Advanced Visible And Near Infrared Radiometer Type 2 Research Articles

Assessment of mangrove cover dynamics and its health status in the Gulf of Khambhat, Western India, using high-resolution multi-temporal satellite data and Google Earth Engine.

Anthropogenic activity is a major driving factor of greenhouse gas emission, leading to climate change worldwide. So, the best natural approach to lowering the carbon from the atmosphere is mangroves which have more potential to sequestrate carbon. But mangroves are under threat due to land use land cover change. This research has been carried out on the mangroves of Gulf of Khambhat, Gujarat, India, where anthropic activity is affecting the mangrove forest cover with spatiotemporal heterogeneity. In the present study, multi-temporal high-resolution satellite data AVNIR-2 (Advanced Visible and Near Infrared Radiometer type-2) and LISS-4 (Linear Imaging Self-Scanning Sensors-4) were used for the demarcation of various land use/land cover class (LULC), and change analysis and assessment of mangroves health for the years 2009, 2014, and 2019. The impact of saltpan/aquaculture on mangroves growth and its health status has been calculated by various MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data products such as gross primary productivity (GPP), enhanced vegetation index (EVI), and leaf area index (LAI) in Google Earth Engine (GEE), and field-based method was also considered. This study suggests that there is a marginal increase (17.11 km2) in mangrove cover during the assessment period 2009-2019; on other side, 65.42 km2 was degraded also. However, increase in saltpan/aquaculture is imposing an adverse effect on mangroves' basal area, plant density, and productivity. Change analysis also suggests a reduction in healthy mangrove area (from 25.20 to 2.84 km2), which will have an impact on ecosystem services.

Artificial neural networks on integrated multispectral and SAR data for high-performance prediction of eucalyptus biomass

Biomass estimation plays an important role in forest management being applied in most carbon sequestration studies, assessment of forest succession, conservation of natural resources, quantification of nutrient cycling, energy planning where forest biomass is used as primary fuel for power generation and harvest planning and stock management in pulp industry. Using data from Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) and Phased Array type L-band Synthetic Aperture Radar (PALSAR) sensors onboard Advanced Land Observing Satellite (ALOS), above-ground biomass (AGB) estimates were generated via artificial neural networks for a eucalyptus planting area in Minas Gerais State, Brazil. With 206 inventory plots, computed coefficient of determination between AGB estimates and observed values within validation sample was 0.95. Relative root mean square error was 2.87% with errors ranging from −8% to 4%. These results demonstrated artificial neural networks higher performance in modeling eucalyptus biomass based on Multispectral and SAR data over previous study, in which multiple linear regression method was applied in the same dataset, achieving R2 equal to 0.71.

Combining ALOS PALSAR and AVNIR-2 data for effective land use/land cover classification in Jharia coalfields region

ABSTRACTThere are various classification techniques available which produce desired results. However, some of the land use/land cover (LU/LC) classes are not discernible in such classifications. The present study attempts for improving LU/LC classification accuracy by applying data fusion techniques. For this, we considered combinations of: (1) Synthetic Aperture Radar (SAR) multi-looked intensity and optical, (2) backscatter with optical and (3) terrain corrected backscatter with optical data. The fusion of terrain corrected backscatter with optical has been considered in this study to negate the effect of topographic undulations on backscatter. The classification accuracy for combinations of cross-polarised terrain corrected backscatter data with Advanced Visible and Near-Infrared Radiometer type 2 (AVNIR-2) (90.33%), co-polarised terrain corrected backscatter data with AVNIR-2 (89.66%), cross-polarised backscatter data with AVNIR-2 (89.0%) and cross-polarised multi-look intensity with AVNIR-2 (87.0%) were found to be better than classified outputs of AVNIR-2 data alone (84.6%), combinations of co-polarised backscatter and AVNIR-2 data (82.7%) and co-polarised multi-look intensity with AVNIR-2 data (80.1%), and combinations of multi-date terrain corrected backscatter (80.66%), multi-date co-polarised backscatter (80.0%) and multi-date co-polarised multi-look intensity (79.0%). The highest accuracy achieved in LU/LC classification is with cross-polarised terrain corrected backscatter with AVNIR-2 (90.33%) data. Data fusion techniques can be an alternative for LU/LC classification.

