An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km² in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r = 0.88) was achieved for the prototype mapping zone.     

Large area forest stem volume mapping in the boreal zone using synergy of ERS-1/2 tandem coherence and MODIS vegetation continuous fields

ERS-1/2 tandem coherence was reported to have high potential for the mapping of boreal forest stem volume (e.g. Santoro et al., 2002, 2007a; Wagner et al., 2003; Askne & Santoro, 2005). Large-scale application of the data for forest stem volume mapping, however, is hindered by the variability of coherence with meteorological and environmental acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, with the aid of forest inventory data which is generally available for a few small test sites but not for large areas. In this paper a new approach is presented that allows model training using the MODIS Vegetation Continuous Fields canopy cover product (Hansen et al., 2003) without further need for ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training is presented in this paper for a multi-seasonal ERS-1/2 tandem dataset covering several well known Central Siberian forest sites. As a test scenario for large-area application, the approach was applied to a multi-seasonal ERS-1/2 tandem dataset of 223 ERS-1 and ERS-2 image pairs covering Northeast China (~ 1.5 million km²) to map four stem volume classes (0-20, 20-50, 50-80, and > 80 m³/ha).     

Mapping population change index in Southern Serbia (1961-2027) as a function of environmental factors

Niche analysis methods developed within the biogeography community are routinely used for species distribution modeling of wildlife and endangered species. So far, such techniques have not been used to explain distribution of people in an area, nor to assess spatio-temporal dynamics of human populations. In this paper, the MaxEnt approach to species distribution modeling and publicly available gridded predictors were used to analyze the population dynamics in Southern Serbia (South Pomoravlje Region) for the period 1961-2027. Population values from the census administrative units were first downscaled to 200 m grid using a detailed map of populated places and dasymetric interpolation. In the second step, a point pattern representing the whole population (468,500 inhabitants in 2002) was simulated using the R package spatstat. MaxEnt was then used to derive habitat suitability index (HSI) as a function of gridded predictors: distance to roads, elevation, slope, topographic wetness index, enhanced vegetation index and land cover classes. HSI and environmental predictors were further used to explain spatial patterns in the population change index (PCI) through regression modeling. The results show that inhabiting preference for year 1961 is mainly a function of topography (TWI, elevation). The HSI for year 2027 shows that large portions of remote areas are becoming less preferred for inhabiting. The results of cross-validation in MaxEnt show that distribution of population is distinctly controlled by environmental factors (AUC > 0.84). Population decrease is particularly significant in areas >25 km distant from the main road network. The results of regression analysis show that 40% of variability in the PCI values can be explained with these environmental maps, distance to roads and urban areas being the main drivers of migration process. This approach allows precise mapping of demographic patterns that otherwise would not be visible from the census data alone.     

Mapping forest alliances and associations using fuzzy systems and nearest neighbor classifiers

The study and management of biological communities depends on systems of classification and mapping for the organization and communication of resource information. Recent advances in remote sensing technology may enable the mapping of forest plant associations using image classification techniques. But few areas outside Europe have alliances and associations described in detail sufficient to support remote sensing-based modeling. Northwestern Montana has one of the few completed plant association classifications in the United States compliant with the recently established National Vegetation Classification system. This project examined the feasibility of mapping forest plant associations using Landsat Enhanced Thematic Mapper Plus data and advanced remote sensing technology and image classification techniques.Suitable reference data were selected from an extensive regional database of plot records. Fifteen percent of the plot samples were reserved for validation of map products, the remainder of plots designated as training data for map modeling. Key differentiae for image classification were identified from a suite of spectral and biophysical variables. Fuzzy rules were formulated for partitioning physiognomic classes in the upper levels of our image classification hierarchy. Nearest neighbor classifiers were developed for classification of lower levels (alliances and associations), where spectral and biophysical contrasts are less distinct.Maps were produced to reflect nine forest alliances and 24 associations across the study area. Error matrices were constructed for each map based on stratified random selections of map validation samples. Accuracy for the alliance map was estimated at 60%. Association classifiers provide between 54 and 86% accuracy within their respective alliances. Alternative techniques are proposed for aggregating classes and enhancing decision tree classifiers to model alliances and associations for interior forest types.     

