Development of a forest canopy height estimation model using GLAS full waveform data over sloping terrain

ABSTRACT

The accurate estimation of forest canopy height is important because it leads to increased accuracy in the estimation of biomass, which is used in the study of the global carbon cycle, forest productivity, and climate change. However, there is no well-developed model that accurately estimates canopy height over undulating land. This paper describes the development of a back-propagation (BP) neural network model that estimates forest canopy height more accurately than other types of model. For modeling purposes, the land in the study area was classified as either plain (low relief areas) or hilly (high relief areas). Four different slope partition thresholds (5°, 10°, 15°, and 20°) were tested to determine the most suitable boundary value. ICESat-GLAS data provided by the Geoscience Laser Altimeter System (GLAS) aboard the Ice, Cloud and Land Elevation Satellite (ICESat), field survey data, and digital elevation model (DEM) data were collected and refined, and various parameters, including waveform extent and topographic index, were calculated. A BP neural network model was created to estimate forest canopy height. Two other models were also developed, one using the topographic index and the other using multiple linear regression, for comparison with the BP neural network model. After calibration, the three models were tested to assess the accuracy of the estimates. The results showed that the BP model estimated canopy height more accurately than the other two models. The use of a 10° boundary to partition the topography into low relief areas and high relief areas improved the accuracy of each model; using the 10° slope boundary, the coefficient of correlation r between the estimates given by the BP neural network model and the field-measured data increased from 0.89 to 0.95 and the Root Mean Square Error (RMSE) decreased from 1.01 to 0.73 m.     

Technical note GPS location data An aid to satellite image analyses of poorly mapped regions

Abstract

In inaccessible and highly heterogeneous areas where aerial photography and accurate up-to-date topographic maps are unavailable and where suitable features for ground-based navigation by triangulation are absent, accurately locating ground truth sites and then integrating the field data with digital imagery is often extremely difficult. The advent of the satellite global positioning system (GPS) offers a solution to this problem. Over a 6 month period a battery powered, solar recharged, backpack mounted GPS was used to collect the precise location data essential to the ground truth component of a Landsat Thematic Mapper (TM) landcover classification of the Ituri rain forest of northeastern Zaire. Three-dimensional locations were readily obtained in forest openings > 0·125 ha where the angle to the horizon did not exceed 50^° and canopy closure was less than 30 per cent. GPS location data are presently being used to reference a TM scene geographically and to assign pixels to appropriate landcover classes accurately.     

Improvement of forest type classification by SPOT HRV with 20 m mesh DTM

Abstract

For multispectral analysis of forest land in mountainous areas, the estimation of true reflectance without the terrain having an effect on the sensor response is indispensable. To study this subject, the authors carried out the following experiment. First, we made a precise digital terrain model (DTM) at an interval of 10 m for a test forest site covered with Lambertian-type crown surface. Analysing the forest land from the SPOT data with the precise DTM, we obtained a classification result of forest type about 20 per cent higher accuracy than the result without application of this method.     

Production of land-use and land-cover maps of central Guangdong Province of China from LANDSAT MSS imagery†

Abstract

The problems of mapping the land use and land cover of a large area of central Guangdong Province of China from LANDSAT MSS data were examined with reference to the manual and digital approaches. Based on intensive field study of a test site in the study area, we discovered the importance of topographic effects, slopes, seasons, cropping system and the intensity of land use in affecting the accuracy of the resultant maps. It was concluded that visual interpretation was essential in providing the level of reference required for the image interpreter to perform the digital analysis satisfactorily. In view of the coarse spatial resolution of the LANDSAT MSS data it is recommended that the most straightforward digital analysis involving the use of the supervised approach with the minimum Euclidean distance classification and an iterative selection of training areas be adopted for the land-use/land-cover mapping of the whole study area to achieve an accuracy of 80 per cent for eight level I and fifteen level II categories of the land-use and land-cover classification scheme.     

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.     

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.     