Landslide detection using ALOS optical and radar data. A case study from the Ilia prefecture.

In this study the usefulness of the ALOS optical and radar data for landslide monitoring is examined. ALOS contains three sensors, commonly referred to as the “three eyes” of ALOS. These sensors are: the Panchromatic Remote-Sensing Instrument for Stereo Mapping (PRISM), the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2), and the Phased Array type L-band Synthetic Aperture Radar (PALSAR). The area of study is located in a small village named Sykies near to the city of Andritsena in Western Peloponnese. The area suffered during the last years from enormous fires. As a result many landslides have been recorded. One of the latest Landslides has been recorded on January 2009 as a consequence of heavy rains. That landslide was mapped in situ using differential GPS. The possibility of detecting and mapping the specific landslide using ALOS data is examined in this study and the results are presented. Thirty ALOS radar images within a period of three years, two ALOS Prism data sets and two ALOS AVNIR collected over the same area within a year were used.

Application of ALOS AVNIR-2 for the detection of seaweed and seagrass beds on the northeast of Brazil

ABSTRACTSeaweed and seagrass beds play a multiplicity of functions within ecosystems, and have both ecological and economical value. However, anthropogenic activities, as well as climate change have contributed on the degradation. This study used data from Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer Type 2 (AVNIR-2) orbital images to detect seaweeds and seagrasses. Qualitative and quantitative samples were used to validate the images, classified using the kappa coefficient (κ). The supervised classifications performed by the algorithms fuzzy logic – Fuzclass, maximum likelihood – Maxlike, minimum distance to means – Mindist, parallelepiped – Piped, showed an accuracy level of 0.93, 0.84, 0.83 (both excellent), and 0.62 (substantial), respectively. The results of the hard classifiers (Maxlike, Mindist, and Piped) submitted a new classification based on fuzzy logic (Fuzclass) demonstrated accuracy level of 0.74, 0.61 (both substantial), and 0.50 (moderate), respectively. Considered superior to the others, the Fuzclass classifier exhibited the best tendency in representing reef bottom–type distribution. Maxlike generated a map of seaweed and seagrass spatial distribution and abundance of the Maracajaú reef, identifying seven classes: (1) dense seaweeds; (2) sand; (3) dense seagrasses; (4) sparse seagrasses; (5) calcareous seaweeds; (6) sparse seaweeds; and (7) fine sand. The map of Maracajaú reef bottom type showed that it was possible to apply image processing and digital classification methodologies to distinguish submerged organisms, revealing information to help in planning and management of these ecosystems, enabling future monitoring.

Analysis of Water Quality of Lake Hachiroko in Japan Using a Fuzzy Multiple Regression Model with ALOS AVNIR-2 Data

The distribution of water pollution is often assessed by remote sensing. In this study, we develop a fuzzy multiple regression model and analyze water quality using data collected by the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) of the Advanced Land Observing Satellite at different time points. We conduct a fuzzy multiple regression analysis of the AVNIR-2 data and direct measurements of the local water quality of Lake Hachiroko in Japan. The relationship between the AVNIR-2 and water quality data are analyzed by solving both min and max problems. We compare the estimated water quality maps with the actual distributions in the study area, and determine that the proposed method enables us to derive water quality conditions effectively from the AVNIR-2 data. Furthermore, by comparing maps created using AVNIR-2 data collected at different times, we obtain results revealing temporal changes in water quality. In addition, we compare maps created using the fuzzy multiple regression and fuzzy regression models. We demonstrate that the former offers a greater number of solutions and provides more details about water quality.

