Sun Canopy Sensor Research Articles

Demi-decadal land use land cover change analysis of Mizoram, India, with topographic correction using machine learning algorithm.

Mizoram (India) is part of UNESCO's biodiversity hotspots in India that is primarily populated by tribes who engage in shifting agriculture. Hence, the land use land cover (LULC) pattern of the state is frequently changing. We have used Landsat 5 and 8 satellite images to prepare LULC maps from 2000 to 2020 in every 5years. The atmospherically corrected images were pre-processed for removal of cloud cover and then classified into six classes: waterbodies, farmland, settlement, open forest, dense forest, and bare land. We applied four machine learning (ML) algorithms for classification, namely, random forest (RF), classification and regression tree (CART), minimum distance (MD), and support vector machine (SVM) for the images from 2000 to 2020. With 80% training and 20% testing data, we found that the RF classifier works best with the most accuracy than other classifiers. The average overall accuracy (OA) and Kappa coefficient (KC) from 2000 to 2020 were 84.00% and 0.79 when the RF classifier was used. When using SVM, CART, and MD, the average OA and KC were 78.06%, 0.73;78.60%, 0.72;and73.32%, 0.65, respectively. We utilised three methods of topographic correction, namely, C-correction, SCS (sun canopy sensor) correction, and SCS + C correction to reduce the misclassification due to shadow effects. SCS + C correction worked best for this region; hence, we prepared LULC maps on SCS + C corrected satellite image. Hence, we have used RF classifier for LULC preparation demi-decadal from 2000 to 2020. The OA for 2000, 2005, 2010, 2015, and 2020 was found to be 84%, 81%, 81%, 85%, and 89%, respectively, using RF. The dense forest decreased from 2000 to 2020 with an increase in open forest, settlement, and agriculture; nevertheless, when Farmland was low, there was an increase in the barren land. The results were significantly improved with the topographic correction, and misclassification was quite less.

Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier

Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development.

HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence

Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy's structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun-canopy-sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red (F687) and far-red (F760) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m-2 sr-1 nm-1. The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m-2 sr-1 nm-1, whereas vegetation targets had a mean F687 of 1.13 and F760 of 1.96 mW m-2 sr-1 nm-1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F687 and F760; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants' photosynthetic capacity.

Evaluation of Topographic Correction Models Based on 3-D Radiative Transfer Simulation

The timely and accurate assessment of changes in mountain vegetation biomass and other parameters is of great importance to mountain ecosystem conservation. With the rapid development of remote sensing technology, hyperspectral remote sensing images have facilitated the large-scale and long-time series monitoring of environmental changes in mountainous areas. However, topographic effects cause remote sensing images of mountainous areas to be prone to spectral variations within the same land cover and spectral confusion among different land covers. This phenomenon seriously affects the accuracy of remote sensing inversions and hinders the development and application of remote sensing in mountainous areas. Numerous scholars have established various topographic correction models (TCMs) to eliminate the influence of topographic effects. Comparative evaluation of the performance of different TCMs allows us to better understand their characteristics. Most previous evaluation studies have directly applied in remote sensing images, which were limited by the changing conditions of the study area. Therefore, this letter used computer simulations to controllably evaluate six popular TCMs on hyperspectral images. The results showed that their performance varied with the spectral band, and overall, the best performance was achieved by the C correction model, followed by the sun-canopy-sensor (SCS) + C model. This letter provides a basis for the optimal selection of TCMs in complex terrains.

