Assessment of forest damage caused by an ice storm using multi-temporal remote-sensing images: a case study from Guangdong Province

In early 2008, forest ecosystems in southern China suffered damage due to a severe ice storm disaster. The area and degree of forest damage caused by the ice storm was assessed using Satellite Pour l’Observation de la Terre (SPOT)-Vegetation images for Guangdong Province acquired between 1999 and 2008. By using the maximum value composition method and image thresholding techniques, the forest vegetation loss, expressed as the change in net primary productivity (NPP) and two indicators (I₁, I₂), was estimated. The damage threshold was determined by comparing the standard deviation of pixels of the undamaged areas in 2008 and other years without any disaster, which was 10%. The area of damaged forest vegetation was 47,670 km², with the northern Guangdong Province most seriously affected. The total loss of NPP for forest vegetation was 50,578,055 t (DW) year^?1, with 52 counties (43.7%) suffering forest vegetation damage. Evergreen coniferous forest was most widely affected, but evergreen broad-leaved forest was the most severely damaged vegetation type. Terrain topography influenced the damage to forest vegetation, which was found to increase with increasing elevation and slope gradient. The range and degree of damaged forest determined by remote-sensing data is consistent with the extent of the ice storm, indicating that this study provides a new approach for rapid assessment of forest disasters at a regional scale.     

Upper ocean response to Super Typhoon Tembin (2012) explored using multiplatform satellites and Argo float observations

Using multiplatform satellites and in situ Argo float observations, this study systematically examined the upper ocean response to Super Typhoon Tembin (2012) in the western north pacific, and the interaction between typhoon and a pre-existing cold core eddy (CCE) was particularly focused on. Significant sea surface temperature (SST) cooling and sea surface height anomaly (SSHA) decrease was detected along track after typhoon, with the maximum SST cooling and SSHA decrease reaching 4.0°C and 25 cm, respectively. The pre-existing CCE was located to the left of the typhoon track, resulting in an intriguing leftward bias of SST cooling. The maximum SST cooling appeared at about 25 km to the left of the typhoon track, with SST cooling to the left of the track 40–100% larger than that to the right. After typhoon, the CCE was expanded by 50% due to the typhoon’s cyclonic wind stress. The thermocline was uplifted by 15–25 m by the typhoon-induced upwelling. Typhoon-enhanced vertical mixing was inferred from high-resolution Argo float data based on the Gregg–Henyey–Polzin parameterization method. The diapycnal diffusivity reached 9 × 10^?4 m² s^?1 after typhoon, which was more than 10 times larger than that before typhoon.     

Forest recovery trends derived from Landsat time series for North American boreal forests

A critical component of landscape dynamics is the recovery of vegetation following disturbance. The objective of this research was to characterize the forest recovery trends associated with a range of spectral indicators and report their observed performance and identified limitations. Forest disturbances were mapped for a random sample of three major bioclimate zones of North American boreal forests. The mean number of years for forest to recover, defined as time required to for a pixel to attain 80% of the mean spectral value of the 2 years prior to disturbance, was estimated for each disturbed pixel. The majority of disturbed pixels recovered within the first 5 years regardless of the index ranging from approximately 78% with normalized burn ratio (NBR) to 95% with tasselled cap greenness (TCG) and after 10 years more than 93% of disturbed pixels had recovered. Recovery rates suggest that normalized differenced vegetation index (NDVI) and TCG saturate earlier than indices that emphasize longer wavelengths. Thus, indices such as NBR and the mid-infrared spectral band offer increased capacity to characterize different levels of forest recovery. The mean length of time for spectral indices to recover to 80% of the pre-disturbance value for pixels disturbed 10 or more years ago was highest for NBR, 5.6 years, and lowest for TCG, 1.7 years. The mid-infrared spectral band had the greatest difference in recovered pixels among bioclimate zones 1 year after disturbance, ranging from approximately 42% of disturbed pixels for the cold and mesic bioclimate zone to 60% for the extremely cold and mesic bioclimate zone. The cold and mesic bioclimate zone had the longest mean years to recover ranging from 1.9 years for TCG to 4.2 years for NBR, while the cool temperate and dry bioclimate zone had the shortest mean years to recover ranging from 1.6 years for TCG to 2.9 years for NBR suggesting differences in pre-disturbance conditions or successional processes. The results highlight the need for caution when selecting and interpreting a spectral index for recovery characterization, as spectral indices, based upon the constituent wavelengths, are sensitive to different vegetation conditions and will provide a variable representation of structural conditions of forests.     

