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Mangrove resources provide economic and ecological value. The expansion of residential areas makes the level of need for shelter as well as the region increases. This will have an impact on the utilization of mangrove in sufficient needs such as firewood, charcoal and building materials. If continue berlasngsung then do not deny the existence of mangrove degradation. The occurrence of mangrove degradation will have a direct impact on the local community. Considering the importance of mangrove function and the need for data of mangrove area change, it is needed to analyze the change of mangrove area in coastal of Bangka Tengah, which will be analyzed within 15 years in 2002 and 2017, so that the result of this study is expected to be further management by local government to conserve mangrove forests. This research was conducted from December 2017 to March 2018 with location station determination based on unsupervised image classification, that is classification based on digital number using RGB_231 image composite for ASTER image. Based on the results of the study, there is a change of mangrove area from 2002-2014 as much as 176.3 Ha or about 15.95% and in 2014-2017 there is an increase of 36.23 Ha or about 3.9%. The value of NDVI analys in East Coast of Central Bangka Regency in 2017 ranges from 0.005 to 0.156. the occurrence of the reduction of mangrove area due to the changing function of mangrove land into settlement, mangrove logging in the utilization of charcoal and mining industry. The addition of the extent of the addition of seedlings along the coast of Kurau and Penyak.

Brightness of nighttime lights has been used as an indicator for spatial disaggregation of CO 2 emission based on an assumed linear relationship between the digital number (DN) values of nighttime light imagery and the amount of CO 2 emissions. However, reliability of the linear relationship of these variables has not been thoroughly examined. In this study we find that the actual overall correlations are exponential rather than linear. More specific analyses showed that the DN values of nighttime light imagery first behaves linearly (from 3 to 50) and then exponentially (from 51 to 63), correlating to the amount of CO 2 emissions. Regardless of the use of a linear or piecewise function, some featured limitations are evident as we developed the methodology. Among significant limitations, CO 2 emissions were not visualized in unlit areas and a lack of variation existed in regions with the same DN values of nighttime light imagery. Lastly CO 2 emissions in urban core areas were grossly under-estimated.

data. Recent developments in unmanned aerial vehicle (UAV) technology provide cost effective and real time applications for site specific data collection. For the mapping of herbage biomass (BM) on a hill pasture, we tested a UAV system with digital cameras (visible and near-infrared (NIR) camera). The field measurements were conducted on the grazing hill pasture at Hanwoo Improvement Office, Seosan City, Chungcheongnam-do Province, Korea on May 17 and June 27, 2014. Plant samples were obtained from 28 sites. A UAV system was used to obtain aerial photos from a height of approximately 50 m (approximately 30 cm spatial resolution). Normalized digital number (DN) values of Red and NIR channels were extracted from the aerial photos and a normalized differential vegetation index using DN (NDVIdn) was calculated. The results show that the correlation coefficient between BM and NDVIdn was 0.88. For the precision management of hilly grazing pastures, UAV monitoring systems can be a quick and cost effective tool to obtain site-specific herbage BM data.

