stands. Vegetation indices obtained from Landsat-8 remotely sensed data were used to estimate the forest parameters in the western mountains of Iran. Thirty-four sample points were selected on the map of Bayangan County, Kermanshah province, Iran. At each point, a cluster of five rectangular plots of 8100 m2 and 200 m apart was established. Some clusters were primarily sampled to determine the variance of the forest parameters. The coefficient of variation was used as a criterion for sample allocation. Stand density, canopy cover and basal area per hectare were calculated for each plot. Vegetation indices were extracted from the Landsat-8 images for each plot. Simple linear and nonlinear regressions were conducted to develop the models. The models were validated using an independent data set. Stability of model parameters was statistically evaluated. The results showed that Normalized difference vegetation index (NDVI) and Transformed normalized difference vegetation index (TNDVI) followed by Simple ratio vegetation index (SRVI) were the best predictors, explaining up to 91% of the variations with high precision. For NDVI, Soil adjusted vegetation index 2 (SAVI2) and SRVI, the cubic model was more appropriate than the linear model for predicting the forest parameters. For this model, the R-square value increased while NRMSE decreased significantly. For Infrared percentage vegetation index (IPVI), the quadratic model was better, but, for other vegetation indices, nonlinear models were not superior to linear ones. Finally, it can be concluded that Landsat-8 imagery is suitable for predicting forest parameters in the oak forests of western Iran. Of course, large plots must be selected, and pre-classification is necessary to gain accurate and precise estimations.

LANDSLIDE IDENTIFICATION FROM IRS-P6 LISS-IV TEMPORAL DATA-A COMPARATIVE STUDY USING FUZZY BASED CLASSIFIERS

Abstract. While extracting land cover from remote sensing images, each pixel in the image is allocated to one of the possible class. In reality different land covers within a pixel can be found due to continuum of variation in landscape and intrinsic mixed nature of most classes. Mixed pixels may not be appropriately processed by traditional image classifiers, which assume that pixels are pure. The existence of mixed pixels led to the development of several approaches for soft (often termed fuzzy in the remote sensing literature) classification in which each pixel is allocated to all classes in varying proportions. However, while the proportions of each land cover within each pixel may be predicted, the spatial location of each land cover within each pixel is not. Thus, it is important to develop and implement a classifier that can work as soft classifiers for landslide identification. This work is an attempt to document and identify landslide areas by five spectral indices using temporal multi-spectral images from IRS-P6 LISS-IV images. To improve the spectral properties of spectral indices for specific class identification (in this case landslide) a Class Based Sensor Independent (CBSI) technique proposed. The result indicates that CBSI based Transformed Normalized Difference Vegetation Index (TNDVI) temporal indices data gives better results for landslide identification with minimum entropy and membership range.

Real time estimation of chlorophyll content based on vegetation indices derived from multispectral UAV in the kinnow orchard

Abstract: Nondestructive estimation of the biophysical properties of crops provide quick and real time information of crop health under wide range of environment.  The chlorophyll content is an important indicator of crop health and widely used for determination of nutritional status of the crops real time in precision agriculture.  Advancement in the low altitude remote sensing (LARS) technologies such as Unmanned Aerial vehicles (UAVs) provides high temporal and spatial resolution solution for nondestructive, rapid and accurate estimation of biophysical properties of various crops.  The main objective of this study was to evaluate the high resolution multispectral UAV images for nondestructive and real time estimation of the kinnow tree leaves chlorophyll content in district Sargodha, Pakistan.  Kinnow tree leaves chlorophyll contents were measured manually using chlorophyll meter (SPAD-502 Minolta) in the kinnow orchard along with GPS positions in district Sargodha.  The UAVs images were also acquired during the same time when ground-truthing campaign for kinnow leaves chlorophyll content was performed. Vegetation indices including Normalized Difference Vegetation Index (NDVI), Transformed Normalized Difference Vegetation Index (TNDVI), Modified Chlorophyll Absorbed Ratio Index (MCARI2), Soil adjusted vegetation Index (SAVI) and Modified soil adjusted vegetation index (MSAVI2) were derived by multispectral UAV images for chlorophyll estimation.  The regression analysis was performed between ground-truthing data of chlorophyll content and UAV derived vegetation indices for predicting kinnow leave chlorophyll content model. MSAVI2 and TNDVI were proved to be more robust indices to estimate the chlorophyll content in the kinnow orchard with the highest coefficients of determination ( R 2 ) 0.89 and 0.85 respectively.  The results showed that the multispectral UAV can be used for accurately estimation of chlorophyll content and assess crop health status in a wider range which will help in managing crop nutrition requirement in real time in the kinnow orchard. Keywords: Chlorophyll content, kinnow orchard, Multispectral UAV, Vegetation indices DOI: 10.33440/j.ijpaa.20180101.0001 Citation: Tahir M N, Naqvi S Z A, Lan Y B, Zhang Y L, Wang Y K, Afzal M, et al.  Real time monitoring chlorophyll content based on vegetation indices derived from multispectral UAVs in the kinnow orchard.  Int J Precis Agric Aviat, 2018; 1(1): 24–31.