An Alternative Method for the Generation of Consistent Mapping to Monitoring Land Cover Change: A Case Study of Guerrero State in Mexico

Land cover is crucial for ecosystems and human activities. Therefore, monitoring land cover changes has become relevant in recent years. This study proposes an alternative method based on conventional change detection techniques combined with maximum likelihood (MaxLike) supervised classification of satellite images to generate consistent Land Use/Land Cover (LULC) maps. The novelty of this method is that the supervised classification is applied in an earlier stage of change detection exclusively to identified dynamics zones. The LULC categories of the stable zones are acquired from an initial date’s previously elaborated base map. The methodology comprised the use of Landsat images from 2011 and 2016, applying the Sun Canopy Sensor (SCS + C) topographic correction model enhanced through the classification of slopes, using derived topographic corrected images with NDVI, and employing Tasseled Cap (TC) Brightness-Greenness-Wetness indices and Principal Components (PCs). The study incorporated a comparative analysis of the consistency of the LULC mapping, which is generated based on control areas. The results show that the proposed method, although slightly laborious, is viable and fully automatable. The generated LULC map is accurate and robust and achieves a Kappa concordance index of 87.53. Furthermore, the boundary consistency was visually superior to the conventional classified map.

A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

Airborne hyper-spectral imaging has been proven to be an efficient means to provide new insights for the retrieval of biophysical variables. However, quantitative estimates of unbiased information derived from airborne hyperspectral measurements primarily require a correction of the anisotropic scattering properties of the land surface depicted by the bidirectional reflectance distribution function (BRDF). Hitherto, angular BRDF correction methods rarely combined viewing-illumination geometry and topographic information to achieve a comprehensive understanding and quantification of the BRDF effects. This is in particular the case for forested areas, frequently underlaid by rugged topography. This paper describes a method to correct the BRDF effects of airborne hyperspectral imagery over forested areas overlying rugged topography, referred in the reminder of the paper as rugged topography-BRDF (RT-BRDF) correction. The local viewing and illumination geometry are calculated for each pixel based on the characteristics of the airborne scanner and the local topography, and these two variables are used to adapt the Ross-Thick-Maignan and Li-Transit-Reciprocal kernels in the case of rugged topography. The new BRDF model is fitted to the anisotropy of multi-line airborne hyperspectral data. The number of pixels is set at 35,000 in this study, based on a stratified random sampling method to ensure a comprehensive coverage of the viewing and illumination angles and to minimize the fitting error of the BRDF model for all bands. Based on multi-line airborne hyperspectral data acquired with the Chinese Academy of Forestry’s LiDAR, CCD, and Hyperspectral system (CAF-LiCHy) in the Pu’er region (China), the results applying the RT-BRDF correction are compared with results from current empirical (C, and sun-canopy-sensor (SCS) adds C (SCS+C)) and semi-physical (SCS) topographic correction methods. Both quantitative assessment and visual inspection indicate that RT-BRDF, C, and SCS+C correction methods all reduce the topographic effects. However, the RT-BRDF method appears more efficient in reducing the variability in reflectance of overlapping areas in multiple flight-lines, with the advantage of reducing the BRDF effects caused by the combination of wide field of view (FOV) airborne scanner, rugged topography, and varying solar illumination angle over long flight time. Specifically, the average decrease in coefficient of variation (CV) is 3% and 3.5% for coniferous forest and broadleaved forest, respectively. This improvement is particularly marked in the near infrared (NIR) region (i.e., >750 nm). This finding opens new possible applications of airborne hyperspectral surveys over large areas.

Band-Based Best Model Selection for Topographic Normalization of Normalized Difference Vegetation Index Map

Topographic effect in remote sensing images is severe in high mountainous areas. Efficiently to reduce the effects, several topographic normalization models have been proposed. Since the performance of the models is largely dependent on the spectral band and land surface type, the best performance model can vary from image to image in an area as well as from band to band in an image. The normalized difference vegetation index (NDVI) map has been widely used for the vegetation monitoring and assessment. An efficient reduction of the topographic effect in the NDVI map must be required for the spatial analysis of the vegetation monitoring and assessment. In this paper, we propose an efficient method to select the best topographic normalization model in each band to reduce the topographic effect of NDVI maps. The histogram structural similarity (HSSIM) index was used for the model selection because the index allows to select the best model in each band of an image. Five topographic normalization models were used for the test, which include the sun-canopy-sensor (SCS), statistical-empirical, C-correction, Minnaert, and Minnaert + SCS. The performance of the proposed method was validated by using two different season Landsat-8 OLI images including the forest area of northern Malaysia. The standard deviations of the two NDVI maps generated from the test images were reduced by about 53.1% and 28.6% after correction in profile analysis. The coefficient of determination (R 2 ) between the two different NDVI maps increased from 0.626 to 0.759. It indicates that the proposed method effectively reduced the topographic effect of the NDVI maps. This result implies that the proposed method can work well in the topographic normalization. Furthermore, the proposed method would be successfully applied to index maps including the normalized difference snow index (NDSI), normalized difference water index (NDWI), etc.

