Multispectral Satellite Research Articles

Timing of red-edge and shortwave infrared reflectance critical for early stress detection induced by bark beetle (Ips typographus, L.) attack

Forest disturbance in Europe, induced by European spruce bark beetle Ips typographus, L., results in regional-scale dieback. Early stress detection in Norway spruce stands caused by bark beetle infestation at the green attack stage (when trees are yet to show distinct symptoms observable by the human eye) is crucial and can lead to improved forest management and reduced economic losses. This study aims to investigate and understand the dynamics of leaf traits and reflectance of Norway spruce (Picea abies) trees during bark beetle attack. Using high-resolution temporal images from RapidEye and SPOT-5 in parallel with the collection of field data, we examined which spectral regions and leaf traits are affected by infestation over time and how they help the discrimination between healthy and infested plots at the early stage of the attack. To achieve this aim, we used a novel approach by targeting both leaf and canopy level. We measured leaf reflectance spectra and six leaf traits (water content, nitrogen, chlorophyll fluorescence, chlorophyll and stomatal conductance) from 66 (30 plots) healthy and 54 (8 plots) infested trees at three consecutive time measurements in the summer of 2015 in the Bavarian Forest National Park. Concurrently, canopy reflectance and spectral vegetation indices (SVIs) were extracted from the selected plots (30 healthy plots) using seven RapidEye images and six SPOT-5 images. For the infested plots, in addition to the field measured plots (8), canopy spectral reflectance were extracted from the reference infestation data (291 plots) obtained through visual interpretation of high-resolution aerial photographs. Results demonstrated significant differences (p < 0.05) in the studied leaf traits between healthy and infested samples, and these differences increased with the progression of infestation. We found that leaf and canopy reflectance were significantly higher (p ≤ 0.05) for the infested trees by bark beetle than the healthy ones in the ‘red edge’ (680–790 nm) and ‘shortwave infrared’ (1110–1490 nm) spectrum throughout the infestation event. Our results further demonstrated that the spectral vegetation indices calculated from the red-edge and SWIR spectral bands, such as NDRE, DSWI, LWCI and NDWI, were able to differentiate between healthy and infested trees earlier than the other SVIs. The new insight offered by these results is that the red-edge and SWIR spectral information from multispectral satellites has the potential to considerably improve monitoring and detection of forest stress and has important implications for European field bark beetle management and future studies.

Multi Spectral Satellite Data to Investigate Land Expansion and Related Micro Climate Change as Threats to the Environment

Technology development of satellite earth observation offers multi-spectral data, variations sensitivity of satellite sensors are very useful to detect existing conditions by using the remote sensing algorithm to extract the land cover and the phenomenon that has occurred. Ecosystems have bound tolerance to nature condition and organisms have adaptation limit to climate change. Method used in this study was the extraction of land cover by applying multi spectral analysis as Maximum Likelihood, object base image analysis, (LST) land surface temperature, and also the formula Threat of Land Expansion (TLE) to the environment. Result showed that multispectral and time series analysis gave the increasing dynamic of land expansion, especially for forest high density, which areas ranged from 29098.73 ha in 2000 to 19216.7 ha in 2018. The surface temperature increase was ± 2.8 C° in eighteen years. The temperature dynamic represented the micro climate changes, has followed stream of land expansion. TLE represented zone threats of land expansion to natural ecosystems. It was found that threat zone because land expansion. The area of high threat vale 0.4 around central human activity was a built up area.

High Resolution Satellite Image Compression using DCT and EZW

Image Compression has played vital role for efficient image storage, retrieval and transmission. In today’s digital world, images are captured with high wavelength camera lens for better quality, but requires more storage space. Comparison is carried out on multi spectral satellite images using DCT and EZW on basis of compression ratio, PSNR and MSE. DCT performs efficiently at medium bit rates giving compression ratio over 99% and low PSNR whereas EZW performs well at low bit rate giving efficient compression ratio. Disadvantage with DCT is that only spatial correlation of the pixels is considered and the neighborhood correlation is neglected, on the other hand, use of larger EZW wavelet functions produced blurred images.