Classification of Land Use on Sand-Dune Topography by Object-Based Analysis, Digital Photogrammetry, and GIS Analysis in the Horqin Sandy Land, China

Previous field research on the Horqin Sandy Land (China), which has suffered from severe desertification during recent decades, revealed how land use on a sand-dune topography affects both land degradation and restoration. This study aimed to depict the spatial distribution of local land use in order to shed more light on previous field findings regarding policies on a broader scale. We performed the following analyses with Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) images of Advanced Land Observing Satellite (ALOS): (1) object-based classification to discriminate preliminary classification of land-use types that were approximately differentiated by ordinary pixel-based analysis with spectral information; (2) digital photogrammetry to generate a digital surface model (DSM) with adequately high accuracy to represent undulating sand-dune topography; (3) geographic information system (GIS) analysis to classify major topographic types with the digital surface model (DSM); and (4) overlay of the two classification results to depict the local land-use types. The overall accuracies of the object-based and GIS-based classifications were high, at 93% (kappa statistic: 0.84) and 89% (kappa statistic: 0.81), respectively. The resultant local land-use map represents areas covered in previous field studies, showing where and how land degradation and restoration are likely to occur. This research can contribute to future environmental surveys, models, and policies in the study area.

Urban landscape extraction and analysis based on optical and microwave ALOS satellite data

Accurate mapping of urban land cover from satellite data provides essential input to urban landscape analysis, modelling and urban ecosystem studies. Additionally, analysis of urban landscape metrics will provide a positive step towards comprehensive understanding of the features of urban landscape structure and further planning. In the present study, multi-spectral Advanced Land Observing Satellite (ALOS)/Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) images and ALOS/Phased Array type L-band Synthetic Aperture Radar (PALSAR) dual-polarized (FBD) microwave images were used to extract urban land cover information by applying the decision tree method, and additional Advanced Space borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER/GDEM) was used to reduce the effects of mountains in Synthetic Aperture Radar (SAR) images due to high backscattering from urban construction land. A set of landscape metrics, such as landscape diversity, edge density and landscape shape indices with supplementary ecological meanings, were chosen to quantitatively analysis urban landscape patterns in arid environments. The overall accuracy assessment result was 91.50%, and the experimental results demonstrate that synergetic use of optical and SAR ALOS data has the potential and advantages for Arid Urban Region mapping, while the decision tree method showed intuitive simplicity and computational efficiency. The quantitative analysis results of landscape metrics showed that distribution of landscape types in Urumqi city were inhomogeneous, the urban landscape dominated by a few classes. Urbanization in this region has resulted in dramatic increases in patch density (PD), edge density (ED) and landscape shape complexity.

Identification and Seasonal Analysis of Degraded Tropical Peatland by Using ALOS AVNIR-2 Data

Tropical peatlands are being subjected to the consequences of rapid economic development without any consideration of the importance of sustainable management practices. Sustainable management of tropical peatlands is an important element in controlling carbon emission. However, the available information of tropical peatlands lacks of accuracy and is outdated, especially in terms of medium to high resolution. Thus, development of reliable monitoring techniques is a significant step towards the sustainable management of tropical peatlands. The remote sensing (RS) application is suitable as a tool to monitor tropical peatlands, whereas direct measurements are generally labor-intensive, time-consuming and limited to accessibility. In this study, methodology to identify degraded tropical peatland was developed by using the McFeeters Normalized Difference Water Index (McFeeters-NDWI), which was derived by Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) data. Additionally, a seasonal analysis was carried out to examine the characteristics of degraded tropical peatland during the rainy and dry seasons from the viewpoint of the medium to high resolution of optical RS. Overall, a relationship was discovered such that the wet shrub class was considered as the degraded tropical peatland area, and was identified as being in between -0.43 to -0.11 of the McFeeters-NDWI value. The wet-shrub class yielded a producer's accuracy of 80.6% and a user's accuracy of 91.8%. Afterwards, the seasonal change was discovered to slightly shift the threshold values (TrVs) in the identification of degraded tropical peatland by as much as -0.05. However, the interval of the TrVs for the wet shrub class was stable and remained unchanged.