A digital rock density map of New Zealand

Digital geological maps of New Zealand (QMAP) are combined with 9256 samples with rock density measurements from the national rock catalogue PETLAB and supplementary geological sources to generate a first digital density model of New Zealand. This digital density model will be used to compile a new geoid model for New Zealand. The geological map GIS dataset contains 123 unique main rock types spread over more than 1800 mapping units. Through these main rock types, rock densities from measurements in the PETLAB database and other sources have been assigned to geological mapping units. A mean surface rock density of 2440 kg/m³ for New Zealand is obtained from the analysis of the derived digital density model. The lower North Island mean of 2336 kg/m³ reflects the predominance of relatively young, weakly consolidated sedimentary rock, tephra, and ignimbrite compared to the South Island’s 2514 kg/m³ mean where igneous intrusions and metamorphosed sedimentary rocks including schist and gneiss are more common. All of these values are significantly lower than the mean density of the upper continental crust that is commonly adopted in geological, geophysical, and geodetic applications (2670 kg/m³) and typically attributed to the crystalline and granitic rock formations. The lighter density has implications for the calculation of the geoid surface and gravimetric reductions through New Zealand.     

Combining Isomap ordination and imaging spectroscopy to map continuous floristic gradients in a heterogeneous landscape

Mapping requires a meaningful generalization of information. For vegetation maps, classification is frequently used to generalize the species composition of (semi-)natural plant assemblages. As an alternative to classification, ordination methods aim to extract major floristic gradients describing the prevailing compositional variation in a floristic data set as metric variables. This ability has been used previously to derive gradient maps of homogeneous landscapes that show plant species composition in continuous fields. In the present study, gradient mapping was used in a more heterogeneous landscape with intricate environmental gradients and higher variation in vegetation physiognomy. Since established ordination methods may have difficulties to cope with the highly variable plant species composition, we tested the novel method Isometric Feature Mapping (Isomap) against conventional methods (Detrended Correspondence Analysis and Nonmetric Multidimensional Scaling). The resulting floristic gradients were related to hyperspectral imagery (HyMap) using partial least squares regression (PLSR) and subsequently mapped. Prediction uncertainties are provided as additional map. Isomap was able to preserve 74% of the original variation inherent to the floristic data set in a three-dimensional solution. This was considerably more than the established techniques achieved. The PLSR models for the floristic gradients extracted with Isomap showed model fits ranging from R² = 0.59 to R² = 0.73 in calibration and from R² = 0.55 to R² = 0.69 in tenfold cross-validation. The resulting gradient map provides detailed information on compositional vegetation patterns.     

Combining per-pixel and object-based classifications for mapping land cover over large areas

A plethora of national and regional applications need land-cover information covering large areas. Manual classification based on visual interpretation and digital per-pixel classification are the two most commonly applied methods for land-cover mapping over large areas using remote-sensing images, but both present several drawbacks. This paper tests a method with moderate spatial resolution images for deriving a product with a predefined minimum mapping unit (MMU) unconstrained by spatial resolution. The approach consists of a traditional supervised per-pixel classification followed by a post-classification processing that includes image segmentation and semantic map generalization. The approach was tested with AWiFS data collected over a region in Portugal to map 15 land-cover classes with 10 ha MMU. The map presents a thematic accuracy of 72.6 ± 3.7% at the 95% confidence level, which is approximately 10% higher than the per-pixel classification accuracy. The results show that segmentation of moderate-spatial resolution images and semantic map generalization can be used in an operational context to automatically produce land-cover maps with a predefined MMU over large areas.     

Estimating Siberian timber volume using MODIS and ICESat/GLAS

Geosciences Laser Altimeter System (GLAS) space LiDAR data are used to attribute a MODerate resolution Imaging Spectrometer (MODIS) 500 m land cover classification of a 10° latitude by 12° longitude study area in south-central Siberia. Timber volume estimates are generated for 16 forest classes, i.e., four forest cover types × four canopy density classes, across this 811,414 km² area and compared with a ground-based regional volume estimate. Two regional GLAS/MODIS timber volume products, one considering only those pulses falling on slopes ≤ 10° and one utilizing all GLAS pulses regardless of slope, are generated. Using a two-phase(GLAS-ground plot) sampling design, GLAS/MODIS volumes average 163.4 ± 11.8 m³/ha across all 16 forest classes based on GLAS pulses on slopes ≤ 10° and 171.9 ± 12.4 m³/ha considering GLAS shots on all slopes. The increase in regional GLAS volume per-hectare estimates as a function of increasing slope most likely illustrate the effects of vertical waveform expansion due to the convolution of topography with the forest canopy response. A comparable, independent, ground-based estimate is 146 m³/ha [Shepashenko, D., Shvidenko, A., and Nilsson, S. (1998). Phytomass (live biomass) and carbon of Siberian forests. Biomass and Bioenergy, 14, 21-31], a difference of 11.9% and 17.7% for GLAS shots on slopes ≤ 10° and all GLAS shots regardless of slope, respectively. A ground-based estimate of total volume for the entire study area, 7.46 × 10⁹ m³, is derived using Shepashenko et al.'s per-hectare volume estimate in conjunction with forest area derived from a 1990 forest map [Grasia, M.G. (ed.). (1990). Forest Map of USSR. Soyuzgiproleskhoz, Moscow, RU. Scale: 1:2,500,000]. The comparable GLAS/MODIS estimate is 7.38 × 10⁹ m³, a difference of less than 1.1%. Results indicate that GLAS data can be used to attribute digital land cover maps to estimate forest resources over subcontinental areas encompassing hundreds of thousands of square kilometers.     