Reconstructing Cloud Contaminated Pixels Using Spatiotemporal Covariance Functions and Multitemporal Hyperspectral Imagery

One of the major challenges in optical-based remote sensing is the presence of clouds, which imposes a hard constraint on the use of multispectral or hyperspectral satellite imagery for earth observation. While some studies have used interpolation models to remove cloud affected data, relatively few aim at restoration via the use of multi-temporal reference images. This paper proposes not only the use of image time-series, but also the implementation of a geostatistical model that considers the spatiotemporal correlation between them to fill the cloud-related gaps. Using Hyperion hyperspectral images, we demonstrate a capacity to reconstruct cloud-affected pixels and predict their underlying surface reflectance values. To do this, cloudy pixels were masked and a parametric family of non-separable covariance functions was automated fitted, using a composite likelihood estimator. A subset of cloud-free pixels per scene was used to perform a kriging interpolation and to predict the spectral reflectance per each cloud-affected pixel. The approach was evaluated using a benchmark dataset of cloud-free pixels, with a synthetic cloud superimposed upon these data. An overall root mean square error (RMSE) of between 0.5% and 16% of the reflectance was achieved, representing a relative root mean square error (rRMSE) of between 0.2% and 7.5%. The spectral similarity between the predicted and reference reflectance signatures was described by a mean spectral angle (MSA) of between 1° and 11°, demonstrating the spatial and spectral coherence of predictions. The approach provides an efficient spatiotemporal interpolation framework for cloud removal, gap-filling, and denoising in remotely sensed datasets.

Remote Sensing Data to Detect Hessian Fly Infestation in Commercial Wheat Fields

Remote sensing data that are efficiently used in ecological research and management are seldom used to study insect pest infestations in agricultural ecosystems. Here, we used multispectral satellite and aircraft data to evaluate the relationship between normalized difference vegetation index (NDVI) and Hessian fly (Mayetiola destructor) infestation in commercial winter wheat (Triticum aestivum) fields in Kansas, USA. We used visible and near-infrared data from each aerial platform to develop a series of NDVI maps for multiple fields for most of the winter wheat growing season. Hessian fly infestation in each field was surveyed in a uniform grid of multiple sampling points. For both satellite and aircraft data, NDVI decreased with increasing pest infestation. Despite the coarse resolution, NDVI from satellite data performed substantially better in explaining pest infestation in the fields than NDVI from high-resolution aircraft data. These results indicate that remote sensing data can be used to assess the areas of poor growth and health of wheat plants due to Hessian fly infestation. Our study suggests that remotely sensed data, including those from satellites orbiting >700 km from the surface of Earth, can offer valuable information on the occurrence and severity of pest infestations in agricultural areas.

A new image classification approach for mapping coral density in State of Kuwait using high spatial resolution satellite images

ABSTRACTRemote sensing technology can be a valuable tool for mapping coral reef ecosystems. However, the resolution capabilities of remote sensors, the diversity and complexity of coral reef ecosystems, and the low reflectivity of marine environments increase the difficulties in identifying and classifying their features. This research study explores the capability of high spatial resolution (WorldView-2 (WV-2) and Pleiades-1B) and low spatial resolution (Land Remote-Sensing Satellite (Landsat 8)) multispectral (MS) satellite sensors in quantitatively mapping coral density. The Kubbar coral reef ecosystem, located in Kuwait’s southern waters, was selected as the research site. The MS imagery of WV-2, Pleiades-1B and Landsat 8 were, after geometric and radiometric assessment and corrections, subjected to new image classification approach using a Multiple Linear Regression (MLR) analysis. The new approach of MLR coral density analysis used the dependent variable of coral density percentage from ground truth and independent variables of spectral reflectance from selected imagery, depth (as estimated from a surface derived from bathymetric charts) and distance to land or reef unit centre. Accuracy assessment using independent ground truth was performed for the selected approach and satellite sensors to determine the quality of the information derived from image classification processes. The results showed that coral density maps developed using the MLR coral density model proved to have some level of reliability (radiometrically corrected WV-2 image (the coefficient determination denoted as R-squared (R²) = 0.5, Root-Mean-Square Error (RMSE) = 10) and radiometrically corrected Pleiades-1B image (R² = 0.8, RMSE = 10)). This study suggested using high spectral resolution data and including additional factors (variables) (e.g. water turbidity, temperature and salinity) could contribute to improving the accuracy of coral density maps produced by application of the MLR model; however, all of these would add cost and effort to the mapping process. The outcomes of this research study provide coral reef ecosystem researchers, managers, and decision makers a tool to determine and map coral reef density in more detail than in the past. It will help quantify coral density at particular points in time leading to estimates of change, and allow coral reef ecologists to identify the current coral reef habitat health status, distribution and extent.

Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data

Tropical montane forests, particularly Andean rainforest, are important ecosystems for regional carbon and water cycles as well as for biological diversity and speciation. Owing to their remoteness, however, ecological key-processes are less understood as in the tropical lowlands. Remote sensing allows modeling of variables related to spatial patterns of carbon stocks and fluxes (e.g., biomass) and ecosystem functioning (e.g., functional leaf traits). However, at a landscape scale most studies conducted so far are based on airborne remote sensing data which is often available only locally and for one time-point. In contrast, multispectral satellites at moderate spectral and spatial resolutions are able to provide spatially continuous and repeated observations. Here, we investigated the effectiveness of Landsat-8 imagery in modeling tropical montane forest biomass, its productivity and selected canopy traits. Topographical, spectral and textural metrics were derived as predictors. To train and validate the models, in-situ data was sampled in 54 permanent plots in forests of southern Ecuador distributed within three study sites at 1000 m, 2000 m and 3000 m a.s.l. We used partial least squares regressions to model and map all response variables. Along the whole elevation gradient biomass and productivity models explained 31%, 43%, 69% and 63% of variance in aboveground biomass, annual wood production, fine litter production and aboveground net primary production, respectively. Regression models of canopy traits measured as community weighted means explained 62%, 78%, 65% and 65% of variance in leaf toughness, specific leaf area, foliar N concentration, and foliar P concentration, respectively. Models at single study sites hardly explained variation in aboveground biomass and the annual wood production indicating that these measures are mainly determined by the change of forest types along with elevation. In contrast, the models of fine litter production and canopy traits explained between 8%–85% in variation depending on the study site. We found spectral metrics, in particular a vegetation index using the red and the green band to provide complementary information to topographical metrics. The model performances for estimating leaf toughness, biochemical canopy traits and related fine litter production all improved when adding spectral information. Our findings therefore revealed that differences in fine litter production and canopy traits in our study area are driven by local changes in vegetation edaphically induced by topography. We conclude that Landsat-derived metrics are useful in modeling fine litter production and biochemical canopy traits, in a topographically and ecologically complex tropical montane forest.

Evaluation of HJ-1A/B CCD Surface Reflectance Products Using the VNIR and MODIS-Based Atmospheric Correction Approaches

The growing development of medium- to high-resolution satellites in China has led to a considerable increase in the quantitative applications using the data; therefore, it is important to produce standard surface reflectance (SR) products operationally from such data. However, there is still a lack of relevant SR systems and SR products. We applied two atmospheric correction algorithms, adaptive to most multispectral satellites with visible and near-infrared (VNIR) bands, to HJ-1A/B charge-coupled device (CCD) instrument data, namely, the VNIR method and the MODIS-based method, with both methods being based on the Second Simulation of the Satellite Signal in the Solar Spectrum, Vector (6SV) code, and the look-up tables. We evaluated the accuracy of the SR by these two approaches for HJ-1A/B CCD images compared with the AErosol RObotic NETwork (AERONET) corrected reflectance for the period July 2011 to June 2012 over China and surrounding regions. We assessed more than 12 million pixels for the 49 spatial circular subsets, with a radius of 5 km, centered at 12 AERONET sites. The evaluation results indicated that both methods were suitable for operational flow, and that the MODIS-based method had better accuracy than the VNIR method, except for the near-infrared band. This conclusion was also validated by comparison with the normalized difference vegetation index products derived from the MODIS-based SR, VNIR SR, and AERONET SR. Additionally, the MODIS-based method showed superior accuracy when the overpass time of HJ-1A/B was more approximate to that of Terra MODIS.