Effects of satellite image spatial aggregation and resolution on estimates of forest land area

Satellite imagery is being used increasingly in association with national forest inventories (NFIs) to produce maps and enhance estimates of forest attributes. We simulated several image spatial resolutions within sparsely and heavily forested study areas to assess resolution effects on estimates of forest land area, independent of other sensor characteristics. We spatially aggregated 30 m datasets to coarser spatial resolutions (90, 150, 210, 270, 510 and 990 m) and produced estimates of forest proportion for each spatial resolution using both model‐ and design‐based approaches. Average‐based aggregation had no effect on per‐image estimates of forest proportion; image variability decreased with increasing spatial resolution and local variability peaked between 210 and 270 m. Majority‐based aggregation resulted in overestimation of forest land in a heavily forested landscape and underestimation of forest land in a sparsely forested landscape, with both trends following a natural log distribution. Of the spatial resolutions tested, 30 m was superior for obtaining estimates using model‐based approaches. However, standard errors of design‐based inventory estimates of forest proportion were smallest when accompanying stratification maps which were aggregated to between 90 and 150 m spatial resolutions and strata thresholds were optimized by study area. These results suggest that spatially aggregating existing 30 m land cover datasets can provide NFIs with gains in precision of their estimates of forest land area, while reducing image storage size and processing times; land cover datasets derived from coarser spatial resolution sensors may provide similar benefits.     

Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information

Land-surface temperature (LST) is strongly affected by altitude and surface albedo. In mountain regions where steep slopes and heterogeneous land cover are predominant, LST can vary significantly within short distances. Although remote sensing currently provides opportunities for monitoring LST in inaccessible regions, the coarse resolution of some sensors may result in large uncertainties at sub-pixel scales. This study aimed to develop a simple methodology for downscaling 1 km Moderate Resolution Spectroradiometer (MODIS) LST pixels, by accounting for sub-pixel LST variation associated with altitude and land-cover spatial changes. The approach was tested in Mount Kilimanjaro, Tanzania, where changes in altitude and vegetation can take place over short distances. Daytime and night-time MODIS LST estimates were considered separately. A digital elevation model (DEM) and normalized difference vegetation index (NDVI), both at 250 m spatial resolution, were used to assess altitude and land-cover changes, respectively. Simple linear regressions and multivariate regressions were used to quantify the relationship between LST and the independent variables, altitude and NDVI. The results show that, in Kilimanjaro, altitude variation within the area covered by a 1 km MODIS LST pixel can be up to ±300 m. These altitude changes can cause sub-pixel variation of up to ±2.13°C for night-time and ±2.88°C for daytime LST. NDVI variation within 1 km pixels ranged between –0.2 and 0.2. For night-time measurements, altitude explained up to 97% of LST variation, while daytime LST was strongly affected by land cover. Using multivariate regressions, the combination of altitude and NDVI explained up to 94% of daytime LST variation in Kilimanjaro. Finally, the downscaling approach proposed in this study allowed an improved representation of the influence of landscape features on local-scale LST patterns.     