Modelling and mapping potential hooded warbler (Wilsonia citrina) habitat using remotely sensed imagery

Modelling and mapping of hooded warbler (Wilsonia citrina) nesting habitat in forests of southern Ontario were conducted using Ikonos and Landsat data. The study began with an analysis of skyward hemispherical photography to determine canopy characteristics associated with nest sites. It showed that nest sites had significantly less overhead canopy cover and larger maximum gap size than in non-nest areas. These findings led to the hypothesis that brightness variability in high to moderate resolution remotely sensed imagery may be greater at nest sites than in non-nest areas due to larger shadows and greater shadow variability related to these gap characteristics. This was confirmed when, in addition to some spectral band brightness variables, several image texture and spectrally unmixed fraction (shadow, bare soil) variables were found to be significantly different for nest and non-nest sites in Ikonos and Landsat imagery. These significantly different variables were used in maximum likelihood classification (MLC) and logistic regression (LR) to produce maps of potential nesting habitat. Mapping was conducted with Ikonos and Landsat in a local area where most known nest sites occur, and regionally using Landsat data for almost all of the hooded warbler range in southern Ontario. For the local area mapping using Ikonos data, a posteriori probabilities for both the MLC and LR methods showed that about 62% of the nest sites set aside for validation had been classified with high probability (p > 0.70) in the nest class. MLC mapping accuracy was 70% for the validation nest sites and 87% of validation nest sites were within 10 m of classified nesting habitat, a distance approximately equivalent to expected positional error in the data. LR accuracy was slightly lower. Nest site MLC mapping accuracy in the local area using Landsat data was 87% but the map was much coarser due to the larger pixel size. Regional mapping with Landsat imagery produced lower classification accuracy due to high errors of commission for the habitat class. This resulted from a poor spatial distribution and low number of observations of nest sites throughout the region compared to the local area, while the non-nest site data distribution was too narrow, having been defined and assessed (using standard accepted methods) as areas with no ground shrubs. If either of these problems can be ameliorated, regional mapping accuracy may improve.     

A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States

Improved wildland fire emission inventory methods are needed to support air quality forecasting and guide the development of air shed management strategies. Air quality forecasting requires dynamic fire emission estimates that are generated in a timely manner to support real-time operations. In the regulatory and planning realm, emission inventories are essential for quantitatively assessing the contribution of wildfire to air pollution. The development of wildland fire emission inventories depends on burned area as a critical input. This study presents a Moderate Resolution Imaging Spectroradiometer (MODIS) - direct broadcast (DB) burned area mapping algorithm designed to support air quality forecasting and emission inventory development. The algorithm combines active fire locations and single satellite scene burn scar detections to provide a rapid yet robust mapping of burned area. Using the U.S. Forest Service Fire Sciences Laboratory (FiSL) MODIS-DB receiving station in Missoula, Montana, the algorithm provided daily measurements of burned area for wildfire events in the western U.S. in 2006 and 2007. We evaluated the algorithm's fire detection rate and burned area mapping using fire perimeter data and burn scar information derived from high resolution satellite imagery. The FiSL MODIS-DB system detected 87% of all reference fires > 4 km², and 93% of all reference fires > 10 km². The burned area was highly correlated (R² = 0.93) with a high resolution imagery reference burn scar dataset, but exhibited a large over estimation of burned area (56%). The reference burn scar dataset was used to calibrate the algorithm response and quantify the uncertainty in the burned area measurement at the fire incident level. An objective, empirical error based approach was employed to quantify the uncertainty of our burned area measurement and provide a metric that is meaningful in context of remotely sensed burned area and emission inventories. The algorithm uncertainty is ± 36% for fires 50 km² in size, improving to ± 31% at a fire size of 100 km². Fires in this size range account for a substantial portion of burned area in the western U.S. (77% of burned area is due to fires > 50 km², and 66% results from fires > 100 km²). The dominance of these large wildfires in burned area, duration, and emissions makes these events a significant concern of air quality forecasters and regulators. With daily coverage at 1-km² spatial resolution, and a quantified measurement uncertainty, the burned area mapping algorithm presented in this paper is well suited for the development of wildfire emission inventories. Furthermore, the algorithm's DB implementation enables time sensitive burned area mapping to support operational air quality forecasting.     