Geocoded digital videography for validation of land cover mapping in the Amazon basin

Four Validation Overflights for Amazon Mosaics (VOAM) aerial video surveys have been carried out in the Brazilian Amazon to provide ground verification for mapping of wetland cover with the Global Rain Forest Mapping (GRFM) Project JERS-1 (Japanese Earth Remote Sensing Satellite) mosaics of the Amazon basin. Surveys in 1995 and 1996, acquired with handheld analog camcorders from small aircraft, were timed to imaging of the GRFM low- and high-water mosaics, and limited to within 600 km of Manaus. For the 1997 and 1999 flights, digital camcorder systems were installed in the Bandeirante survey plane operated by Brazil's National Institute for Space Research. The VOAM97 and VOAM99 surveys circumscribed the Brazilian Amazon, documenting ground conditions at resolutions on the order of 1 m (wide-angle format) and 10 cm (zoom format) for wetlands, forests, savannas, and human-impacted areas. Global Positioning System (GPS) information encoded on the video audio track was extracted by mosaicking software that automatically generates geocoded digital mosaics from video clips. On the 1999 survey, a laser altimeter recorded profiles of terrain and vegetation canopy heights. A validation dataset was compiled from the videography for a portion of the GRFM mosaics extending 6° by 4° in longitude and latitude, using randomly selected points along flight lines. Other applications of the VOAM videography include acquisition of ground control points for image geolocation, creation of a high-resolution geocoded mosaic of a forest study area, forest biomass estimation, and rapid assessment of fire damage. Geocoded digital videography provides a cost-effective means of compiling high-resolution validation datasets for land cover mapping in remote, cloud-covered regions.     

Deriving surface albedo measurements from narrow band satellite data

Abstract

A target calibration procedure for obtaining surface albedo from satellite data is presented. The methodology addresses two key issues, the calibration of remotely-sensed, discrete wavelength, digital data and the derivation of an albedo measurement (defined over the solar short wave spectrum) from spectrally limited observations. Twenty-seven LANDSAT observations, calibrated with urban targets (building roof-tops and parking lots), are used to derive spatial and seasonal patterns of surface reflectance and albedo for four land cover types, city, suburb, farm and forest.     

Topographic effects on estimating net primary productivity of green coniferous forest in complex terrain using Landsat data: a case study of Yoshino Mountain,Japan

In mountainous areas, irregular terrain significantly affects spatial variations of climatic variables and the reflectance of pixels in remote sensing imagery. Consequently, the variations may affect the estimation of net primary productivity (NPP). The light-use efficiency (LUE) model is used to analyse topographic influence on NPP by evaluating topographic effects on primary input data to the model, including both Normalized Difference Vegetation Index (NDVI) and climatic data. A typical green coniferous forest in Yoshino Mountain, Japan, was employed as the study area. The results show that the average NPP is significantly increased after removing topographic influences on NDVI; the average NPP has a relatively minimal change when only topographic effects on climatic data are considered. When both topographic effects on NDVI and climatic data are considered, the average NPP is 1.80 kg m^?2 yr^?1, which is very similar to the ground measurement result of 1.74 kg m^?2 yr^?1.     

Radiometric slope correction for forest biomass estimation from SAR data in the Western Sayani Mountains,Siberia

We investigated the possibility of using multiple polarization (SIR-C) L-band data to map forest biomass in a mountainous area in Siberia. The use of a digital elevation model (DEM) and a model-based method for reducing terrain effects was evaluated. We found that the available DEM data were not suitable to correct the topographic effects on the SIR-C radar images. A model-based slope correction was applied to an L-band cross-polarized (hv) backscattering image and found to reduce the topographic effect. A map of aboveground biomass was produced from the corrected image. The results indicated that multipolarization L-band synthetic aperture radar (SAR) data can be useful for estimation of total aboveground biomass of forest stands in mountainous areas.     