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

Spectral reflectance distortions caused by terrain and solar illumination seriously reduce the accuracy of mapping forest carbon density, especially in mountainous regions. Many models have been developed for mitigating or eliminating the terrain effects on the quality of remote sensing images in hilly and mountainous areas. However, these models usually use global parameters, which may lead to overcorrections for regions with poor illumination and steep slopes. In this study, we present a local parameter estimation (LPE) method based on a pixel-moving window for topographic correction (TC), which can be considered as a general optimization framework for most semiempirical TC models. We set seven kernel sizes for the presented framework, which are 15 pixels, 25 pixels, 50 pixels, 100 pixels, 250 pixels, 500 pixels, and 1000 pixels, respectively. The proposed method was then applied to four traditional TC models, Minnaert (MIN), C Correction (CC), Sun Canopy Sensor + C (SCSC) and Statistical Empirical Correction (SEC), to form four new TC models. These new models were used to estimate forest carbon density of a mountainous area in Southern China using field plot data and a Landsat 8 image. Four evaluation methods, including correlation analysis, the stability of land covers, comparison of reflectance between sunlit and shaded slopes, and accuracy assessment of forest carbon density, were employed to evaluate the contributions of moving window sizes, and assess the performance of the TC models for forest carbon density estimation. The results show that the four TC models with LPE perform much better than the traditional TC models in reducing the topographic effects and improving the estimation accuracy of forest carbon density for the study area. Among the traditional TC models, SEC performs slightly better than SCSC, CC, and MIN. Therefore, the SEC-based model with LPE, that is, LPE-SEC, gets greater R2 and smaller relative RMSE values in estimating forest carbon density than other models. Moreover, all the means of the predicted forest carbon density values fall in the confidence interval of the validation data at a significant level of 0.05. Overall, this study implies that the proposed method with LPE provides great potential to improve the performance of TC and forest carbon density estimation for the study area. It is expected that the improved TC method can be applied to other mountainous areas to improve the quality of remotely sensed images.

Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data

Recently, the United States Geological Survey (USGS) has released a new dataset, called Landsat Analysis Ready Data (ARD), which is designed specifically for facilitating time series analysis. In this study, we evaluated the temporal consistency of this new dataset and recommended several processing streamlines for improving data consistency. Specifically, we examined the impacts of data resampling, cloud/cloud shadow detection, Bidirectional Reflectance Distribution Function (BRDF) correction, and topographic correction on the temporal consistency of the Landsat Time Series (LTS). We have four major observations. First, single-resampled data (ARD) are generally more consistent than double-resampled data (re-projected Collection 1 data), but the difference is very minor. Second, the improved cloud and cloud shadow detection approach (e.g., Fmask 4.0 vs. 3.3) moderately increased data consistency. Third, BRDF correction contributed the most in making LTS consistent. Finally, we corrected the topographic effects by using several widely used algorithms, including Sun-Canopy-Sensor (SCS), a semiempirical SCS (SCS+C), and Illumination Correction (IC) algorithms, however they were found to have very limited or even negative impacts on the consistency of LTS. Therefore, we recommend using Landsat ARD with the improved cloud and cloud shadow detection approach (Fmask 4.0), and with BRDF correction for routine time series analysis.