Image Segmentation Based Approach for the Purpose of Developing Satellite Image Spatial Information Extraction for Forestation and River Bed Analysis

Classification of remote sensing spatial information from multi spectral satellite imagery can be used to obtain multiple representation of the image and capture different structure lineaments. Pixels are grouped using clustering and morphology based segmentation for region based spatial information. This is used to calculate the spatial features of the contiguous regions by classifying the region into the statistics of the pixel properties. In the proposed work, analysis of Google Earth images for identification of morphological patterns of the river flow is done for remote sensing image using graph-cuts. Multi-temporal satellite images acquired from Google Earth to identify the digital elevation is used to formulate the energy function from images to compare the displacement in pixel value using similarity measure. A method is proposed to solve non-rigid image transformation via graph-cuts algorithm by modeling the registration process as a discrete labeling problem. A displacement vector associated to each pixel in the source image indicates the corresponding position in the moving image. The transformation matrix produced from change in the intensity of the pixels for a region is then optimized for energy minimization by using the graph-cuts algorithm and demon registration technique. The proposed study enhances the advantages of regional segmentation in order to know homogeneous areas for optimal image segmentation and digital footprints for change in the river bed patterns by identifying the change in LANDSAT data from temporal satellite images. By applying the proposed multi-level registration method, the number of labels used in each level is greatly reduced due to lower image resolution being used in coarser levels. The results demonstrate that the lineament detection for better accuracy compared to traditional sources of lineament identification methods. It has provided better geotectonic understanding of Cudappah rock in Ahobhilam with Quartzite. The imprints of Eastern Ghat orogeny are seen in upper stream section through a graph cut based segmentation approach.

Multicore Platform Efficiency Across Remote Sensing Applications

A wide range of modern system architectures and platforms targeted for different algorithms and application areas is now available. Even general-purpose systems have advantages in some computation areas and bottlenecks in another. Scientific applications on specific areas, on the other hand, have different requirements for CPU performance, scalability and power consumption. The best practice now is algorithm/architecture co-exploration approach, where scientific problem requirements influence the hardware configuration; on the other hand, algorithm implementation is re factored and optimized in accordance with the platform architectural features. In this research, two typical modules used for multispectral nighttime satellite image processing are studied: • measurement of local perceived sharpness in visible band using the Fourier transform; • cross-correlation in a moving window between visible and infrared bands. Both modules are optimized and studied on wide range of up-to-date testbeds, based on different architectures. Our testbeds include computational nodes based on Intel Xeon E5-2697A v4, Intel Xeon Phi, Texas Instruments Sitara AM5728 dual-core ARM Cortex-A15, and NVIDIA JETSON TX2. The study includes performance testing and energy consumption measurements. The results achieved can be used for assessing serviceability for multispectral nighttime satellite image processing by two key parameters: execution time and energy consumption.

Shape feature-assisted discrimination of cloud and snow in GF-1 spectral images of mountainous areas

ABSTRACTIn this study, we propose a new method for cloud and snow discrimination, which is based on spectral, texture, and shape features and combined with support vector machine multi-classification strategy. A new feature called curvature histogram is designed to describe edge shape. First, the region of interest test is applied to extract cloud and snow area. Then, the extracted area is combined with the segmentation results using the mean shift algorithm to obtain the object of interest while calculating the feature values. The complexity of surfaces in the cloud and snow area is classified into four types, namely, thick cloud, thin cloud, snow, and snow-covered land, such that six kinds of classifiers are obtained by designing a classifier between every two categories. Through six classifiers and calculating the sum of confidence coefficients for each category, every superpixel is classified into the class with the highest confidence coefficient and a rough cloud and snow mask is obtained. Finally, the GrabCut algorithm is applied to optimize the classification results at the pixel level. The experiments on the images of China’s GF-1 satellite, indicate that the proposed method is effective for cloud and snow discrimination on multispectral high-resolution satellites images in mountainous area.