Identification of the glaciers and mountain naturally dammed lakes in the Pskem, the Kashkadarya and the Surhandarya River basins, Uzbekistan, using ALOS satellite data

The glacierized area of Uzbekistan is represented in three river basins – the Pskem, the Kashkadarya and the Surhandarya. This study considers the present state of the glaciers and high-mountain lakes distribution in this area based on the analysis and validation of advanced land observing satellite (ALOS)/advanced visible and near infrared radiometer type 2 (AVNIR-2) satellite data. Between the 1960s and the 2010s, the glacierized area decreased by 23% in the Pskem River basin (including the Maydantal), by 49% in the Kashkadarya and by 40% in the Surhandarya (including the Sangardak and the Tupalang) River basins. The retreat fairly slowed in the 1980s–2010s. There are 75 glacial lakes and 35 rock-dammed lakes (including landslide-dammed ones) in the Pskem River basin, 45% of all the lakes covering the area less than 0.002 km2; 13 glacial lakes and 4 rock-dammed lakes in the Kashkadarya and 34 glacial lakes and 16 rock-dammed lakes in the Surhandarya River basins. The landslide rock-dammed Ikhnach Upper Lake lost 0.04 km2 in size from 1 August 2010 to 30 August 2010 because of the seepage through the rock dam and 0.10 km2 from 1 August to 18 October 2013.

Effect of surface BRDF of various land cover types on geostationary observations of tropospheric NO<sub>2</sub>

Abstract. We investigated the effect of surface reflectance anisotropy, bidirectional reflectance distribution function (BRDF), on satellite retrievals of tropospheric NO2. We assume the geometry of geostationary measurements over Tokyo, which is one of the worst air-polluted regions in East Asia. We calculated air mass factors (AMF) and box AMFs (BAMF) for tropospheric NO2 to evaluate the effect of BRDF by using the radiative transfer model SCIATRAN. To model the BRDF effect, we utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) products (MOD43B1 and MOD43B2), which provide three coefficients to express the RossThick–LiSparse reciprocal model, a semi-empirical and kernel-based model of BRDF. Because BRDF depends on the land cover type, we also utilized the High Resolution Land-Use and Land-Cover Map of the Advanced Land Observing Satellite (ALOS)/Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2), which classifies the ground pixels over Tokyo into six main types: water, urban, paddy, crop, deciduous forest, and evergreen forest. We first develop an empirical model of the three BRDF coefficients for each land cover type over Tokyo and then apply the model to the calculation of land-cover-type-dependent AMFs and BAMFs. Results show that the variability of AMF among the land types is up to several tens of percent, and if we neglect the reflectance anisotropy, the difference with AMFs based on BRDF reaches 10% or more. The evaluation of the BAMFs calculated shows that not considering BRDF will cause large errors if the concentration of NO2 is high close to the surface, although the importance of BRDF for AMFs decreases for large aerosol optical depth (AOD).

Water Quality Analysis of Lake Hachiroko, Japan, using ALOS PALSAR Data

In recent years, remote sensing data have been used for water quality analyses. Such data have favorable features, including instantaneousness, wide area of coverage and periodicity. In our previous studies, we analyzed the data collected from an Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) optical sensor of the Advanced Land Observing Satellite (ALOS), and an active sensor of JERS-1 Synthetic Aperture Radar (SAR), using a fuzzy regression model with one input and one output. In this paper, to examine the usefulness of the fuzzy regression model for a water quality analysis, data from ALOS Phased Array type L-band Synthetic Aperture Radar (PALSAR) are used. A fuzzy regression analysis was performed using the Min and Max problems, and through fuzzy-level slice processing, maps of the estimated water quality were obtained from the ALSO PALSAR data. Furthermore, by comparing the results obtained using data collected from both ALOS AVNIR-2 and JERS-1 SAR, we examine the differences in the classification resulting from the differences in the sensors and resolutions.