Monitoring Change of Marshes In South of Iraq by Using Image Processing Techniques for Landsat Images Through Period From 1990 to 2015

This study was conducted for the purpose of monitoring changes in the marshes of southern Iraq, depend on image processing techniques for Landsat images for the period from 1990 to 2015.Landsat satellite images such as TM, ETM+, and LDCM for years 1990,2000, and 2015 in addition to set of maps were used, and then all these data were analysed and extracted the information from it by using ERDAS EMAGINE 2014 program also to extract the final maps layout the ARC GIS 10.2 program was used .Two important indices were extracted from satellite images, Transformed Normalized Difference Vegetation Index (TNDVI) and Normalized Difference Water Index (NDWI) for extract natural vegetation and water in study area. Supervised classification has been used to product three land cover maps for study area. After conducting all necessary analyses, the final results showed that the deterioration has happened largely in the waters of marshes and natural vegetation area in the period from 1990 to 2000 and then this deterioration was beginning decrease gradually and marshes began to recover from 2000 to 2015, there is increase in the surface area of waters of the marshes and natural vegetation in year 2015 than in year 2000, but this increase does not reach to the area of water and natural vegetation in year 1990.

Earthquake-induced built-up damage identification using fuzzy approach

A strong earthquake with magnitude Mw 7.6 shook the Kashmir (Himalayan region) on 8 October 2005, causing wide spread destruction and casualties. The earthquake destroyed approximately 400,000 houses and over 86,000 people lost their lives. The difficult mountainous terrain, with poor accessibility, of the earthquake affected region covering parts of Pakistan and India has become a natural hindrance for any comprehensive ground survey. In such a situation, remotely sensed imagery data from satellites have become an important tool to assess damage due to natural disasters. This work is an attempt to document and identify built-up damage (BD) areas by spectral indices using temporal (pre- and post-earthquake) multispectral images from IRS P6 LISS-IV data. Five spectral indices have been used to identify BD areas damage using supervised Noise Cluster (NC) classifier. The result indicates that Class Based Sensor Independent (CBSI) based Transformed Normalized Difference Vegetation Index (TNDVI) temporal indices data generate better output for BD areas and simple ratio (SR) provide the best results for identifying built-up undamaged (BUD) areas with less entropy as well as membership range.

Evaluation of fuzzy-based classifiers for cotton crop identification

In this study, an evaluation of fuzzy-based classifiers for specific crop identification using multi-spectral temporal data spanning over one growing season has been carried out. The temporal data sets have been georeferenced with 0.3 pixel rms error. Temporal information of cotton crop has been incorporated through the following five indices: simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), to study the effect of indices on classified output. For this purpose, a comparative study between two fuzzy-based soft classification approaches, possibilistic c-means (PCM) and noise classifier (NC), was undertaken. In this study, advanced wide field sensor (AWiFS) data for soft classification and linear imaging self scanner sensor (LISS III) data for soft testing purpose from Resourcesat-1 (IRS-P6) satellite were used. It has been observed that NC fuzzy classifier using TNDVI temporal index – dataset 2, which comprises four temporal images performs better than PCM classifier giving highest fuzzy overall accuracy of 96.03%.