Effects of Topographic Normalization on the Relationship Between Tropical Forest Biomass and Landsat TM Images

The tropical forest system of Xishuangbanna is of important value in global forest system, because it is the only green island in the tropical desert belt and the largest intact best-preserved tropical rain forest in China. Discovering the relationship between the tropical forest biomass of Xishuangbanna and Landsat TM data is very important for depicting the tropical forest biomass by using remote sensing. The study aimed to (1) discover the relationship between tropical forest biomass and Landsat Thematic Mapper (TM) images in Xishuangbanna Prefecture, Yunnan Province, China, by using forest permanent plot data and correlation analysis; (2) explore how to improve the relationship by using topographic correction models such as the Lambert cosine correction model (LCCM) and the sun–canopy–sensor topographic correction model (SCSTCM). The results revealed the tropical forest biomass characteristics and the relationship characteristics between the tropical forest biomass and Landsat TM data and its improvement by using LCCM and SCSTCM. The results are beneficial to monitoring the tropical forest biomass variation in Xishuangbanna.

Topographic Correction to Landsat Imagery through Slope Classification by Applying the SCS + C Method in Mountainous Forest Areas

The aim of the topographic normalization of remotely sensed imagery is to reduce reflectance variability caused by steep terrain and thus improve further processing of images. A process of topographic correction was applied to Landsat imagery in a mountainous forest area in the south of Mexico. The method used was the Sun Canopy Sensor + C correction (SCS + C) where the C parameter was differently determined according to a classification of the topographic slopes of the studied area in nine classes for each band, instead of using a single C parameter for each band. A comparative, visual, and numerical analysis of the normalized reflectance was performed based on the corrected images. The results showed that the correction by slope classification improves the elimination of the effect of shadows and relief, especially in steep slope areas, modifying the normalized reflectance values according to the combination of slope, aspect, and solar geometry, obtaining reflectance values more suitable than the correction by non-slope classification. The application of the proposed method can be generalized, improving its performance in forest mountainous areas.

Evaluating and comparing performances of topographic correction methods based on multi-source DEMs and Landsat-8 OLI data

ABSTRACTTopographic correction is a crucial and challenging step in interpreting optical remote-sensing images of extremely complex terrain environments due to the lack of universally suitable correction algorithms and digital elevation models (DEMs) of adequate resolution and quality. The free availability of open source global DEMs provides an unprecedented opportunity to remove topographic effects associated with remote-sensing data in remote and rugged mountain terrains. This study evaluated the performances of seven topographic correction methods including C-correction (C), Cosine C-correction (CC), Minnaert correction (M), Sun–canopy–sensor (SCS) correction (S), SCS+C-correction (SC), Teillet regression correction (TR), and the Terrain illumination correction model (TI) based on multi-source DEMs (local topographic map, Shuttle Radar Topography Mission (SRTM) DEM filled-finished A/B and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data sets) and Landsat-8 Operational Land Imager (OLI) data using visual and statistical evaluation strategies. Overall, these investigated topographic correction methods removed topographic effects associated with Landsat-8 OLI data to varying degrees. However, the performances of these methods generally depend on the use of different DEMs and evaluation strategies. Among these correction methods, the SCS+C-correction performed best and was less sensitive to the use of different DEMs. The performances of topographic corrections based on free and open-access DEMs were generally better than or comparable to those based on local topographic maps. In particular, the topographic correction performance was greatly improved using the SRTM filled-finished B (FFB) data set with a resampling scheme based on the average value within a 3 × 3 pixel window. Nevertheless, further quantitative investigation is needed to determine the relative importance of DEMs and evaluation strategies used to select topographic correction methods.