Assessment of forest cover in Russia by combining a wall-to-wall coarse-resolution land-cover map with a sample of 30 m resolution forest maps

The process of gathering land-cover information has evolved significantly over the last decade (2000–2010). In addition to this, current technical infrastructure allows for more rapid and efficient processing of large multi-temporal image databases at continental scale. But whereas the data availability and processing capabilities have increased, the production of dedicated land-cover products with adequate accuracy is still a prerequisite for most users. Indeed, spatially explicit land-cover information is important and does not exist for many regions. Our study focuses on the boreal Eurasia region for which limited land-cover information is available at regional level.

The main aim of this paper is to demonstrate that a coarse-resolution land-cover map of the Russian Federation, the ‘TerraNorte’ map at 230 m × 230 m resolution for the year 2010, can be used in combination with a sample of reference forest maps at 30 m resolution to correctly assess forest cover in the Russian federation.

First, an accuracy assessment of the TerraNorte map is carried out through the use of reference forest maps derived from finer-resolution satellite imagery (Landsat Thematic Mapper (TM) sensor). A sample of 32 sites was selected for the detailed identification of forest cover from Landsat TM imagery. A methodological approach is developed to process and analyse the Landsat imagery based on unsupervised classification and cluster-based visual labelling. The resulting forest maps over the 32 sites are then used to evaluate the accuracy of the forest classes of the TerraNorte land-cover map. A regression analysis shows that the TerraNorte map produces satisfactory results for areas south of 65° N, whereas several forest classes in more northern areas have lower accuracy. This might be explained by the strong reflectance of background (i.e. non-tree) cover.

A forest area estimate is then derived by calibration of the TerraNorte Russian map using a sample of Landsat-derived reference maps (using a regression estimator approach). This estimate compares very well with the FAO FRA exercise for 2010 (1% difference for total forested area). We conclude that the TerraNorte map combined with finer-resolution reference maps can be used as a reliable spatial information layer for forest resources assessment over the Russian Federation at national scale.     

Land-use land-cover classification analysis of Giba catchment using hyper temporal MODIS NDVI satellite images

Landsat-based land-use land-cover (LULC) mapping studies were previously conducted in Giba catchment, comprising an area of 4019 km². No attempt has been done to map LULC of this catchment through the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series data. This article is aimed to see whether time-series MODIS NDVI data set is applicable for LULC mapping of Giba catchment or not. MODIS NDVI data sets of the year 2010 were used for classification analysis. The original data were subjected to MODIS Reproduction Tool and stacking. The re-projected and stacked images were filtered using Harmonic Analysis of Time-Series filtering algorism to remove the effects of cloud and other noises. The MODIS NDVI data sets (16-day maximum value composite) were classified using the ISODATA clustering algorithm available under ERDAS IMAGINE software. A series of unsupervised classification runs were carried out with a pre-defined number of classes (5–24). From this classification, the optimal numbers of classes were determined to be eight after checking for average divergence analysis. The classification result became eight LULC classes namely: bare land, grass land, irrigated land, cultivated land, area closure, shrub land, bush land, and forest land with an overall accuracy of 87.7%. It was therefore concluded that MODIS NDVI time-series image is applicable for mapping large watersheds.     

Forest biomass mapping from lidar and radar synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed.In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R² of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and was used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R² of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar-SAR synergy was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.     

Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data

Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale.     