Fuzzy Based Approach for Moist Deciduous Forest Identification Using MODIS Temporal Data

Fuzzy based soft classification have been used immensely for handling the mixed pixel and hence to extract the single class of interest. The present research attempts to extract the moist deciduous forest from MODIS temporal data using the Possibilistic c-Means (PCM) soft classification approach. Temporal MODIS (7 dates) data were used to identify moist deciduous forest and temporal AWiFS (7 dates) data were used as reference data for testing. The Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI) were used to generate the temporal vegetation indices for both the MODIS and the AWiFS datasets. It was observed from the research that the MODIS temporal NDVI data set1, which contain the minimum number of images and avoids the temporal images corresponding to the highest frequency stages of onset of greenness (OG) and end of senescence (ES) activity of moist deciduous forest have been found most suitable data set for identification of moist deciduous forest with the maximum fuzzy overall accuracy of 96.731 %.

Earthquake-induced built-up damage identification using IRS-P6 data: a comparative study using fuzzy-based classifiers

The 8 October 2005 earthquake caused widespread destruction in both the state of Jammu and Kashmir of India and Northern Pakistan. Due to poor accessibility in the hazardous and difficult mountainous terrain, a proper and comprehensive ground-based survey was not possible. However, with the help of remote sensing data and its analysis techniques, it is feasible to undertake both earthquake-related damage identification and assessment. This study attempts to document and identify built-up damaged (BD) areas using spectral indices taking temporal multispectral images from IRS-P6 LISS-IV. Five spectral indices have been used to identify BD areas using supervised possibilistic c-means (PCM) and noise cluster (NC) classifiers, to analyse the satellite data. The result indicates that Class Based Sensor Independent (CBSI) based Transformed Normalized Difference Vegetation Index (TNDVI) temporal indices provide the best results for identifying BD areas, while Simple Ratio (SR) index gives the best results for built-up undamaged area identification. Further, it observed that PCM classifier performed better in comparison to NC classifier.

Detection of change in vegetation cover using multi-spectral and multi-temporal information for district Sargodha, Pakistan

Detection of change is the measure of the distinct data framework and thematic change information that can direct to more tangible insights into underlying process involving land cover and landuse changes. Monitoring the locations and distributions of land cover changes is important for establishing links between policy decisions, regulatory actions and subsequent landuse activities. Change detection is the process that helps in determining the changes associated with landuse and land cover properties with reference to geo-registered multi-temporal remote sensing information. It assists in identifying change between two or more dates that is uncharacterized of normal variation. After image to image registrations, the normalized difference vegetation index (NDVI), the transformed normalized difference vegetation index (TNDVI), the enhanced vegetation index (EVI) and the soil-adjusted vegetation index (SAVI) values were derived from Landsat ETM+ dataset and an image differencing algorithm was applied to detect changes. This paper presents an application of the use of multi-temporal Landsat ETM+ images and multi-spectral MODIS (Terra) EVI/NDVI time-series vegetation phenology metrics for the District Sargodha. The results can be utilized as a temporal land use change model for Punjab province of Pakistan to quantify the extent and nature of change and assist in future prediction studies. This will support environmental planning to develop sustainable landuse practices.

TEMPORAL INDICES DATA FOR SPECIFIC CROP DISCRIMINATION USING FUZZY BASED NOISE CLASSIFIER

Abstract. Evaluation of fuzzy based classifier to identify and map a specific crop using multi-spectral and time series data spanning over one growing season. The temporal data is pre-processed with respect to geo-registration and five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil- Adjusted Vegetation Index) and TVI (Triangular Vegetation Index). The noise classifier (NC) is evaluated in sub pixel classification approach and accuracy assessment has been carried out using fuzzy error matrix (FERM). The classification results with respect to the additional indices were compared in terms of image to image maximum classification accuracy. The overall accuracy observed in dataset 2 was 96.03% for TNDVI indices, using NC. Data used for this study was AWIFS for soft classification and LISS-III data for soft testing generated from Resourcesat-1(IRS-P6) satellite. The research indicates that appropriately used indices can incorporate temporal variations while extracting specific crop of interest with soft computing techniques for images having coarser spatial and temporal resolution remote sensing data.