遥感影像地形与大气校正系统设计与实现

遥感影像地形和大气校正是提高定量化遥感数据处理精度的重要因素。目前的数据处理软件系统集成了一些地形和大气校正算法,但在应用中还存在不能获取重要的地形参数(如阴影因子、天空可视因子等),需提供精准DEM和校正方法基于朗伯体地表假设等问题。为应对遥感专业用户需求,设计并实现了遥感影像地形与大气校正软件系统,用以对影像进行地形辐射校正、获取DEM数据和相关地形参数、地形与大气校正等。介绍了系统的功能模块设计并展示了系统的原型版本,并应用系统中的地形和大气校正方法获取了HJ/CCD影像和Landsat/TM影像的反射率。校正结果表明:该系统中的BRDF模型能够有效消除地形影响。系统的实现可以为遥感科学研究和应用提供支撑。     

Application of digital terrain data to quantify and reduce the topographic effect on Landsat data

Integration of Landsat multispectral scanner (MSS) data with 30 m U.S. Geological Survey (USGS) digital terrain data was undertaken to quantify and reduce the topographic effect on imagery of a forested mountain-ridge test site in central Pennsylvania. Four methods for reducing the topographic effect were compared; band ratioing and three radiance models. Band ratioing did not eliminate the topographic effect due to the lack of variation in MSS4 radiances. Of the three radiance models applied to the data, the lambertian and modified lambertian models increased the topographic effect. The non-lambertian model considerably reduced (86 per cent) the topographic effect in the Landsat data. The study demonstrates that high quality digital terrain data as provided by the USGS can be used to enhance the utility of multispectral satellite data.     

Performance analysis of image processing algorithms for classification of natural vegetation in the mountains of Southern California

Abstract

The Earth's forests fix carbon from the atmosphere during photosynthesis. Scientists are concerned that massive forest removals may promote an increase in atmospheric carbon dioxide, with possible global warming and related environmental effects. Space-based remote sensing may enable the production of accurate world forest maps needed to examine this concern objectively. To test the limits of remote sensing for large-area forest mapping, we use LANDSAT data acquired over a site in the forested mountains of southern California to examine the relative capacities of a variety of popular image processing algorithms to discriminate different forest types. Results indicate that certain algorithms are best suited to forest classification. Differences in performance between the algorithms tested appear related to variations in their sensitivities to spectral variations caused by background reflectance, differential illumination, and spatial pattern by species. Results emphasize the complexity between the land-cover regime, remotely sensed data and the algorithms used to process these data.     

Generation of a DTM and building detection based on an MPF through integrating airborne lidar data and aerial images

This study presents an approach that uses airborne light detection and ranging (lidar) data and aerial imagery for creating a digital terrain model (DTM) and for extracting building objects. The process of creating the DTM from lidar data requires four steps in this study: pre-processing, segmentation, extraction of ground points, and refinement. In the pre-processing step, raw data are transformed to raster data. For segmentation, we propose a new mean planar filter (MPF) that uses a 3 × 3 kernel to divide lidar data into planar and nonplanar surfaces. For extraction of ground points, a new method to extract additional ground points in forest areas is used, thus improving the accuracy of the DTM. The refinement process further increases the accuracy of the DTM by repeated comparison of a temporary DTM and the digital surface model. After the DTM is generated, building objects are extracted via a proposed three-step process: detection of high objects, removal of forest areas, and removal of small areas. High objects are extracted using the height threshold from the normalized digital surface model. To remove forest areas from among the high objects, an aerial image and normalized digital surface model from the lidar data are used in a supervised classification. Finally, an area-based filter eliminates small areas, such as noise, thus extracting building objects. To evaluate the proposed method, we applied this and three other methods to five sites in different environments. The experiment showed that the proposed method leads to a notable increase in accuracy over three other methods when compared with the in situ reference data.     

Determining the rate of forest conversion in Mato Grosso,Brazil, using Landsat MSS and AVHRR data

Abstract

AVHRR-LAC thermal data and Landsat MSS and TM spectral data were used to estimate the rate of forest clearing in Mato Grosso, Brazil, between 1981 and 1984. The Brazilian state was stratified into forest and non-forest. A list sampling procedure was used in the forest stratum to select Landsat MSS scenes for processing based on estimates of fire activity in the scenes. Fire activity in 1984 was estimated using AVHRR-LAC thermal data. Slate-wide estimates of forest conversion indicate that between 1981 and 1984, 353966 ha ±77 000 ha (0·4 percent of the state area) were converted per year. No evidence of reforestation was found in this digital sample. The relationship between forest clearing rate (based on MSS-TM analysis)and fire activity (estimated using AVHRR data)was noisy (R²= 0·41). The results suggest that AVHRR data may be put to better use as a stratification tool rather than as a subsidiary variable in list sampling.     