Impacts of land use and socioeconomic patterns on urban heat Island

Intensive land surface change and human activities induced by rapid urbanization are the major causes of the urban heat island (UHI) phenomenon. In this article, we examined the spatial variability of UHI and its relationships with land use and socioeconomic patterns in the Baltimore–DC metropolitan area. Census data, road network as well the digital elevation model (DEM) and average water surface percentage were selected to analyse the correlation between spatial patterns of UHI and socioeconomic factors. The impervious surface (coefficient of determination R² = 0.89) and normalized difference vegetation index (R² = 0.81) were the two most important landscape factors, and population density (R² = 0.57) was the most influential socioeconomic variable in contributing to the UHI intensity. Generally, the socioeconomic variables had smaller influence on the UHI intensity than the landscape variables. Based on the patch analysis, most of the socioeconomic variables influenced the UHI intensity indirectly through changing the physical environment (e.g. impervious surface or forest cover). The selected landscape and socioeconomic variables, except impervious surface percentage, demonstrated third-order polynomial correlation with the UHI intensity. The higher correlations were found within certain ranges such as forest percentage from 0% to 30% and population density from 0 to 5000 km^–2. This research provides a case study to understand the urban land surface, vegetation, and microclimate for urban management and planning.     

Differentiation of Acacia koa forest stands across an elevation gradient in Hawai‘i using fine-resolution remotely sensed imagery

Koa (Acacia koa) forests are found across broad environmental gradients in the Hawaiian Islands. Previous studies have identified important environmental factors controlling stand structure and productivity at the plot level, but these have not been applied at the landscape level because of small-scale spatial variability. The goal of this study is to compare the differentiation of koa forest types across an elevation/temperature gradient ranging from 1200 to 2050 m asl (17–13°C mean annual temperature (MAT)) through the analysis of field measurements of forest structure and fine-resolution remotely sensed imagery. Several vegetation indices (VIs) (atmospherically resistant vegetation index (ARVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), simple ratio (SR) and modified simple ratio (MSR)) are calculated from IKONOS satellite imagery of these stands and analysed using supervised classification techniques. This procedure allows a clear differentiation of koa stands from areas dominated by grasses, shrubs and bare lava. Across the elevation gradient, VIs allow differentiation of three koa forest stand classes at upper, intermediate and lower elevations. In agreement with the image classification, analysis of variance (ANOVA) of tree height and leaf phosphorus (P) suggests that there are also three significantly different groups of koa stands at those elevations. A landscape-scale map of land cover and koa stand classes demonstrates both the general trend with elevation and the small-scale heterogeneity that exists across the elevation gradient. Application of these classification techniques with fine spatial resolution imagery can improve the characterization of different koa stand types across the islands of Hawai‘i, which should aid both the conservation and utilization of this ecologically important species.     

Assessment of tsunami and anthropogenic impacts on the forest of the North Andaman Islands,India

Forests are being depleted drastically at higher rates to cater to the needs of growing population. In this context, an attempt was made to identify the drivers of forest changes on the vegetation of the North Andaman islands by broadly categorising the changes as anthropogenic and natural disturbances (tsunami) using satellite images of 1976, 1999 and 2005. The images were classified using visual interpretation technique to generate land cover maps of the area under study. A detailed change analysis of the 1976, 1999 and 2005 images showed that a high proportion of the natural vegetation has been converted into agriculture, settlement, sand and water. The overall forest change from 1976 to 2005 is 11,670 ha with a deforestation rate of 389 ha yr^?1. The tsunami of 26 December, 2004 was found to be a major cause of deforestation of coastal forests in the North Andaman Islands, deforesting an area of 3292.5 ha. Simulation of forest cover in the next 25 and 50 years predicted a deforestation of 13,100 and 22,700 ha with a corresponding increase in non‐forest land cover to 19,600 and 29,600 ha respectively. It is predicted that after 50 years the forest area of 131,200 ha, estimated from the 1999 satellite data, may reduce to 108,500 ha, if proper conservation measures are not taken.     