Analysis of multi-temporal SPOT NDVI images for small-scale land-use mapping

Land-use information is required for a number of purposes such as to address food security issues, to ensure the sustainable use of natural resources and to support decisions regarding food trade and crop insurance. Suitable land-use maps often either do not exist or are not readily available. This article presents a novel method to compile spatial and temporal land-use data sets using multi-temporal remote sensing in combination with existing data sources. Satellite Pour l'Observation de la Terre (SPOT)-Vegetation 10-day composite normalized difference vegetation index (NDVI) images (1998–2002) at 1km² resolution for a part of the Nizamabad district, Andhra Pradesh, India, were linked with available crop calendars and information about cropping patterns. The NDVI images were used to stratify the study area into map units represented by 11 distinct NDVI classes. These were then related to an existing land-cover map compiled from high resolution Indian Remote Sensing (IRS)-images (Liss-III on IRS-1C), reported crop areas by sub-district and practised crop calendar information. This resulted in an improved map containing baseline information on both land cover and land use. It is concluded that each defined NDVI class represents a varying but distinct mix of land-cover classes and that the existing land-cover map consists of too many detailed ‘year-specific’ features. Four groups of the NDVI classes present in agricultural areas match well with four categories of practised crop calendars. Differences within a group of NDVI classes reveal area specific variations in cropping intensities. The remaining groups of NDVI classes represent other land-cover complexes. The method illustrated in this article has the potential to be incorporated into remote sensing and Geographical Information System (GIS)-based drought monitoring systems.     

Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems

Large areas of the world's coastal marine environments remain poorly characterized because they have not been mapped with sufficient accuracy and at spatial resolutions high enough to support a wide range of societal needs. Expediting the rate of seafloor mapping requires the collection of multi-use datasets that concurrently address hydrographic charting needs and support decision-making in ecosystem-based management. While active optical and acoustic sensors have previously been compared for the purpose of hydrographic charting, few studies have evaluated the performance and cost effectiveness of these systems for providing benthic habitat maps. Bathymetric and intensity data were collected in shallow water (< 50 m depth) coral reef ecosystems using two conventional remote sensing technologies: (1) airborne Light Detection and Ranging (LiDAR), and (2) ship-based multibeam (MBES) Sound Navigation and Ranging (SoNAR). A comparative assessment using a suite of twelve metrics demonstrated that LiDAR and MBES were equally capable of discriminating seafloor topography (r = > 0.9), although LiDAR depths were found to be consistently shallower than MBES depths. The intensity datasets were not significantly correlated at a broad 4 × 5 km spatial scale (r = − 0.11), but were moderately correlated in flat areas at a fine 4 × 500 m spatial scale (r = 0.51), indicating that the LiDAR intensity algorithm needs to be improved before LiDAR intensity surfaces can be used for habitat mapping. LiDAR cost 6.6% less than MBES and required 40 fewer hours to map the same study area. MBES provided more detail about the seafloor by fully ensonifying high-relief features, by differentiating between fine and coarse sediments and by collecting data with higher spatial resolutions. Surface fractal dimensions and fast Fourier transformations emerged as useful methods for detecting artifacts in the datasets. Overall, LiDAR provided a more cost effective alternative to MBES for mapping and monitoring shallow water coral reef ecosystems (< 50 m depth), although the unique advantages of MBES may make it a more appropriate choice for answering certain ecological or geological questions requiring very high resolution data.     

Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing

Floodplain roughness parameterization is one of the key elements of hydrodynamic modeling of river flow, which is directly linked to exceedance levels of the embankments of lowland fluvial areas. The present way of roughness mapping is based on manually delineated floodplain vegetation types, schematized as cylindrical elements of which the height (m) and the vertical density (the projected plant area in the direction of the flow per unit volume, m^− 1) have to be assigned using a lookup table. This paper presents a novel method of automated roughness parameterization. It delivers a spatially distributed roughness parameterization in an entire floodplain by fusion of CASI multispectral data with airborne laser scanning (ALS) data. The method consists of three stages: (1) pre-processing of the raw data, (2) image segmentation of the fused data set and classification into the dominant land cover classes (KHAT = 0.78), (3) determination of hydrodynamic roughness characteristics for each land cover class separately. In stage three, a lookup table provides numerical values that enable roughness calculation for the classes water, sand, paved area, meadows and built-up area. For forest and herbaceous vegetation, ALS data enable spatially detailed analysis of vegetation height and density. The hydrodynamic vegetation density of forest is mapped using a calibrated regression model. Herbaceous vegetation cover is further subdivided in single trees and non-woody vegetation. Single trees were delineated using a novel iterative cluster merging method, and their height is predicted (R² = 0.41, rse = 0.84 m). The vegetation density of single trees was determined in an identical way as for forest. Vegetation height and density of non-woody herbaceous vegetation were also determined using calibrated regression models. A 2D hydrodynamic model was applied with the results of this novel method, and compared with a traditional roughness parameterization approach. The modeling results showed that the new method is well able to provide accurate output data. The new method provides a faster, repeatable, and more accurate way of obtaining floodplain roughness, which enables regular updating of river flow models.     

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the west African Sahel

Abstract

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.