Modified enhanced vegetation index for reducing topographic effects

Monitoring the environmental status of mountainous or hilly areas is very important for their great influence on the global ecosystem and humanity. The enhanced vegetation index (EVI) has been widely used in environmental monitoring. It can reduce background and atmospheric noise via its feedback-based format. However, the application of EVI in mountainous areas will be limited, because EVI is greatly affected by topographic effects as its soil adjustment index is not in a band ratio format. To moderate the topographic effects on EVI, we modified the EVI by changing the soil adjustment index from a constant to a variable related to the incidence angle. In the evaluation of the modified EVI, three other well-known topographic correction methods, Sun-canopy-sensor (SCS), SCS with C-correction (SCS+C), and modified Minnaert (MM), were used for comparison. The results indicated that the modified EVI and SCS+C perform better than MM and SCS by visual comparison. Quantitatively, modified EVI, which has an effect similar to SCS+C in the low incidence angle regions, largely decreased the standard deviation of the same land features and the correlation between EVI and the cosine of the incidence angle. When the incidence angle exceeds 90 deg, SCS+C and other two topographic correction methods caused overcorrections. However, modified EVI solved this problem well due to its smaller increasing curvature than other three topographic correction methods. Moreover, compared to SCS+C, modified EVI better preserved the characters of land surface features.

不同林分郁闭度与遥感数据的相关性分析

Correlation analysis of canopy density with remote sensing data for different forest stand is basis for estimating canopy density using remote sensing,which is an important field of forest remote sensing.The relationships of the canopy density with Landsat Thematic Mapper(TM,which includes seven bands represented as TM1、TM2、TM3、TM4、TM5、TM6、and TM7) data for different forest stand were explored in Shimian County of Sichuan Province of P.R.of China,and how they were influenced by topographically correcting TM using the Lambert Cosine Correction(LCC) model and the Sun Canopy Sensor(SCS) model was also studied here.Firstly,the topographic database and the forest resource GIS database whose data were acquired in1994 in field were created.Secondly,Landsat TM data acquired on June 26,1994 weregeometrically corrected by using topographic maps,and matched well with the forest resource database.Thirdly,TM-LCC and TM-SCS were respectively obtained by topographically correcting TM using LCC model and SCS model.Fourthly,the mean and standard deviation of each band of TM,TM-LCC and TM-SCS for each forest sub-compartment were calculated by overlaying the forest resource GIS data with each band of TM,TM-LCC and TM-SCS,and were added into the attribute table of the forest resource database.1194 sub-compartment samples of relatively lower standard deviation were selected from the forest resource database.Finally,the samples were stratified into eight forest stands according to their dominant tree,and the correlation coefficients of canopy density with each band of TM,TM-LCC and TM-SCS were calculated for each forest stand.It was shown that the correlation coefficients differ along with different band and different forest stand.Correlation coefficients of canopy density with TM2,TM3,TM4,TM5 and TM7 for Tsuga chinensis,with TM4,TM5 and TM7 for Abies fabri,and with TM1 for Picea asperata stand were significant at the 99% level of confidence.The highest is the correlation coefficient of canopy density with TM5 for Tsuga chinensis stand,which is-0.324.The correlation coefficient of canopy density with TM1 for Tsuga chinensis stand is significant at the 95% level of confidence.The correlation coefficients of canopy density with each band of Landsat TM for Betula,Quercus,Alnus cremastogyna,soft broadleave and Pinus yunnanensis stand were not significant at the 95% level of confidence.The correlation coefficients of canopy density with TM4 and TM5for Tsuga chinensi,Abies fabri and soft broadleave were enhanced by topographically correcting TM4 and TM5 using the LCC model,which are respectively-0.394,-0.374,-0.209,-0.210,0.545 and0.577,and significant at the 99% level of confidence.The correlation coefficient of canopy density with TM7 for soft broadleave was enhanced by topographically correcting TM7 using the LCC model(from 0.051 to 0.513),and significant at the 99% level of confidence.The correlation coefficients of canopy density with TM4 and TM5 for Abies fabri was enhanced by topographically correcting TM4 and TM5 using CSC model(from-0.170 to-0.213 and from-0.181 to-0.207),and significant at the 99% level of confidence.The correlation coefficients of canopy density with Landsat TM for Betula,Quercus,Alnus cremastogyna,Pinus yunnanensis and Picea asperata were not significantly enhanced by topographically correcting Landsat TM using the models of LCC and SCS.The study is of important value to stand canopy density remote sensing.

