Possibility investigation of tree diversity mapping using Landsat ETM+ data in the Hyrcanian forests of Iran

Lack of data often limits understanding and management of biodiversity in forested areas. Remote sensing imagery has considerable potential to aid in the monitoring and prediction of biodiversity across many spatial and temporal scales. In this paper, we explored the possibility of defining relationships between species diversity indices and Landsat ETM+ reflectance values for Hyrcanian forests in Golestan province of Iran. We used the COST model for atmospheric correction of the imagery. Linear regression models were implemented to predict measures of biodiversity (species richness and reciprocal of Simpson indices) using various combinations of Landsat spectral data. Species richness was modeled using the band set ETM5, ETM7, DVI, wetness and variances of ETM1, ETM2 and ETM5 (adjusted R² = 0.59, RMSE = 1.51). Reciprocal of Simpson index was modeled using the band set NDVI, brightness, greenness, variances of ETM2, ETM5 and ETM7 (adjusted R² = 0.459 RMSE = 1.15). The results demonstrated that spectral reflectance from Landsat can be used to effectively model tree species diversity. Predictive map derived from the presented methodology can help evaluate spatial aspects and monitor tree species diversity of the studied forest. The methodology also facilitates the evaluation of forest management and conservation strategies in northern Iran.     

Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data

Integration of multisensor data provides the opportunity to explore benefits emanating from different data sources. A fusion between fraction images derived from spectral mixture analysis of Landsat-7 ETM+ and phased array L-band synthetic aperture radar (PALSAR) is introduced. The aim of this fusion is to improve the estimation accuracy of above-ground biomass (AGB) in lowland mixed dipterocarp forest. Spectral mixture analysis was applied to decompose a mixture of spectral components of Landsat-7 ETM+ into vegetation, soil, and shade fractions. These fraction images were integrated with PALSAR data using the discrete wavelet transform (DWT) and Brovey transform. As a comparison, spectral reflectance of Landsat-7 ETM+ was fused directly with PALSAR data. Backscatter of horizontal–horizontal and horizontal–vertical polarizations was also used to estimate AGB. Forest inventory was carried out in 77 randomly distributed plots, the data being used for either model development or validation. A local allometric equation was applied to calculate AGB per plot. Regression models were developed by integrating field measurements of 50 sample plots with remotely sensed data, e.g. fraction images, reflectance of Landsat-7 ETM+, and PALSAR data. The models developed were validated using 27 independent sample plots. The results showed that not all fused images significantly improved the accuracy of AGB estimation. The model based on Brovey transform using the reflectance of Landsat-7ETM+ and PALSAR produced an R² of only 0.03–0.10. By contrast, fusion between PALSAR data and fraction images using Brovey transform improved the accuracy of R² to 0.33–0.46. Further improvement in the accuracy of estimating AGB was observed when DWT was applied to integrate PALSAR with the reflectance of Landsat-7ETM+ (R² = 0.69–0.72) and PALSAR with fraction images (R² = 0.70–0.75).     

Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga,South Africa

The leaf area index (LAI) is the key biophysical indicator used to assess the condition of rangeland. In this study, we investigated the implications of narrow spectral response, high radiometric resolution (12 bits), and higher signal-to-noise ratio of the Landsat 8 Operational Land Imager (OLI) sensor for the estimation of LAI. The Landsat 8 LAI estimates were compared to that of its predecessors, namely Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (8 bits). Furthermore, we compared the radiative transfer model (RTM) and spectral indices approaches for estimating LAI on rangeland systems in South Africa. The RTM was inverted using artificial neural network (ANN) and lookup table (LUT) algorithms. The accuracy of the models was higher for Landsat 8 OLI, where ANN (root mean squared error, RMSE = 0. 13; R² = 0. 89), LUT (RMSE = 0. 25; R² = 0. 50), compared to Landsat 7 ETM+, where ANN (RMSE = 0. 35; R² = 0. 60), LUT (RMSE = 0. 38; R² = 0. 50). Compared to an empirical approach, the RTM provided higher accuracy. In conclusion, Landsat 8 OLI provides an improvement for the estimation of LAI over Landsat 7 ETM+. This is useful for rangeland monitoring.     

Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia

This article demonstrates some techniques for studying the age of oil palm trees (Elaeis guineensis Jacq.) using the Disaster Monitoring Constellation 2 from the UK (UK-DMC 2) and Advanced Land Observing Satellite phased array L-band synthetic aperture radar (ALOS PALSAR) remote-sensing data at a private oil palm estate in southern peninsular Malaysia. Several techniques were explored with UK-DMC 2 data, namely (1) radiance, vegetation indices, and fraction of shadow; (2) texture measurement; (3) classifications, namely Iterative Self-Organizing Data Analysis Technique (ISODATA) classification, maximum-likelihood classification (MLC), and random forest (RF) classification; (4) in terms of ALOS PALSAR data, the correlation of polarizations (i.e. horizontal transmitting and horizontal receiving (termed HH polarization) and horizontal transmitting and vertical receiving (termed HV polarization)) and the ratio of these polarizations to the age of oil palm trees. From the results, band 1 (near-infrared) of UK-DMC 2, fraction of shadow, and mean filter from the grey-level co-occurrence matrix (GLCM) demonstrated strong correlation of determination (R ²?=?0.76–0.80) with the age of oil palm trees, while the ALOS PALSAR HH polarization could correlate moderately strongly (R ²?=?0.49) with the age of oil palm trees. Adding fraction of shadow and UK-DMC 2 data using the RF method further improved the overall accuracy of age classification from 45.3% (MLC method) to 52.9%. This study concluded that texture measurement (GLCM mean) and fraction of shadow are useful for studying the age of oil palm trees, although discriminating variation in age between mature oil palm trees is difficult because the leaf area index development of mature oil palm trees stabilizes at about 10 years of age. Future studies should involve height information, because this has the potential to be used as one of the most important variables for studying the age of oil palm trees.     

Defining the Landsat TM/ETM+ and CHRIS/PROBA spectral regions in which turbidity can be retrieved in inland waterbodies using field spectroscopy

This article describes the results obtained by an existing campaign in which in situ spectroradiometric measurements using a GER1500 field spectroradiometer, Secchi disk depth, and turbidity measurements (using a portable turbidity meter) were acquired at Asprokremmos Reservoir in Paphos District, Cyprus. Field spectroradiometric and water quality data span 18 sampling campaigns during the period May 2010–October 2010. By applying several regression analyses between ‘In-Band’ mean reflectance values against turbidity values for all spectral bands corresponding to Landsat TM/ETM+ (Bands 1 to 4) and CHRIS/PROBA (Bands A1 to A62), the highest correlation was found for Landsat TM/ETM+ Band 3 (R² = 0.85) and for CHRIS/PROBA Bands A30 to A32 (R² = 0.90).     

The application of a binary division procedure to the classification of forest subcategories using MODIS time-series data during 2000–2010 in China

Forests account for more than 23% of China’s total area. As the most important terrestrial ecosystem, forests have tremendous ecological value. However, it remains difficult to classify forest subcategories at the national scale. In this study, a newly developed binary division procedure was used to categorize forest areas, including their spatiotemporal dynamics, during the period 2000–2010. Time-series images acquired using the Moderate Resolution Imaging Spectroradiometer (MODIS), together with auxiliary data on land use, climate zoning, and topography, were utilized. Hierarchical classification and zoning were combined with remote-sensing auto-classification. Based on the forest extent mask, the state-level forest system was divided into four classes and 18 subcategories. The method achieved an acceptable overall accuracy of 73.1%, based on a comparison to the sample points of China’s fourth forest general survey data set. In 2010, the total forest area was 1.755 × 10⁶ km², and the total area of and shrubs was 4.885 × 10⁵ km². The total area of woodland increased by 2536.25 km² during the decade 2000–2010. The shrub subcategories exhibited almost no change during this time period; however, significant changes in forest area occurred in the mountainous region of Northeast China as well as in the hilly regions of Southern China. The main transformations took place in cold-temperate and temperate mountainous deciduous coniferous forest, subtropical deciduous coniferous forest, subtropical evergreen coniferous forest, and temperate and subtropical deciduous broadleaved mixed forests. The binary division procedure proposed herein can be used not only to rapidly classify more forest subcategories and monitor their dynamic changes, but also to improve the classification accuracy compared with global and national land-cover maps.     