Feature extraction of coastal surface inundation via water index algorithms using multispectral satellite on North Jakarta

The study aims to characterize water index algorithms using Sentinel 2A-MSI and Landsat 8-OLI sensors to generate the performance of CSI and its proper uses in science. Feature extraction of the 35 combinations of CSI specifically uses information deriving from a number of water index spectral bands. Supervised classification is used to categorize water and non-water features using Spectral Angle Mapper (SAM). Furthermore, Dynamic Surface Water Extent (DSWE) algorithm was taken to mapping inundation and non-inundation. Result of this research shows that the best water index algorithms is exhibited by the MNDWI algorithm where it can enhance the water reflectance to be positive value. Re-classification on all water index algorithms indicates a similar pattern to the feature extraction of water and non-water. It is determined by overall accuracy (99.56-99.99%), Kappa coefficient (0.97-1.00), producer accuracy (99.05-99.99%) and user accuracy (93.43-99.94%). Substituting all water index algorithms into DSWE algorithm points out that the MNDWI is still the good performance and other substituted algorithms generate low performances. Overall feature extractions of CSI in North Jakarta at this research manifests that the MNDWI algorithm using multi-spectral and satellites is the best algorithm.

Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption peaks). Algorithms’ indices are correlated with measured Chla through the regression process. The main drawback of the semi-analytical algorithms is that the derived relation is location and data limited. Spectrum matching and the look-up table approach rely on matching the measured reflectance with a large library of simulated references corresponding to wide ranges of water properties. The spectral matching approach taking hyperspectral measured reflectance as an input, leading to difficulties in incorporating data from multispectral satellites. Consequently, multi-algorithm indices and the look-up table (MAIN-LUT) technique is proposed to combine the merits of semi-analytical algorithms and look-up table, which can be applied to multispectral data. Eight combinations of four algorithms (i.e., 2-band, 3-band, maximum chlorophyll index, and normalized difference chlorophyll index) are investigated for the MAIN-LUT technique. In situ measurements and Medium Resolution Imaging Spectrometer (MERIS) sensor data are used to validate MAIN-LUT. In general, the MAIN-LUT provide a comparable retrieval accuracy with locally tuned algorithms. The most accurate of the locally tuned algorithms varied among datasets, revealing the limitation of these algorithms to be applied universally. In contrast, the MAIN-LUT provided relatively high retrieval accuracy for Tokyo Bay (R2 = 0.692, root mean square error (RMSE) = 21.4 mg m−3), Lake Kasumigaura (R2 = 0.866, RMSE = 11.3 mg m−3), and MERIS data over Lake Kasumigaura (R2 = 0.57, RMSE = 36.5 mg m−3). The simulated reflectance library of MAIN-LUT was generated based on inherent optical properties of Tokyo Bay; however, the MAIN-LUT also provided high retrieval accuracy for Lake Kasumigaura. MAIN-LUT could capture the spatial and temporal distribution of Chla concentration for Lake Kasumigaura.

An Integrated Approach of Remote Sensing and Gis for Land Use and Land Cover Change Detection: A Case Study of Banjar River Watershed Of Madhya Pradesh, India

Land use and land cover of a region is one of the prime concerns in current strategies for the evaluationof the watersheds and development of decision making policies. An uncertain increase in the population of the country along with the increasing demands has imposed an immense pressure on the land threatening the sustainability of the natural resources especially in the developing countries like India. The study was carried out to detect the land use and land cover changes observed in the Banjar River watershed lies in between Balaghat and Mandla districts of Madhya Pradesh, India using the multi spectral satellite dataset for two different years i.e. 2009 &amp; 2013. The supervised classification was done in ERDAS IMAGINE software. The images of the study area was classified into seven classes namely, river, water body, waste land, habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The result indicates that during the last five years, forest, water body, waste land and open land/fallow land/barren has been increased by 2.26%, 0.55%, 0.23% and 0.48% respectively and the river, habitation and agriculture/other vegetation has been decreased by 0.26%, 0.04%, and 3.22% respectively. Accuracy assessment was also performed in this study to determine the quality of land use/cover map and overall accuracy was found 89.70% for 2009 and 91.91% for 2013.Present study emphasizes on digital change detection techniques for recognizing temporal changes in land use/land cover of the watershed. Outcomes of this study indicate need of implementing conservation and management practices for the socioeconomic development.