A sun–crown–sensor model and adapted C-correction logic for topographic correction of high resolution forest imagery

Canopy shadowing mediated by topography is an important source of radiometric distortion on remote sensing images of rugged terrain. Topographic correction based on the sun–canopy–sensor (SCS) model significantly improved over those based on the sun–terrain–sensor (STS) model for surfaces with high forest canopy cover, because the SCS model considers and preserves the geotropic nature of trees. The SCS model accounts for sub-pixel canopy shadowing effects and normalizes the sunlit canopy area within a pixel. However, it does not account for mutual shadowing between neighboring pixels. Pixel-to-pixel shadowing is especially apparent for fine resolution satellite images in which individual tree crowns are resolved. This paper proposes a new topographic correction model: the sun–crown–sensor (SCnS) model based on high-resolution satellite imagery (IKONOS) and high-precision LiDAR digital elevation model. An improvement on the C-correction logic with a radiance partitioning method to address the effects of diffuse irradiance is also introduced (SCnS+C). In addition, we incorporate a weighting variable, based on pixel shadow fraction, on the direct and diffuse radiance portions to enhance the retrieval of at-sensor radiance and reflectance of highly shadowed tree pixels and form another variety of SCnS model (SCnS+W). Model evaluation with IKONOS test data showed that the new SCnS model outperformed the STS and SCS models in quantifying the correlation between terrain-regulated illumination factor and at-sensor radiance. Our adapted C-correction logic based on the sun–crown–sensor geometry and radiance partitioning better represented the general additive effects of diffuse radiation than C parameters derived from the STS or SCS models. The weighting factor Wt also significantly enhanced correction results by reducing within-class standard deviation and balancing the mean pixel radiance between sunlit and shaded slopes. We analyzed these improvements with model comparison on the red and near infrared bands. The advantages of SCnS+C and SCnS+W on both bands are expected to facilitate forest classification and change detection applications.

Evaluation of Different Topographic Corrections for Landsat TM Data by Prediction of Foliage Projective Cover (FPC) in Topographically Complex Landscapes

The reflected radiance in topographically complex areas is severely affected by variations in topography; thus, topographic correction is considered a necessary pre-processing step when retrieving biophysical variables from these images. We assessed the performance of five topographic corrections: (i) C correction (C), (ii) Minnaert, (iii) Sun Canopy Sensor (SCS), (iv) SCS + C and (v) the Processing Scheme for Standardised Surface Reflectance (PSSSR) on the Landsat-5 Thematic Mapper (TM) reflectance in the context of prediction of Foliage Projective Cover (FPC) in hilly landscapes in north-eastern Australia. The performance of topographic corrections on the TM reflectance was assessed by (i) visual comparison and (ii) statistically comparing TM predicted FPC with ground measured FPC and LiDAR (Light Detection and Ranging)-derived FPC estimates. In the majority of cases, the PSSSR method performed best in terms of eliminating topographic effects, providing the best relationship and lowest residual error when comparing ground measured FPC and LiDAR FPC with TM predicted FPC. The Minnaert, C and SCS + C showed the poorest performance. Finally, the use of TM surface reflectance, which includes atmospheric correction and broad Bidirectional Reflectance Distribution Function (BRDF) effects, seemed to account for most topographic variation when predicting biophysical variables, such as FPC.

Assessment of different topographic correction methods in ALOS AVNIR-2 data over a forest area

Because the removal of topographic effects is one the most important pre-processing steps when extracting information from satellite images in digital Earth applications, the problem of differential terrain illumination on satellite imagery has been investigated for at least 20 years. As there is no superior topographic correction method applicable to all areas and all images, a comparison of topographic normalization methods in different regions and images is necessary. In this study, common topographic correction methods were applied on an ALOS AVNIR-2 image of a rugged forest area, and the results were evaluated through different criteria. The results show that the simple correction methods [Cosine, Sun-Canopy-sensor (SCS), and Minnaert correction] are inefficient in exceptionally rough forests. Among the improved correction methods (SCS+C, modified Minnaert, and pixel-based Minnaert), the best result was achieved using a pixel-based Minnaert approach in which a separate correction factor in various slope angles is used. Thus, this method should be considered for topographic correction, especially in forests with severe topography.