Predicting Eucalyptus spp. stand volume in Zululand,South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets

Accurate, reliable, and up-to-date forest stand volume information is a prerequisite for a detailed evaluation of commercial forest resources and their sustainable management. Commercial forest responses to global climate change remain uncertain, and hence the mapping of stand volume as carbon sinks is fundamentally important in understanding the role of forests in stabilizing climate change effects. The aim of this study was to examine the utility of stochastic gradient boosting (SGB) and multi-source data to predict stand volume of a Eucalyptus plantation in South Africa. The SGB ensemble, random forest (RF), and stepwise multiple-linear regression (SMLR) were used to predict Eucalyptus stand volume and other related tree-structural attributes such as mean tree height and mean diameter at breast height (DBH). Multi-source data consisted of SPOT-5 raw spectral features (four bands), 14 spectral vegetation indices, rainfall data, and stand age. When all variables were used, the SGB algorithm showed that stand volume can be accurately estimated (R² = 0.78 and RMSE = 33.16 m³ ha^?1 (23.01% of the mean)). The competing RF ensemble produced an R² value of 0.76 and a RMSE value of 37.28 m³ ha^?1 (38.28% of the mean). SMLR on the other hand, produced an R² value of 0.65 and an RMSE value of 42.50 m³ ha^?1 (42.50% of the mean). Our study further showed that Eucalyptus mean tree height (R² = 0.83 and RMSE = 1.63 m (9.08% of the mean)) and mean diameter at breast height (R² = 0.74 and RMSE = 1.06 (7.89% of the mean)) can also be reasonably predicted using SGB and multi-source data. Furthermore, when the most important SGB model-selected variables were used for prediction, the predictive accuracies improved significantly for mean DBH (R² = 0.81 and RMSE = 1.21 cm (6.12% of the mean)), mean tree height (R² = 0.86 and RMSE = 1.39 m (7.02% of the mean)), and stand volume (R² = 0.83 and RMSE = 29.58 m³ ha^?1 (17.63% of the mean)). These results underscore the importance of integrating multi-source data with remotely sensed data for predicting Eucalyptus stand volume and related tree-structural attributes.     

Estimating montane forest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data

In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. Estimates of AGB are relevant for sustainable forest management, monitoring global change, and carbon accounting. This is particularly true for the Qilian Mountains, which are a water resource protection zone. We combined forest inventory data from 133 plots with TM images and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) V2 products (GDEM) in order to analyse the influence of the sun-canopy-sensor plus C (SCS+C) topographic correction on estimations of forest AGB using the stepwise multiple linear regression (SMLR) and k-nearest neighbour (k-NN) methods. For both methods, our results indicated that the SCS+C correction was necessary for getting more reliable forest AGB estimates within this complex terrain. Remotely sensed AGB estimates were validated against forest inventory data using the leave-one-out (LOO) method. An optimized k-NN method was designed by varying both mathematical formulation of the algorithm and remote-sensing data input, which resulted in 3000 different model configurations. Following topographic correction, performance of the optimized k-NN method was compared to that of the regression method. The optimized k-NN method (R² = 0.59, root mean square error (RMSE) = 24.92 tonnes ha^–1) was found to perform much better than the regression method (R² = 0.42, RMSE = 29.74 tonnes ha^–1) for forest AGB retrieval over this montane area. Our results indicated that the optimized k-NN method is capable of operational application to forest AGB estimates in regions where few inventory data are available.     

Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study

Landscapes containing differing amounts of ecological disturbance provide an excellent opportunity to validate and better understand the emerging Moderate Resolution Imaging Spectrometer (MODIS) vegetation products. Four sites, including 1‐year post‐fire coniferous, 13‐year post‐fire deciduous, 24‐year post‐fire deciduous, and >100 year old post‐fire coniferous forests, were selected to serve as a post‐fire chronosequence in the central Siberian region of Krasnoyarsk (57.3°N, 91.6°E) with which to study the MODIS leaf area index (LAI) and vegetation index (VI) products. The collection 4 MODIS LAI product correctly represented the summer site phenologies, but significantly underestimated the LAI value of the >100 year old coniferous forest during the November to April time period. Landsat 7‐derived enhanced vegetation index (EVI) performed better than normalized difference vegetation index (NDVI) to separate the deciduous and conifer forests, and both indices contained significant correlation with field‐derived LAI values at coniferous forest sites (r ² = 0.61 and r ² = 0.69, respectively). The reduced simple ratio (RSR) markedly improved LAI prediction from satellite measurements (r ² = 0.89) relative to NDVI and EVI. LAI estimates derived from ETM+ images were scaled up to evaluate the 1 km resolution MODIS LAI product; from this analysis MODIS LAI overestimated values in the low LAI deciduous forests (where LAI<5) and underestimated values in the high LAI conifer forests (where LAI>6). Our results indicate that further research on the MODIS LAI product is warranted to better understand and improve remote LAI quantification in disturbed forest landscapes over the course of the year.     

Forest stand biomass estimation using ALOS PALSAR data based on LiDAR-derived prior knowledge in the Qilian Mountain,western China

Studies are needed to evaluate the ability of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) for forest aboveground biomass (AGB) extraction in mountainous areas. In this article, forest biomass was estimated at plot and stand levels, and different biomass grades, respectively. Light detection and ranging (LiDAR) data with about one hit per m² were first used for forest biomass estimation at the plot level, with R ² of 0.77. Then the LiDAR-derived biomass, as prior knowledge, was used to investigate the relationship between ALOS PALSAR data and biomass. The results showed that at each biomass level, the range of the back-scatter coefficient in HH and HV polarization (where H and V represent horizontal and vertical polarizations, respectively, and the first of the two letters refers to the transmission polarization and the second to the received polarization) was very large and there was no obvious relationship between the synthetic aperture radar (SAR) back-scatter coefficient and biomass at plot level. At stand level and in different biomass grades, the back-scatter coefficient increased with the increase of forest biomass, and a logarithm equation can be used to describe the relationship. The main reason may be that forest structure is complex at the plot level, while the average value could partly decrease the influence of forest structure at stand level. Meanwhile, terrain radiometric correction (TRC) was investigated and found effective for forest biomass estimation.     

Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta

This study explored hyperspectral field and satellite-based remote sensing of soil salt content. Using Kenli County in the Yellow River Delta as the study area, in situ soil field spectra and satellite-based remote-sensing images were integrated with laboratory measurements of soil sample salinity to improve remote sensing-based soil salt estimation and inversion procedures. First, the narrow-band hyperspectral reflectance field data were used to model the wide-band reflectance data from Landsat 7. Second, the bands and spectral features sensitive to soil salt content were identified through correlation analysis and band combination. Stepwise multiple linear regression was used to find a best model, which was then inverted to predict soil salt content using remote-sensing images from Landsat 7 and Landsat 8. The applicability of the model was verified by ground-checking the inversion results. The results show that the bands sensitive to soil salinity are mainly in the visible and near-infrared (NIR) regions. Combining information from these bands can eliminate some background effects and significantly improve the correlation with salinity. The best model of soil salinity is y = 1.345 ? 25.898 × g_SWIR1 ? 245.440 × g_Red × (g_Red ? g_NIR) ? 0.252 × (g_Red + g_NIR)/(g_Red ? g_NIR) ? 19.563 × (g_Red ? g_SWIR1). This model has a coefficient of determination (R²) of 0.896, a verification R² of 0.867, a relative prediction deviation (RPD) of 2.135, and a root mean square error (RMSE) of 0.264. The model fits well and is highly stable. The inversion results based on Landsat 7 and Landsat 8 images are consistent with the actual situation of soil salinity in the study area. This study provides an effective and feasible method for the estimation of soil salt content in coastal regions based on field spectral measurements and remote-sensing inversion.     

Modelling forest stand volume and tree density using Landsat ETM+ data

Estimation of stand volume and tree density in a large area using remotely sensed data has considerable significance for sustainable management of natural resources. In this paper, we explore likely relationships between forest stand characteristics and Landsat Enhanced Thematic Mapper Plus (ETM+) reflectance values. We used multivariate regression technique to predict stand volume and tree density. The result showed that a linear combination of greenness and difference vegetation index (DVI) were better predictors of stand volume (adjusted R² = 43%; root mean square error (RMSE) = 97.4 m³ ha^?1) than other ETM+ bands and vegetation indices. In addition, the regression model with ETM4 (near infrared band) and ETM5 (first shortwave band) as independent variables was a better predictor of tree density (adjusted R² = 73.4%; RMSE = 170.13 ha^?1) than other combinations of ETM⁺ bands and vegetation indices. Results obtained from this study demonstrate the significant relationship between forest stand characteristics and ETM+ reflectance values and the utility of transformed bands in modelling stand volume and tree density. Based on the results of this study, we conclude that ETM+ data are useful to estimate forest volume and density and to gain insights into its structural characteristics in our study area. Forest managers could use ETM+ data for gaining insights into stand characteristics and generating maps required for developing forest management plans and identifying locations within stands that require treatments and other interventions.     

Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution (<30 m) data are used to assess their accuracy with little regard to the accuracy of the higher spatial resolution reference data. In this study we aimed to investigate whether Landsat Enhanced Thematic Mapper (ETM+)‐derived reference imagery can be more accurately produced using such spectrally informed methods. The efficacy of several spectral index methods to discriminate between burned and unburned surfaces over a series of spatial scales (ground, IKONOS, Landsat ETM+ and data from the MOderate Resolution Imaging Spectrometer, MODIS) were evaluated. The optimal Landsat ETM+ reference image of burned area was achieved using a charcoal fraction map derived by linear spectral unmixing (k = 1.00, a = 99.5%), where pixels were defined as burnt if the charcoal fraction per pixel exceeded 50%. Comparison of coincident Landsat ETM+ and IKONOS burned area maps of a neighbouring region in Mongu (Zambia) indicated that the charcoal fraction map method overestimated the area burned by 1.6%. This method was, however, unstable, with the optimal fixed threshold occurring at >65% at the MODIS scale, presumably because of the decrease in signal‐to‐noise ratio as compared to the Landsat scale. At the MODIS scale the Mid‐Infrared Bispectral Index (MIRBI) using a fixed threshold of >1.75 was determined to be the optimal regional burned area mapping index (slope = 0.99, r ² = 0.95, SE = 61.40, y = Landsat burned area, x = MODIS burned area). Application of MIRBI to the entire MODIS temporal series measured the burned area as 10 267 km² during the 2001 fire season. The char fraction map and the MIRBI methodologies, which both produced reasonable burned area maps within southern African savannah environments, should also be evaluated in woodland and forested environments.     