A better understanding the effects of urbanization on vegetation is vital to promoting the sustainable development of cities in China, which has also experienced the world’s most remarkable urbanization during the past decades. This paper used satellite-derived night light, vegetation index, and socio-economic data to analyze spatio-temporal patterns of vegetation changes and evaluate the influence of urbanization on vegetation cover in 46 Chinese metropolises over the last four decades. To examine the effects of urbanization on vegetation and its valid distance, we proposed a new method of comparing three indicators, namely, annual average normalized difference vegetation index (NDVI), long-term trend of NDVI change and the proportion of pixels characterized by significant change in NDVI between built-up areas and its multiple buffer zones. The results showed that the effects of urbanization on vegetation have obvious spatial differences. Urbanization generally resulted in the decrease of vegetation coverage in built-up areas for most of China’s central and eastern metropolises, while in Western China, usually restricted by natural conditions, the urbanization usually improved vegetation coverage. The rates of increase in NDVI were significant lower than that of the buffer zones for most cities in central and eastern China. Urbanized areas of most cities located in central and eastern China had a higher percent of vegetation pixels, representing degradation than that in the buffer zones. The opposite was true for pixels representing vegetation restoration. The changes of NDVI in the cities in Western China were basically contrary to those in central and eastern China. The impacts of urbanization on vegetation are stage-specific. With the continuous progress of urbanization level, the adverse effect of urbanization on vegetation were gradually diminished or even disappeared. Cities with higher nighttime light levels appeared to have less vegetation coverage in the early years, and negative correlations between mean NDVI and the mean digital number value have become weak in more recent years. The changes in NDVI in urbanized areas of most cities significantly followed a ‘U’ shape. This phenomenon may further suggest that positive effects of urbanization become more obvious over time during advanced urbanization. The spatial scope of the impacts of urbanization on vegetation was beyond the urban built-up areas, and the effect intensity appeared to attenuate as the buffer distance became further away from the urbanized area. The valid distance of impacts of urbanization on vegetation is generally about 30–40 km.

Atmospheric correction is an essential process in quantitative remote sensing studies. There is lack of assessment of methods for coastal zone data, particularly historical data. In this study, the performances of existing methods are evaluated, with Jiangsu coastal zone, east China as the study area. Two methods, which are based on image acquisition date and on the maximal and minimal radiances, are used to convert digital number values to at-sensor radiance. The apparent reflectance method, and four image-based dark object subtraction (DOS) methods are used for correction of four historical thematic mapper (TM) images. The performances of these methods are evaluated by employing four assessment procedures. Results show that the method based on image acquisition date is superior to that based on the maximal and minimal radiances. Moreover, DOS-Iteration can achieve better results and can thus be a more reasonable correction model than the other methods.

This study investigated the potential of using remote sensing for mapping and monitoring of submerged aquatic vegetation (SAV) on a large scale. The spectral characteristics of SAV with varied coverage were measured using a portable spectroradiometer on a man-made lake at the Chongming International Wetland Park, Shanghai, China. A good relationship between the coverage of SAV and their field spectral characteristics was established and the reflectance of SAV increased with its increasing coverage. A regression analysis was then carried out between the coverage and the reflectance at the wavelengths of four QuickBird bands. After making an atmospheric correction from a synchronous QuickBird image for the study site, the image digital number (DN) was converted into the ground reflectance. The reflectance image was then deduced into a distribution map of SAV coverage by using the results of the regression functions between the coverage of SAV and the reflectance rate measured in situ . An accuracy assessment indicated that this approach could be used to quickly monitor the distribution and growth situation of SAV. The implications of this observation, in terms of the ability of remote sensing to estimate and monitor the distribution and dynamics of SAV on a large scale are discussed.

Pre-monsoon and post-monsoon surface waterlogged areas were delineated using satellite remote sensing data for Muzaffarpur, Vaishali and Saran districts of North Bihar. Digital data of IRS-1C LISS-III sensor acquired on December 7, 1998 and April 6, 1999 were analyzed using digital image processing software-ERDAS Imagine 8.3.1. The surface waterlogged areas were delineated using modeling technique which is the most advanced and accurate method. Using the modeling technique, a pixel is classified as water if the digital number (DN) value of its Near Infra Red (NIR) band is less than the DN value of the Red band and the Green band, and the Normalized Difference Water Index (NDWI) is greater than or equal to 0.32. The pre-monsoon surface waterlogged areas are found to be 14.02, 23.61 and 9.61 km2 while the post-monsoon surface waterlogged areas are found to be 231.83, 118.19 and 176.06 km2 for Muzaffarpur, Vaishali and Saran districts, respectively. Also, land use/land cover maps were prepared.