Intercalibration of NOAA and Meteosat window channel brightness temperatures

This study presents an intercalibration of Meteosat‐5 11 µm channel and NOAA‐14 10.8 µm and 12.0 µm channels, and their comparison for sea and land pixels. The intercalibration empirical relation is derived for clear‐sky sea measurements, with similar zenith viewing angles. The root mean square difference between NOAA‐14 and Meteosat‐5 intercalibrated brightness temperatures is about 1.4 K (4.7 K) for all clear‐sky sea (land) pixels. The discrepancies over land are analysed in terms of viewing angle, surface type, terrain elevation and exposure to sunlight. The satellite viewing geometry is responsible for two major impacts, namely: the obstruction by neighbouring clouds towards one of the satellites; and differences in surface solar illumination viewed by each sensor. It is also shown that the higher discrepancies between intercalibrated temperatures occur for the most heterogeneous surfaces (e.g. Open Shrublands). The effect of terrain variability is not linear and depends strongly on the surface type.     

Thermal infrared inverse model for component temperatures of mixed pixels

Multiangular remote sensing data can be used to retrieve land surface component temperatures, which will have a broad application in the future. For higher resolution pixels of satellite radiometers, the component temperatures may be separated adequately by some methods. However, for coarse resolution pixels that contain a mixture of vegetation and bare soil, the component temperatures may not be retrieved robustly by traditional inversion methods. In this study, a thermal model-based algorithm was developed for mixed pixels. A simulation method was implemented to assess the performance of the algorithm. The method consisted of extensive radiative transfer simulations under a wide variety of Leaf Area Index (LAI) values in the vegetation part of directional thermal infrared (TIR) radiation, vegetation and soil emissivity, vegetation and soil temperatures, bare soil area ratio and downwelling longwave atmospheric radiation. The results indicate that the inversion error of the component temperatures does not exceed 0.5° when LAI values are less than 6.0. A field experiment was also conducted to assess the accuracy of the model. The experimental results indicate that even if the differences between the nadir and off-nadir radiative temperatures over a mixed pixel are small, the model can still determine the component temperatures accurately. A sensitivity analysis shows that an accuracy of less than 10% for LAI in the vegetation part and the bare soil area ratio is required to achieve a precision of 1 K for the component temperatures derived. An error of 1 K in the radiometric temperature leads to an error of 1 K in the component temperatures retrieved.     

View angle effects on the discrimination of selected Amazonian land cover types from a principal‐component analysis of MISR spectra

Multi‐angle Imaging Spectroradiometer (MISR) data, collected in four bands and at nine view angles in the Brazilian Amazon region, were used to describe view‐angle effects on the spectral response and discrimination of three forest types; close and open lowland forests, open submontane forest and green/emerging pastures. A principal‐component analysis (PCA) was applied over 450 bidirectional reflectance factor (BRF) MISR spectra (10 pixels, five land covers and nine view angles) to characterize the spectral‐angular variability in the dataset and to identify the best view direction to enhance land cover discrimination. The analysis was extended into the images of the different cameras, which were classified for the presence of the forest covers using the minimum distance of the pixels to the average PC1 and PC2 scores of each forest class calculated from spectra analysis. Results showed an increase in the mean reflectance over the spectral bands (brightness) of the land covers from nadir to extreme viewing, as indicated by the first principal component, especially in the backward direction due to the predominance of sunlit view vegetation components. The transition from the backward (sunlit view surface components) to the forward (shaded view surface components) scattering directions was also characterized by changes in the shape of the BRF spectra, as indicated by decreasing PC2 score or near‐infrared/blue ratio values. The variations in the MISR BRF followed the regularities expected from theory. PCA results also indicated that the best viewing to discriminate the forest types was the backward scattering direction (?26.1° view angle), whereas the less favourable viewing was the forward scattering direction under the view shading condition (e.g. +45.6° view angle). The overall classification accuracy for the three forest types increased from 52.4% at +45.6° view angle to 78.7% at nadir, and to 95.0% at a ?26.1° view angle. From nadir to extreme view angles, directional effects produced a NDVI decrease for the forest types and an NDVI increase for the green and especially emerging pastures. Results demonstrated that data acquisition in off‐nadir viewing may improve the discrimination and mapping of the Amazonian land cover types.     