Evaluation of LANDSAT and airborne multispectral data and aerial photographs for mapping forest features and phenomena in a part of the Godavari basin

The automatic interpretation of multispectral digital data obtained from LANDSAT as well as from an airborne multispectral scanner using an interactive computer system and visual interpretation of colour composites of LANDSAT imagery and aerial photographs of a dry deciduous forest tract were used for evaluating the discrimination capabilities of each technique and for comparative evaluation. While visual interpretation of LANDSAT imagery could give only general information, such as contiguity of vegetation cover, digital analysis of the same yielded more detailed information, such as teak-bearing and non-teak-bearing regions. The analysis of airborne multispectral data, in the present state of the art, for performing forest surveys and making maps is limited. Aerial photographs are very useful for mapping forest land features and stock, which can be done more reliably than could be done by ground surveys. Infrared photographs show better promise in mapping forest features. The integration of multitemporal data and the incorporation of digitized additional information into the data stream for the improvement of digital analysis are suggested. Acquisition of data including aerial photographs for general surveys during a period prior to leaf fall in a deciduous forest is also recommended.     

A georeferenced LANDSAT digital database for forest insect-damage assessment

A joint research project involving NASA/Goddard Space Flight Center and the Pennsylvania Bureau of Forestry/Division of Forest Pest Management demonstrated the utility of LANDSAT data for assessing forest insect damage. A major effort within the project was the creation of a map-registered, statewide LANDSAT digital database for Pennsylvania. The database, developed and implemented on Pennsylvania State University computers, incorporates LANDSAT imagery, a LANDSAT-derived forest resource map and digitized data layers depicting Forest Pest Management district boundaries and county boundaries. A user-friendly data management system was also developed to provide an interface between the various layers of information within the database and image-analysis software. This system ensures that an automated assessment of defoliation damage can be conducted and summarized by geographic area or jurisdiction of interest     

Sensitivity of space-borne SAR data to forest parameters over sloping terrain. Theory and experiment

Recently, SAR data proved to be useful for the retrieval of forest biomass. However, the effects of terrain slope must be addressed towards the generalization of biomass retrieval for varied forest and environmental conditions. To this aim, we developed experimental and theoretical approaches allowing the study of multi-frequency/multi-polarization forest backscatter of a given forest type, as a function of forest parameters and SAR local incidence angle over the relief. The experimental results showed that the sensitivity of SAR data to biomass was similar to that obtained over a flat terrain, only if the backscatter data were calibrated for slope effects. Moreover, the backscatter must also be corrected for its angular decrease, which can be removed using a simple angular model developed under assumptions of theoretical equations. The highest correlation of corrected backscatter with forest parameters related to aboveground biomass (such as stand age and bole volume) was achieved at L-HV 55° (R ²     

SRTM DEM accuracy assessment over vegetated areas in Norway

Results from the Shuttle Radar Topography Mission (SRTM) are presented. The SRTM C‐band and X‐band digital elevation models (DEMs) are evaluated with regard to elevation accuracies over agricultural fields, forest areas and man‐made features in Norway. High‐resolution digital maps and satellite images are used as background data. In general, many terrain details can be observed in the SRTM elevation datasets. The elevation accuracy (90% confidence level) of the two SRTM systems is estimated to less than 6.5 m for open agricultural fields and less than 11 m considering all land surface covers. This is better than specifications. Analysis of dense Norwegian forest stands shows that the SRTM system will produce elevation data that are as much as 15 m higher than the ground surface. The SRTM DEM products will therefore partly indicate canopy elevations in forested areas. We also show that SRTM data can be used to update older DEMs obtained from other sources, as well as estimating the volume of rock removed from large man‐made gravel pits.