It is well known that the surface temperature distribution in the urban area is significantly warmer than its surrounding suburban areas. This phenomenon is known as urban heat island (UHI). Vegetations play a vital role to mitigate the UHI effects especially in regulating high temperature in saturated urban area. This study attempts to assess the effect of vegetation growth on the surface temperature distribution of the surrounding areas. The area of study for this research is Petaling District (i.e. City of Shah Alam, Petaling Jaya and Subang Jaya), Selangor. To monitor the land use/land cover changes within the study areas, Landsat 5 TM images of 1991 and 2009 are used. The land use/land cover of the study area is classified into five major classes i.e. water bodies, high-dense trees, mix-vegetation, built-up area and open land. Based on the thermal band of Landsat 5 TM, the land surface temperature maps are derived. Mono-window algorithm is used to convert digital number to surface temperature. Results from this study have shown that there are significant land use changes within the study area. This study demonstrates that rapid urban growth significantly decreased the vegetated area, hence increased the surface radiant temperature. Although the conversion of green areas to residential and commercial areas significantly increases the land surface temperature (LST), matured trees help to mitigate the effects of UHI.

The aim of this paper is to study the water quality indices and their classifications for irrigation use at many stations along the Euphrates River inside the Iraqi lands and to try to correlate the results with the satellite image analyses for the purpose of making a colored model for the Euphrates that can be used to predict the quality classifications of the river for irrigation use at any point along the river. The Bhargava method was used to calculate the water quality index for irrigation use at sixteen stations along the river from its entrance to the Iraqi land at Al-Qaim in Anbar governorate to its union with the Tigris River at Qurna in Basrah governorate. Coordinates of the sixteen stations of the Euphrates River were projected at the mosaic of Iraq satellite image which was taken from LANDSAT satellite for bands 1, 2 and 3. It was noticed that there was a strong negative correlation between the water quality index and the digital numbers at band 2 for the mosaic satellite image. A regression model was built between the water quality index at December, 2009 and the digital number at band 2 so as to build a colored model which was used to predict a water quality classification for irrigation use at any point along the river.

In 1984, the Shuttle Imaging Radar‐B (SIR‐B) acquired digital data over an inaccessible, heavily‐vegetated and generally cloud‐covered region of the Guiana Shield in northwestern Brazil. Available natural resource information in this area is limited to a reconnaissance survey conducted by the RADAMBRASIL Project at a 1:1,000,000 scale. Classification of this SIR‐B data set is performed using image texture. The algorithm used is the semivariogram textural classifier (STC). This is a deterministic, supervised parallelepiped type classifier which provides the option of combining textural and radiometric information. Textural information is expressed by the semivariogram function. Radiometric information is conveyed by the mean digital number (DN) value. Results have shown that the SIR‐B image representation of each cover type in the study area has a distinctive semivariogram, making feasible the discrimination of vegetation types and water bodies by means of STC.

Normalized difference vegetation index (NDVI) is defined as a ratio of the difference of the infrared and red bands to the sum of the two bands. It can be estimated directly from satellite data, and has been widely used in numerous environmental studies. Yet the satellite-based NDVI was criticized for its variations with temporal factors (e.g. sun-surface-satellite geometry, atmospheric variations). Such variations may result in false change of vegetation over surface. However, the uncertainties relevant to the false change are generally unquantified in the studies. It is therefore unclear to what extent the satellite-based NDVI would be reliable. In this study, we used a derived relationship between the digital number (DN) with and without temporal influences for the same area. Using the derived relationship, NDVI can be expressed as a function of atmospheric optical thickness (AOT), view angle, and DN without temporal influences. As a result, the uncertainties relevant to the temporal factors were quantified with a mathematical expression. We found that satellite-based NDVI was a function of AOT, day of year, latitude, and NDVI without temporal influences. We made simulations in the case of Landsat TM data. Simulations showed that atmospheric effect was most influential to a satellite-based NDVI, and the NDVI would suffer more serious influences at higher latitude than at lower latitude. In general, the temporal influences on NDVI cannot be ignored for a reliable monitoring of surface phenological processes.