SOFT COMPUTING APPROACH FOR LIQUEFACTION IDENTIFICATION USING LANDSAT-7 TEMPORAL INDICES DATA

Abstract. A strong earthquake with magnitude Mw7.7 that shook the Indian Province of Gujarat on the morning of January 26, 2001, caused widespread appearance of water bodies and channels, in the Rann of Kachchh and the coastal areas of Kandla port. In this work, the impact of using conventional band ratio indices from Landsat-7 temporal images for liquefaction extraction was empirically investigated and compared with Class Based Sensor Independent (CBSI) spectral band ratio while applying noise classifier as soft computing approach via supervised classification. Five spectral indices namely, SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and Modified Normalized Difference Water Index (MNDWI) were investigated to identify liquefaction using temporal multi-spectral images. It is found that CBSI-TNDVI with temporal data has higher membership range (0.968–0.996) and minimum entropy (0.011) to outperform for extraction of liquefaction and for water bodies extraction membership range (0.960–0.996) and entropy (0.005) respectively.

Liquefaction identification using class-based sensor independent approach based on single pixel classification after 2001 Bhuj, India earthquake

A strong earthquake with magnitude 7.7 that shook the Indian Province of Gujarat on the morning of January 26, 2001 caused wide spread destruction and casualties. Earthquakeinduced ground failures, including liquefaction and lateral spreading, were observed in many areas. Optical remote sensing offers an excellent opportunity to understand the post-earthquake effects both qualitatively and quantitatively. The impact of using conventional indices from Landsat-7 temporal images for the liquefaction is empirically investigated and compared with class-based sensor independent (CBSI) indices, while applying possibilistic fuzzy classification as a soft computing approach via supervised classification. Five spectral indices, namely simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) are investigated to identify liquefaction using temporal multi-spectral images. A soft-computing based fuzzy algorithm, which is independent of statistical distribution data assumption, is used to extract a single land cover class from remote sensing multi-spectral images. The result indicates that appropriately used indices can incorporate temporal variations, while extracting liquefaction with soft computing techniques for coarser spatial resolution with temporal remote sensing data. It is found that CBSI-NDVI with temporal data was good for extraction liquefaction while CBSI-TNDVI with temporal data was good for extraction water bodies.

Spectral vegetation indices performance evaluated for Cholistan Desert

To enhance the vegetation signal in remotely sensed data and provide an approximate measure of live green vegetation, a number of spectral vegetation indices have been developed to estimate biophysical parameters of vegetation. The sensitivity of the normalized difference vegetation index (NDVI) to the soil background and atmospheric effects has generated an increasing interest in the development of new indices. The modified soil-adjusted vegetation index (MSAVI) and its later revision, MSAVI2, are soil-adjusted vegetation indices that seek to address some of the limitations of NDVI when applied to areas with a high degree of exposed soil surface because the reflectance of light in the red and near-infrared (NIR) spectra can influence vegetation index values. The soil-adjusted vegetation index (SAVI) was developed as a modification ofthe NDVI to correct for the influence of soil brightness when vegetative cover is low. The problem with the SAVI is that it required specifying the soil-brightness correction factorL through trial-and-error based on the amount of vegetation. This article focuses on testing and comparing the sensitivity of vegetation indices to soil background effects. Five vegetation indices; NDVI, transformed normalized difference vegetation index (TNDVI), enhanced vegetation index (EVI), SAVI and MSAVI2 were quantitatively evaluated using Landsat ETM+ dataset over the Cholistan Desert to find the best vegetation index for use in sparsely vegetated semi-arid and arid tracts of Pakistan. Key words: Atmospheric effects, Cholistan, Landsat ETM+, Remote sensing, Spectral vegetation indices.