Urban landcover classification from multispectral image data using optimized AdaBoosted random forests

ABSTRACTWith an ever growing need to classify multispectral images, the accuracy of the classification becomes a matter of concern, especially when mapping heterogeneous environments such as urban areas. Not all of the features of a high-resolution multispectral image would equally contribute to the formulation of an ideal hypothesis, some of which might be redundant and might hinder the classification itself. Random forests (RF), an ensemble of decision trees, would assist in the removal of such extraneous features. While RF has proved to be one of the most efficient classifiers for spectral imagery, it holds untapped potential which could be honed to yield much more accurate prediction rates. This article discusses the application of AdaBoosted random forest (ABRF) to classify landcover segments from multispectral satellite or aerial imagery. A stage-wise optimization procedure is applied to achieve the best possible performance. It involves genetic feature selection and strategic empirical calibration to determine the ideal set of parameters. The application of boosting to RF resulted in the increase in the overall accuracy from 84.42% to 88.8% with an increase in kappa coefficient from 0.804 to 0.859.

The main issues of the Cyrene necropolis and the use of remote sensing for monitoring in the case of the eastern necropolis

AbstractCyrene offers one of the largest and most spectacular necropoleis of the Mediterranean basin and, owing especially to its vastness, it is particularly difficult to control and protect. It reveals an extraordinary patrimony of rock-cut architecture, monumentally built around the ancient site, and also represents a zone at major risk of damage and destruction as a result of continuous looting, uncontrolled urbanisation and vandalism. Recent studies by Jim and Dorothy Thorn have presented a preliminary view and gazetteer of the architectonic monumentality, especially of the rocky chamber tombs, showing the need for survey projects in the area. A joint team of Libyan and Italian archaeologists, geologists, topographers and technicians of the local Department of Antiquities and Chieti University started in 1999 a project of surveying and mapping the southern and eastern parts of the necropolis, giving birth to a GIS using differential GPS and Robotic total station for the positioning and recording of the tombs, as well as multispectral HD satellite images, previously orthorectified and georeferred, combined with old maps and RADAR data for a highly detailed topographic base, up to DEM and DTM. From 2004, the survey and the GIS have been extended to the western and northern parts of the necropolis, at the moment counting more than 2,000 mapped and recorded tombs. An important step in the last two years has been the use of remote-sensing and photo-interpretation analysis in order to map the damages of urbanisation and modern construction in the areas of the necropolis. Using images covering the development of the situation every two to three months, mainly shots coming from Google Earth archives, in combination with further satellite images specifically bought for a more complete view of the last two to three years, it has been possible to start drawing a map of the areas under threat from building works and urbanisation. The aim of this paper is to present the main problems of this huge necropolis, which is at the moment one of the most threatened areas of Cyrene, every day at risk from the destruction of its monumental buildings.

Use of remotely sensed data to characterize weed competition in forest plantations

ABSTRACTCharacterization of weed size using remotely sensed data could provide a useful means of scheduling post-plant weed control in plantation forests. Data from four forestry stands, which included field measurements of weed competition and multispectral data collected from an unmanned aerial vehicle (UAV) and satellite platform (WorldView3), were used to (i) develop models to predict overall weed percentage cover (C), height (H), and a competition index, CI, (C × H/100) derived from these two metrics; and (ii) determine the optimal spatial resolution for imagery from these two platforms for characterizing these measures of weed competition.Bivariate analysis of the three weed metrics showed the optimal spatial resolution to be 5 m for the WorldView3 data and 1 m for data obtained from the UAV. The best multiple regression models developed to predict C, H, and CI had coefficient of determinations (R2) of, respectively, 0.33, 0.68, and 0.56 for data derived from WorldView3 and, respectively, 0.56, 0.79, and 0.71 for data derived from the UAV. Vegetation indices, derived from the multispectral UAV data and satellite imagery, were strong predictors of weed metrics for all but one of the six models developed. These results suggest that it may be possible for forest managers to use satellite imagery and UAV data to schedule weed control operations.