Model development and comparison to predict softwood and hardwood per cent cover using high and medium spatial resolution imagery

The recent availability of high spatial resolution multispectral scanners provides an opportunity to adapt existing methods and test models to derive spatially explicit forest type and per cent cover information at the Landsat pixel level. A regression modelling methodology was applied for scaling‐up high resolution (IKONOS) to medium spatial resolution satellite imagery (Landsat) to predict softwood and hardwood forest type and density (per cent cover) in a northern Maine study area. Regression relationships (63 different models) were developed and compared. The model variables included vegetation indices and several date (season) combinations of Landsat Enhanced Thematic Mapper Plus (ETM+) imagery (August, September, October and May).

A model incorporating all variables from four dates of Landsat ETM+ imagery produced the highest coefficient of variation in predicting both softwood (0.655) and hardwood cover (0.66). The addition of vegetation indices with the six ETM+ reflected bands did not significantly improve or detract from the regression relationships for any of the multi‐date or single date models examined. A two‐date combination of October and May variables provided an acceptable (and arguably more cost‐effective) model as the adjusted R ² value was 0.645 for softwood and 0.649 for hardwood. A significant result was that all single‐date models produced inferior results with a sharp drop in adjusted R ², compared with the multi‐date seasonal models. This research has demonstrated that the regression models including multi‐date variables produce good results and can provide spatially explicit forest type and stand structure data that has been difficult or infeasible to obtain from medium spatial resolution imagery using traditional classification methods.     

Harbour dredging and fish mortality in an aquaculture zone: assessment of changes in suspended particulate matter using multi-sensor remote-sensing data

The semi-closed Rushan Bay is one of the largest aquaculture bases in North China, with its only navigation channel connecting the Rushan harbour and the Yellow Sea. Recent economic growth with increased import and export demands have stimulated a dredging operation, starting on 26 March 2010, which was paused on 17 June 2010 after numerous reports of mortality of cultured shellfish, such as clam (Ruditapes philippinarum), among other precious marine animals. A lawsuit was filed to settle the dispute between the dredging company and an aquaculture company, yet there was endless debate on whether the mortality was caused by the dredging operations. Here, using multi-sensor remote-sensing data collected by Landsat/ETM+, HJ-1A&1 B/CCD, and Aqua & Terra/MODIS, we addressed the two critical questions of (1) whether there was a significant increase in the suspended particulate matter (SPM) in the aquaculture area during and after the dredging operations; and (2) if the answer is yes, whether such an increase was a direct result of the dredging. After careful selection of the satellite data and algorithms, the results from all three sensors suggested positive answers to both questions. In the 2 km² aquaculture zone where significant mortality of cultured clam was reported, SPM derived from all three sensors during the dredging period was found to be at least 20 mg l^–1 higher than that during the same period in previous years, far exceeding the 10 mg l^–1 threshold value that has been used to gauge water quality degradation.     

Satellite estimation of forest carbon using regression models

Periodic monitoring of forest carbon is important, since forest cover is changing rapidly in many parts of the world, and becomes a major source of terrestrial carbon emission that may be one of the main drivers of global climate change. Regression is often used to estimate forest variables (including carbon) using satellite sensor data though a low coefficient of determination (R ²) is apparent and this research was designed to investigate both traditional and alternate regression approaches to increase the magnitude of R ². The study area was located in southeastern Bangladesh. Data from Landsat Enhanced Thematic Mapper Plus (ETM+) and ground‐based forest survey were used. This research explored the use of dummy variables in regression models to increase R ², while the dummies were set from the optimal stratification of forestland. The finding will heighten the accuracy of forest attribute estimation and help to understand terrestrial carbon dynamics and global climate change.     

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.     