Time series analysis of discolored seawater reflectance observed by Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) at Fukutoku-Okonaba submarine volcano, Japan

Monitoring submarine volcanoes is not an easy task compared to land volcanoes because they are covered by seawater and located in remote areas. Satellite remote sensing is a powerful tool for monitoring underwater volcanic activities such as discolored seawater, floating material and volcanic plumes. Discolored seawater is a good indicator of submarine volcanic activities. Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) made extensive observations from 2006 to 2011 of the Fukutoku-Okanoba submarine volcano, which is located 1300km south of Tokyo, and is one of the most active submarine volcanoes in Japan. The high discolored seawater brightness coincides with relatively high activity of Fukutoku-Okanoba. No discolored seawater was observed for 6months before the 2010 Fukutoku-Okanoba submarine eruption, meaning that Fukutoku-Okanoba was quiescent before the eruption. Both high brightness and apparent color change of discolored seawater derived from AVNIR-2 mean emergence of large amount of hot spring water, implying that the submarine volcano is highly active. This study demonstrates that satellite remote sensing is an effective tool for monitoring activities of inaccessible submarine volcanoes.

Water quality analysis in Lake Hachiroko, Japan, using ALOS AVNIR‐2 data

AbstractIn recent years, remote sensing has been used to assessing water pollution distribution. In this study, water quality is analyzed using data collected by the Advanced Visible and Near Infrared Radiometer type‐2 (AVNIR‐2) of the Advanced Land Observing Satellite (ALOS) at various points in time. We carried out fuzzy regression analysis of the AVNIR‐2 data and direct measurements of local water quality. The relationship between the water quality data and the AVNIR‐2 data was analyzed by solving both the min and max problems. By comparing the maps of estimated water quality with actual distributions of water quality in the study area, we found that the method used in this study allows effective derivation of water quality conditions from AVNIR‐2 data, which provides 10‐m spatial resolution. Furthermore, by comparing the maps created using AVNIR‐2 data collected at different times, we obtained results suggesting temporal changes in water quality. We also compared the results obtained using data collected by the optical sensor of the Landsat thematic mapper (TM) with 30‐m resolution and those obtained using data collected by the active sensor of JERS‐1 synthetic aperture radar (SAR), and examined the differences in classification results resulting from differences in resolution and sensors. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Tree Diversity Detection with Mid-Resolution Images and Environmental Data in a Neural Network

Biodiversity maps are crucial to conservation management. The present study assesses the accuracy of detecting tree diversity in an Italian forest site by combining mid-resolution images from Landsat-TM or Advanced Land Observation Satellite (ALOS)’s Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) sensors with environmental data namely elevation, slope, aspect and solar radiation in an artificial Neural Network (NN) classifier. The map accuracies obtained for Landsat-TM and ALOS images are 60 % and 53 % respectively. Use of environmental data increases accuracies to 91 % and 81 % respectively. Landsat-TM detects tree diversity more accurately than ALOS. Both the coarser pixel size and finer spectral resolution of Landsat-TM contributed to its higher accuracy.

Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa

The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset.

Tropical forest mapping using a combination of optical and microwave data of ALOS

It is difficult to monitor forests in tropical regions with frequent cloud cover using optical remote-sensing data. Adequate multi-temporal, high-resolution imagery is often not available. Microwave imagery is able to penetrate cloud cover, enabling imagery of the land surface to be recorded more frequently. This study seeks to improve tropical forest mapping by combining optical and microwave imagery, with one of the main objectives being the discrimination of planted and natural forests. First, multi-spectral Advanced Land Observing Satellite (ALOS)/Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) images were used to create a forest and land-cover classification of the study area. Subsequently, ALOS/Phased Array type L-band Synthetic Aperture Radar (PALSAR) single-polarized and dual-polarized microwave images were used to generate forest and land-cover masks to be used in combination with the ALOS/AVNIR-2 classification. The overall accuracy of the ALOS/AVNIR-2 classification was 77%. When the ALOS/PALSAR masks were used in combination with the ALOS/AVNIR-2 classification, the overall accuracy increased to 88% with higher than 90% accuracy for the main forest classes.