Detecting the Phenology and Discriminating Mediterranean Natural Habitats With Multispectral Sensors—An Analysis Based on Multiseasonal Field Spectra

Due to their high degree of vegetation heterogeneity, fragmentation, and biodiversity, Mediterranean natural habitats are difficult to assess and monitor with in situ observations solely. Together with standardized ground plots and regular in situ measurements, remote sensing contributes to better understand the diversity of these habitats and their phenology. We used field spectroradiometry to simulate the radiometric signal corresponding to six multispectral satellites: 1) IKONOS, 2) Landsat 5 TM, 3) Landsat 8, 4) Pléiades, 5) Sentinel-2, and 6) WorldView-2. We compared the suitability of each sensor for the estimation of the cover fraction of photosynthetic vegetation (PV) observed for five types of habitats during a vegetation cycle from February to October 2013. We also analyzed the contribution of multiseasonal satellite acquisitions for habitat discrimination. We showed that multivariate regression applied to Worldview-2 reflectance produces the most accurate PV. This was explained by the higher number of spectral bands in the visible domain. Habitat discrimination based on monotemporal acquisitions showed better performances when PV was higher. Sentinel-2 and WorldView-2 outperformed other sensors for each individual date. Multitemporal acquisitions outperformed monotemporal acquisition for habitat discrimination. However, selecting all reflectance data acquired during the season resulted in suboptimal performances compared to more parsimonious combinations. Finally, all of them ranged between 86.6% and 89.2% classification accuracy with multiseasonal acquisitions. New strategies need to be designed to identify individual habitats of particular interest. Defining optimal multiseasonal remote-sensing acquisitions specific to each habitat and appropriate spectral and spatial resolution will contribute to improved discrimination of Mediterranean natural habitats.

Band Aalysis for Land Use in Multi Spectral Images

spectral and multi spectral image analysis is the commonly used technique for land use and land cover classification. Effective use of the land cover can play a vital role in the development of country. Multi spectral satellites use passive sensor, hence the only source of energy involved in the acquisition of satellite imagery is the reflectance of the sun. In order to investigate the role of individual bands of the Visible and infra-red region in the recognition of land covers such as vegetation, non-vegetation, settlements and barren land an extensive research has been carried out. This paper is focused in the dissection and contribution of individual component (band) of SPOT-5 imagery for land cover analysis as well. In this article extensive experimentation has been carried out which reveals the effect of individual and combine bands in the recognition of land cover. Classifications of various bands were done using supervised machine learning classification, random forest classifier has been used for classification purpose.

Changes in Ecological Landscape Pattern and Land Use from 1985 to 2014 in the Panshet Dam Catchment

The Sahyadri, the northern part of Western Ghats in the state of Maharashtra, India, are one of the most fragile ecosystems on Earth. There are major changes in the original ecosystem and land use in the Sahyadri over the past two centuries. A ground survey was conducted by Ecological Society, Pune in 1985 as well as in 2014 to assess current land use and status of biodiversity in the Panshet dam catchment situated in Sahyadri. This study probes to understand factors responsible for decline of biodiversity and changed land use. We highlight the extent and causes for change in land use. The study attempted to quantify this change by carrying out manual digitization of multispectral WV-2 satellite images by visual interpretation and ground truthing and compare with the study done by Ecological Society in 1985. The mapping of the catchment area shows that areas towards western escarpment i.e. away from the dam wall have retained better vegetation cover while degradation is evident in eastern parts of the catchment. Such studies will be useful for land use planning, understanding cost-benefits of nature conservation, assessing restoration potential, and developing conservation-restoration strategies on a regional scale. We recommend similar vegetation and land use class mappingfor all dam catchments in the Western Ghats.