The importance of data type,laser spot density and modelling method for vegetation height mapping in continental China

Vegetation height not only has great significance in the field of ecology but also offers a useful contribution to detailed land cover classification. The first vegetation height map was acquired in this study using the ice, cloud, and land elevation satellite /geosciences laser altimeter system (ICESat/GLAS) and other multisource remote sensing data, such as moderate-resolution imaging spectroradiometer (MODIS) tree cover products, leaf area index (LAI) products, Nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR), climatic variables, and topographic indices. We mainly discuss the importance of data type, density of laser spot and modelling method in the generation of this vegetation height map in continental China. It was found that (1) a higher density of laser spot could improve the reliability of modelling in mountainous areas covered by a wide range of forest and shrub land; (2) in terms of the importance of input variables, in the random forest regression modelling, the most important ones are elevation, slope, mean air temperature, temperature variance, precipitation, precipitation variance, and NBAR; (3) when modelling using 50 ecozones covering the whole of continental China, the model showed a good performance with an accuracy of root mean square error (RMSE), correlation coefficient (r), index of agreement (d), and mean absolute error (MAE) at 5.7, 0.7, 0.8, and 3.8 m, respectively. A visual comparison suggests that the spatial pattern of vegetation height is consistent with that of land cover in China. It is very necessary in evaluating the importance of data type, laser spot density, and modelling method in vegetation height mapping in continental China.     

A regional ASTER GDEM error model for the Chinese Loess Plateau

Accuracy of the global ASTER GDEM (Advanced Space-borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) version 2 (v2) elevation data product is highly variable regionally, as are its empirical correlations with landscape variables. This paper investigates GDEM error along a 49-site geomorphologic gradient within the core region of the Chinese Loess Plateau, notable for its heterogeneous terrain. The error is modelled using its associations with MODIS (Moderate Resolution Imaging Spectroradiometer) composite forest cover percentage, GlobeLand30 land cover, and key elevation derivatives, including two indices, terrain roughness index (TRI) and topographic position index (TPI), not previously evaluated in GDEM accuracy studies. Overall root mean squared error (RMSE) is 20.33 m, in excess of the GDEM v2 accuracy specifications, while RMSE at each site varies substantially, from 10.67 m for a low relief area to 21.84 m for the most rugged site. Strong associations between covariates, especially slope, aspect, TRI, and forest cover are identified. A regression model using these variables is developed to formally characterize and predict GDEM error. External validation with independent checkpoints across all sites demonstrates that this model can reduce mean error by about 4 m.     

Major trends in the land surface phenology (LSP) of Africa,controlling for land-cover change

ABSTRACT

Monitoring land surface phenology (LSP) trends is important in understanding how both climatic and non-climatic factors influence vegetation growth and dynamics. Controlling for land-cover changes in these analyses has been undertaken only rarely, especially in poorly studied regions like Africa. Using regression models and controlling for land-cover changes, this study estimated LSP trends for Africa from the enhanced vegetation index (EVI) derived from 500 m surface reflectance Moderate-Resolution Imaging Spectroradiometer (MOD09A1), for the period from 2001 to 2015. Overall end of season showed slightly more pixels with significant trends (12.9% of pixels) than start of season (11.56% of pixels) and length of season (LOS) (5.72% of pixels), leading generally to more ‘longer season’ LOS trends. Importantly, LSP trends that were not affected by land-cover changes were distinguished from those that were influenced by land-cover changes such as to map LSP changes that have occurred within stable land-cover classes and which might, therefore, be reasonably associated with climate changes through time. As expected, greater slope magnitudes were observed more frequently for pixels with land-cover changes compared to those without, indicating the importance of controlling for land cover. Consequently, we suggest that future analyses of LSP trends should control for land-cover changes such as to isolate LSP trends that are solely climate-driven and/or those influenced by other anthropogenic activities or a combination of both.     