Feature analysis of groundwater discharge points in coastal regions around Mt. Chokaisan using ALOS AVNIR‐2 Data

AbstractThe flow of fresh water into the sea, called submarine groundwater discharge, is a key factor for understanding the hydrologic cycle in both sea and land regions. The numerous positions from which fresh water is discharged and the variations in its quantity impede the understanding of its properties. In a previous study, we detected groundwater discharge points expressed by differences in fresh water and seawater by using multispectral Landsat Enhanced Thematic Mapper Plus (ETM+) signals with 30‐m spatial resolution. Details of seawater surface information regarding the submarine groundwater discharge have not yet been obtained. Therefore, this paper analyzes the features of water properties around groundwater discharge points by using Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer type 2 (AVNIR‐2) outputs, which provide 10‐m spatial resolution. Classification maps obtained by k‐means clustering are compared with measurements in ground surveys, geologic maps, and estimation maps of relative salinity. The experimental results obtained from the AVNIR‐2 data are in considerable agreement with the realities in the study area. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(7): 1–9, 2012; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.11398

Improved Biomass Estimation Using the Texture Parameters of Two High-Resolution Optical Sensors

Accurate forest biomass estimation is essential for greenhouse gas inventories, terrestrial carbon accounting, and climate change modeling studies. Unfortunately, no universal and transferable technique has been developed so far to quantify biomass carbon sources and sinks over large areas because of the environmental, topographic, and biophysical complexity of forest ecosystems. Among the remote sensing techniques tested, the use of multisensors and the spatial as well as the spectral characteristics of the data have demonstrated a strong potential for forest biomass estimation. However, the use of multisensor data accompanied by spatial data processing has not been fully investigated because of the unavailability of appropriate data sets and the complexity of image processing techniques in combining multisensor data with the analysis of the spatial characteristics. This paper investigates the texture parameters of two high-resolution (10 m) optical sensors (Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) and SPOT-5) in different processing combinations for biomass estimation. Multiple regression models are developed between image parameters extracted from the different stages of image processing and the biomass of 50 field plots, which was estimated using a newly developed “allometric model” for the study region. The results demonstrate a clear improvement in biomass estimation using the texture parameters of a single sensor ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.854$</tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$ \hbox{rmse} = 38.54$</tex></formula> ) compared to the best result obtained from simple spectral reflectance <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(r^{2} = 0.494)$</tex></formula> and simple spectral band ratios <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(r^{2} = 0.59)$</tex></formula> . This was further improved to obtain a very promising result using the texture parameter of both sensors together ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.897$</tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{rmse} = 32.38$</tex></formula> ), the texture parameters from the principal component analysis of both sensors ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.851$ </tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{rmse} = 38.80$</tex></formula> ), and the texture parameters from the averaging of both sensors ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.911$</tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{rmse} = 30.10$</tex></formula> ). Improvement was also observed using the simple ratio of the texture parameters of AVNIR-2 ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.899$</tex> </formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{rmse} = 32.04$</tex></formula> ) and SPOT-5 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(r^{2} = 0.916)$</tex></formula> , and finally, the most promising result ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.939$</tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$ \hbox{rmse} = 24.77$</tex></formula> ) was achieved using the ratios of the texture parameters of both sensors together. This high level of agreement between the field and image data derived from the two novel techniques (i.e., combination/fusion of the multisensor data and the ratio of the texture parameters) is a very significant improvement over previous work where agreement not exceeding <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r^{2} = 0.65$</tex> </formula> has been achieved using optical sensors. Furthermore, biomass estimates of up to 500 t/ha in our study area far exceed the saturation levels observed in other studies using optical sensors.

Change detection of an earthquake-induced barrier lake based on remote sensing image classification

The earthquake-induced barrier lakes that are caused by landslides are at risk of bursting after excessive rainfall, and they are a serious threat to the surrounding residents. This paper proposes a change detection approach of the barrier lake using the method of post-classification comparison combined with background subtraction. The case studied in the paper is the detection of the Tangjiashan barrier lake, which is one of the largest lakes induced by the Wenchuan Earthquake on 12 May 2008. Both the pre- (31 March 2007) and post-earthquake (16 May 2008) images of the Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer type 2 (AVINER-2) were collected in the paper. The results show that the river widened through the Zhangping area, increasing from 90 m pre-earthquake to 545 m post-earthquake, and the surface area of the barrier lake increased from 0.945 km2 to 1.471 km2.