SAR-derived wind fields at the coastal region in the East/Japan Sea and relation to coastal upwelling

The relationship between the modification of synthetic aperture radar (SAR) wind field and coastal upwelling was investigated using high-resolution wind fields from Advanced Land Observing Satellite (ALOS) Phased Array type L-band synthetic aperture radar (PALSAR) imagery and sea-surface temperature (SST) from National Oceanic and Atmospheric Administration/Advanced Very-High-Resolution Radiometer (NOAA/AVHRR) data. The retrieved SAR wind speeds seem to agree well with in situ buoy measurements with only a relatively small error of 0.7 m s^?1. The SAR wind fields retrieved from the east coast of Korea in August 2007 revealed a spatial distinction between near and offshore regions. Low wind speeds of less than 3 m s^?1 were associated with cold water regions with dominant coastal upwelling. Time series of in situ measurements of both wind speed and water temperature indicated that the upwelling was induced by the wind field. The low wind field from SAR was mainly induced by changes in atmospheric stability due to air–sea temperature differences. In addition, wind speed magnitude showed a positive correlation with the difference between SST and air temperature (R² = 0.63). The dependence of viscosity of water on radar backscattering at the present upwelling region was negligible since SAR data showed a relatively large backscattering attenuation to an SST ratio of 1.2 dB °C^?1. This study also addressed the important role of coastal upwelling on biological bloom under oligotrophic environments during summer.     

Remote-sensing assessment of forest damage by Typhoon Saomai and its related factors at landscape scale

The remote-sensing technique is a cost-effective tool for monitoring large-scale forest damage sustained by typhoon events. Taking Cangnan County as the study area, this study aimed to extract the spatial pattern of damaged forest and determine the influencing factors of Typhoon Saomai in 2006, using Landsat Enhanced Thematic Mapper Plus (ETM+) data before and after the typhoon event. The results showed that 183 km² of forest land were damaged by Typhoon Saomai. There was no obvious diverse influence on forest damage within 25 km of Saomai’s path, after that the area of damaged forest decreased rapidly. For the land uses of construction, crop, and grass, decrease in normalized difference vegetation index was considerable under 100 m elevation and the number of damaged forest pixels showed positive correlation with vegetation aggregation, because trees standing in isolation, alongside roads, or in small groupings were easily damaged. For forest land, the number of damaged forest pixels decreased with higher elevation and relative aspect; when the relative aspect was in the range 0–40°, the number of damaged forest pixels was highest. Considering the interactive effects of these factors on forest damage caused by the typhoon, vegetation aggregation had the strongest influence followed by elevation, land use, relative aspect, and distance from the typhoon’s path.     

Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data

Most terrestrial carbon is stored in forest biomass, which plays an important role in local, regional, and global climate change. Monitoring of forests and their status, and accurate estimation of forest biomass are important in mitigating the impacts of climate change. Empirical models developed using remote-sensing and field-measured forest data are commonly used to estimate forest biomass. In the present study, we used a mechanistic model to estimate height and biomass in the Three Gorges reservoir region (China) based on the allometric scale and resource limits (ASRL) model. The forests in the Three Gorges reservoir region are important and unique in view of the vertical distribution of vegetation and mixed needleleaf. Detailed information about the forest in this region is available from the Geoscience Laser Altimeter System (GLAS) and field measurements from 714 forest plots. The ASRL model parameters were adjusted using GLAS-derived forest tree height to reduce the deviation between modelled and observed forest height. The predicted maximum forest tree height from the optimized ASRL model was compared to measured tree heights, and a good correlation (R² = 0.566) was found. The allometric scale function between forest height and diameter at breast height (DBH) is developed and the maximum forest tree height from the optimized ASRL model transferred to DBH. Moreover, the forest biomass was estimated from DBH according to the allometric scale function that was determined using DBH and biomass data. The results of maximum forest biomass using the ASRL model and the allometric scale function show a good accuracy (R² = 0.887) in the Three Gorges reservoir region. Here, we present the forest biomass estimation approach following allometric theory for accurate estimation of maximum forest tree height and biomass. The proposed approach can be applied to forest species in all types of environmental conditions.