A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series

There is a significant need to provide nationwide consistent information for land managers and scientists to assist with property planning, vegetation monitoring applications, risk assessment, and conservation activities at an appropriate spatial scale. We created maps of woody vegetation cover of Australia using a consistent method applied across the continent, and made them accessible. We classified pixels as woody or not woody, quantified their foliage projective cover, and classed them as forest or other wooded lands based on their cover density. The maps provide, for the first time, cover density estimates of Australian forests and other wooded lands with the spatial detail required for local-scale studies. The maps were created by linking field data, collected by a network of collaborators across the continent, to a time series of Landsat-5 TM and Landsat-7 ETM+ images for the period 2000–2010. The fractions of green vegetation cover, non-green vegetation cover, and bare ground were calculated for each pixel using a previously developed spectral unmixing approach. Time series statistics, for the green vegetation cover, were used to classify each pixel as either woody or not using a random forest classifier. An estimate of woody foliage projective cover was made by calibration with field measurements, and woody pixels classified as forest where the foliage cover was at least 0.1. Validation of the foliage projective cover with field measurements gave a coefficient of determination, R²,of 0.918 and root mean square error of 0.070. The user’s and producer’s accuracies for areas mapped as forest were high at 92.2% and 95.9%, respectively. The user’s and producers’s accuracies were lower for other wooded lands at 75.7% and 61.3%, respectively. Further research into methods to better separate areas with sparse woody vegetation from those without woody vegetation is needed. The maps provide information that will assist in gaining a better understanding of our natural environment. Applications range from the continental-scale activity of estimating national carbon stocks, to the local scale activities of assessing habitat suitability and property planning.     

Tuning of Adaptive Weight Depth Map Generation Algorithms

In depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on unplanned experiments. Algorithms’ performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury’s standards. This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative influence of the parameters and to fine-tune them based on the number of bad pixels. The implemented methodology improves the performance of adaptive weight based dense depth map algorithms. As a result, the algorithm improves from 16.78 to 14.48 % bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78 to 13 %.     

Validation and application of the monitoring method for degraded land-area-based on a dust erodibility in eastern Asia

Arid regions are very sensitive to climate change and human activity, two critical drivers of change that are degrading environmental conditions. Part of the world’s drylands lie in eastern Asia, including China and Mongolia, where the problems of desertification, drought, and Asian dust events (ADEs) are frequent. To help prevent economic damage from these problems, an early warning and monitoring system based on numerical models, remote sensing, and weather forecasts is needed. I define a degraded land area as ‘the area where dust can easily occur’ and make exclusive use of satellite data to identify land that meets certain conditions of vegetation and aridity. I then validate this definition against a dust erodibility map and occurrences of ADEs over Japan, which was closely related to the extent and severity of dust areas in Mongolia and parts of China, especially in March (coefficient of determination R² = 0.856). The yearly change of degraded land area indicates a clear decreasing trend in China (R² = 0.210), but an overall negative trend in Mongolia (R² = 0.010). Years of major droughts in China and Mongolia correspond well to large positive deviations in degraded land area.     