Using Terra/MODIS data of 2001, land cover classifications are studied in Kii peninsula based on pattern decomposition method (PDM). The pattern decomposition method is an analysis method developed in multi-dimensional reflectance space and is suitable for mixing analysis and sensor independent analysis. In this method, a digital number in multi-spectral satellite data is expressed by the combination of three standard spectral patterns multiplied by three decomposition coefficients. Use these three coefficients and vegetation index based on PDM (VIPD), land objects are classified and it is found that 76.8% of Kii peninsula is vegetation and is nearly same results as the values obtained using landsat/TM data of 1995. Our final goal of this study is to estimate net primary production (NPP) of vegetation in Kii peninsula using the results of land cover classification and to study annual change of land cover and NPP.

Medium Resolution NDVI(Normalized Difference Vegetation Index) from different sensor systems such as Landsat, SPOT, ASTER, CBERS and HJ-1A/1B satellites provide detailed spatial information for studies of ecosystems, vegetation biophysics, and land cover. Limitation of sensor designs, cloud contamination, and sensor failure highlighted the need to normalize and integrate NDVI from multiple sensor system in order to create a consistent, long-term NDVI data set. In this paper, we used a reference-based method for NDVI normalization. And present an application of this approach which covert Landsat ETM+ NDVI calculated by digital number (NDVIDN) to NDVI calculated by surface reflectance (NDVISR) using MODIS products as reference, and different cluster was treated differently. Result shows that this approach can produce NDVI with highly agreement to NDVI calculated by surface reflectance from physical approaches based on 6S (Second Simulation of the satellite Signal in the Solar Spectrum). Although some variability exists, the cluster specified reference based approach shows considerable potential for NDVI normalization. Therefore, NDVI products in MODIS era from different sources can be combined for time-series analysis, biophysical parameter retrievals, and other downstream analysis.

The phenomenon of black water aggregation (BWA) occurs in inland water when massive algal bodies aggregate, die, and react with the toxic sludge in certain climate conditions to deprive the water of oxygen. This process results in the deterioration of water quality and damage to the ecosystem. Because charge coupled device (CCD) camera data from the Chinese HJ environmental satellite shows high potential in monitoring BWA, we acquired four HJ-CCD images of Taihu Lake captured during 2009 to 2011 to study this phenomenon. The first study site was selected near the Shore of Taihu Lake. We pre-processed the HJ-CCD images and analyzed the digital number (DN) gray values in the research area and in typical BWA areas. The results show that the DN values of visible bands in BWA areas are obviously lower than those in the research areas. Moreover, we developed an empirical retrieving algorithm of BWA based on the DN mean values and variances of research areas. Finally, we tested the accuracy of this empirical algorithm. The retrieving accuracies were89.9%, 58.1%, 73.4%, and 85.5%, respectively, which demonstrates the efficiency of empirical algorithm in retrieving the approximate distributions of BWA.

Our AVHRR volcano and fire data are in agreement with Setzer and Verstraete, showing that when channel 3 saturates the digital number (DN) recorded is greater than zero. The radiance at which this occurs varies within a single radiometer, and from platform to platform. It appears that non-zero saturation has affected all the thermal channels on all AVHRR instruments to date and may be due to the design of the analogue-to-digital converter circuits. Since the exact relation between non-zero DN and spectral radiance is not known, we suggest that all DN over high temperature targets that arc less than or equal to the non-zero saturation DN cannot be trusted to be reliable measurements of spectral radiance.

In this paper, we present a method of earthquake damage detection by comparing the optical images with panchromatic bands for the Gujarat, India earthquake, which occurred on January 26, 2001. The data used in this study are optical remote sensing images taken by Landsat-7 satellite on January 8 and February 29, 2001, before and after the earthquake. We have investigated the pre and post-earthquake satellite images calculating the differences in the reflection intensity (digital number) of the two images. The estimated affected area has been subtracted on a pixel unit based on the obtained frequency distributions of the differences in the optical sensor values, which show significant changes in the reflectance due to the earthquake disaster. We have investigated the accuracy of our analysis result using a classification method for the training areas with aerial photographs taken after the earthquake. The two damage detection methods show a very similar result.