Cotton Crop Discrimination Using Fuzzy Classification Approach

Crop growth information represented through temporal remote sensing data is of great importance for specific agriculture crop discrimination. In this paper, the effect of various indices was empirically investigated using temporal images for cotton crop discrimination. Five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and TVI (Triangular Vegetation Index) were investigated to identify cotton crop using temporal multi-spectral images. Data used for this study was AWIFS (coarser resolution) for soft classification and LISS-III (medium coarser) data for soft testing from Resourcesat-1 (IRS-P6) satellite. The mixed pixel (i.e. multiple classes within a single pixel) problem had been handled using soft computing techniques. Possibilistic fuzzy classification approach is used to handle mixed pixels for extracting single class of interest. The classification results with respect to various indices were compared in terms of image to image fuzzy overall classification accuracy. It was observed that temporal SAVI indices database with data set-2 outperformed other temporal indices database for cotton crop discrimination. Temporal SAVI indices database gave highest fuzzy overall accuracy of 93.12% with data set-2 in comparison to others.

Monitoring post-tsunami vegetation recovery in Phang-Nga province, Thailand, based on IKONOS imagery and field investigations – a contribution to the analysis of tsunami vulnerability of coastal ecosystems

A major tsunami in December 2004 devastated the coastal ecosystems along the Andaman Sea coast of Thailand. Since intact coastal ecosystems provide many important services for local communities at the Andaman Sea, it is crucial to investigate to what extent (in terms of percentage area and speed) the affected ecosystems were capable of recovering after the tsunami. Field measurements and multi-date IKONOS imagery were used to estimate the recovery and succession patterns of coastal vegetation types in the Phang-Nga province of Thailand, three years after the tsunami. Thus, this study contributes to a holistic understanding of the ecological vulnerability of the coastal area to tsunamis. A zone-based change detection approach is applied by comparing two change detection techniques: the first method involves the calculation of a recovery rate based on multi-temporal TNDVI (transformed normalized difference vegetation index) images (TNDVI approach), whereas the second approach is a combined approach of the change vector analysis (CVA). Although these two methods provide different types of information (quantitative for the TNDVI approach, qualitative for the CVA), they are comparable in terms of results and accuracies. The results reveal that recovery processes vary based on the type of the ecosystem and, furthermore, are strongly influenced by human activities. Grasslands, coconut plantations and the mixed vegetation cover could recover faster than the mangroves and casuarina forests. Among the forest ecosystems, recovery rates of casuarina forests were higher than for mangroves, but the recovery area was smaller. This study also discusses the potential and some limitations and inaccuracies of applying high-resolution optical imagery for assessing vegetation recovery at a local scale.

Estimating east Mediterranean forest parameters using Landsat ETM

The conservation of Jordan's Mediterranean forest requires the use of remote sensing. Among the most important parameters needed are the crown-cover percentage (C) and above-ground biomass (A). This study aims to: (1) identify the best predictor(s) of C using Landsat Enhanced Thematic Mapper (ETM) bands and the derived transformed normalized difference vegetation index (TNDVI); (2) determine if C is a good predictor of A, volume (V), Shannon diversity index (S) and basal area (B); and (3) generate maps of all these parameters. A Landsat ETM image, aerial photographs and ground surveys are used to model C using multiple regression. C is then modelled to A, V, S and B using linear regression. The relationship between C and Landsat ETM bands (1 and 7) plus the TNDVI is significantly high (coefficient of determination R 2 = 0.8) and is used to produce the C map. The generated C map is used to predict A (R 2 = 0.56), V (R 2 = 0.58), S (R 2 = 0.50) and B (R 2 = 0.43). Cross validation for the predicted C map (cross-validation error = 5.3%) and for the predicted forest-parameter maps (cross-validation error = 13.7%–19.9%) shows acceptable error levels. Results indicate that Jordan's east Mediterranean forest parameters can be mapped and monitored for biomass accumulation and carbon dioxide (CO2) flux using Landsat ETM images.