Remote sensing the orographic effects of dry-hot valley on vegetation distribution in the southeast Tibetan Plateau

ABSTRACT

Mountains in the southeast Tibetan Plateau (TP) often intercept and precipitate abundant monsoon-transported vapours, but some deep valleys of this region are likely subjected to heavy water stress possibly related to orographic effects. Understanding the orographic effects of these dry-hot valleys (DHV) on vegetation distribution is crucial to project local ecological response to global warming. In the study, we used multiple satellite observations with limited in-situ records to investigate the links between vegetation cover and geomorphology in the southeast TP. We designed two types of transects to distinguish altitudinal properties of heat and vegetation between the DHV and non-DHV areas with satellite-retrieved enhanced vegetation index and land surface temperature (LST). Our results showed that the DHVs are characterized by the seemingly ‘abnormal’ decreasing of vegetation density from intermediate elevation simultaneously towards both ridge and valley. The significant increase in LST lapse rate with valley depth (1.8 × 10^?3°C km^?1 m^?1, p < 0.01) suggested the positive role of local valley wind system in the DHV development. Satellite observations revealed that there are, respectively, about 530, 420, and 300 km of DHVs developed in the Nujiang, Lancangjiang, and upper Yangtze rivers, and the DHVs are mostly deeper than 1600 m. Current global warming may lead to the altitudinal expansion of DHV dry and hot effects on local ecosystems, which should be carefully accounted in local ecosystem conservation and management.     

Use of RADARSAT‐1 data and a digital elevation model to assess flood damage and improve rice production in the lower part of the Chi River Basin,Thailand

In Thailand, flooding due to seasonal monsoon conditions frequently destroys a substantial amount of rice production, the most important agricultural activity of the country. Taking the 2001 monsoon flooding that hit the Lower Chi River Basin as an example, we developed a new method for accurately assessing damage to flood‐affected paddies. A RADARSAT‐1 image acquired during peak flooding was combined with a 30‐m digital elevation model (DEM) to develop a ‘flood‐level‐determination’ algorithm for estimating floodwater depth. Based on the elongation capability of the rice varieties, a water depth of 80 cm was used to separate ‘non‐damaged’ from ‘damaged’ paddy areas, indicating that about 60% of the paddy fields in the flooded areas were non‐damaged paddies. To minimize the loss of rice and maximize farmers' incomes, a map of rice varieties appropriate for the damaged paddy areas was produced, combining the flood‐affected paddy map with the flood frequency map. Our results demonstrate the potential of using single‐date RADARSAT‐1 data and a DEM to provide accurate and economic means of assessing flood damage to rice fields that can be used to improve rice production.     

A multi-sensor approach for improving biodiversity estimation in the Hyrcanian mountain forest,Iran

ABSTRACT

Modelling tree biodiversity in mountainous forests using remote-sensing data is challenging because forest composition and structure change along elevation. Topographic variations also affect vegetation’s spectral and backscattering behaviour. We demonstrate the potential of multi-source integration to tackle this challenge in a mountainous part of the Hyrcanian forest in Iran. This forest is a remnant of a deciduous broadleaved forest with heterogeneous structure affected by natural and anthropogenic factors. The multi-source approach (i.e. Landsat Enhanced Thematic Mapper Plus (ETM +), Advanced Land Observing Satellite/ Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR), and topographic variables) allows us to propose a biodiversity estimation model using partial least square regression (PLSR) calibrated and validated with limited field data. The effective number of species was calculated based on field measurements of the biodiversity in the study area. In order to model species diversity in more homogeneous extrinsic environmental conditions, we divided data into two groups with relatively uniform slope values. In each slope group, we modelled the correlation between observed biodiversity and satellite-derived data. For that, we followed three scenarios: (A) multispectral Landsat ETM + alone, (B) ALOS/PALSAR alone, and (C) inclusion of both sensors. In each scenario, elevation and slope data were also considered as predictors. We observed that in all scenarios, coefficient of determination (R²) in gentler slopes was higher than that in areas with steeper slopes (average difference in R²: ?R² = 0.21). The highest correlation was achieved by inclusion of synthetic aperture radar (SAR) and ETM + (R² = 0.87). The results clearly confirm that the multi-source remote-sensing approach can provide a practical estimate of biodiversity across the Hyrcanian forest and potentially in other deciduous broadleaved forests in complex terrain.