Building a Forward-Mode Three-Dimensional Reflectance Model for Topographic Normalization of High-Resolution (1–5 m) Imagery: Validation Phase in a Forested Environment

The aim of the topographic normalization of remotely sensed imagery (TNRSI) is to reduce reflectance variability caused by steep terrain and, subsequently, to improve land-cover classification. Recently, multiple-forward-mode (FM) (MFM) reflectance models for topographic normalizations of medium-resolution (20-30 m) satellite imagery have improved the classification of forested covers with respect to more conventional topographic corrections. We propose an FM 3-D reflectance (FM3DR) model, based on the Discrete Anisotropic Radiative Transfer simulator, for the topographic normalization of high-resolution (1-5 m) imagery. The feasibility of this approach was first verified on real IKONOS imagery for three forest types within major biomes (oak, pine, and high tropical forest) in Mexico. Next, we formalized the topographic normalization performance index and variability as relevant criteria to test TNRSI across incident angles in terms of maximum likelihood classification effectiveness. The FM3DR model outperformed five previously published topographic corrections (cosine, Minnaert, sun-canopy-sensor (SCS), Civco two-stage, and slope matching corrections), and image-based statistical strategies (Civco two-stage and slope matching corrections) tended to perform better than more analytical strategies (cosine, Minnaert, and SCS corrections). An asset of this approach versus former models is the realistic account of terrain-related variation of understory and crown cover within a cover type. On top of that, once validated across forest types, the model is sufficient for the application of a full MFM 3-D reflectance-based topographic normalization without additional field measurement.

Modification of the Sun-canopy-sensor Topographic Correction Model by Introduction of a Minnaert Constant

The topographic impact on land surface reflectance is an important factor influencing the practical use of remotely sensed data in forested areas. The sun-canopy-sensor (SCS) topographic correction model has been developed to reduce the topographic effect in forested regions, which is more appropriate than terrain-based methods because it considers the geotropic nature of tree growth. However, the SCS model, similar to the cosine correction, can not avoid an overcorrection problem. To address this problem, an empirical parameter C has been introduced into the SCS algorithm to form the SCS+C model. A well-known Minnaert constant might also play a role in reducing the overcorrection. In this paper, a new SCS+K topographic correction method by introducing the Minnaert constant k into the SCS model was proposed. The SCS+K model was applied to a TM forest image of a rugged site in Huailou County, Beijing, China. In comparison with the SCS and SCS+C algorithms, the SCS+K model performed markedly better than the SCS method and was comparable to the SCS+C approach. The results of this study showed that the SCS+K method can effectively reduce the topographic effect in forested terrain.

Reduction of atmospheric and topographic effect on Landsat TM data for forest classification

The incident radiance in forested areas with rugged terrain varies greatly with the changes in solar elevation and azimuth, slope and aspect of the terrain, and the relative position of trees. The geotropic nature must be considered in the course of topographic correction. The Sun‐Canopy‐Sensor (SCS) model is introduced to substitute the cosine correction in a physical model. We used an atmospheric simulation code, MODTRAN, and a digital elevation model (DEM) to calculate the path radiance, downwards diffuse radiance and two‐way transmittance of direct and diffuse light at different altitudes. Based on the atmospheric parameters derived above and the Lambertian assumption, surface reflectance in a forested area was retrieved from Landsat Thematic Mapper (TM) imagery using a revised physical model. Meanwhile, a smoothed DEM was used to assess the effect of noise on the DEM and misregistration between the DEM and the satellite imagery. Correlation analysis, spectral comparison between sunlit and shaded slopes and a support vector machine (SVM) classification were performed to assess the effect of the revised radiometric correction algorithm. Results indicate that the revised physical model with smoothed DEM is more adequate for forested terrain and more consistent spectra for similar vegetation under different illuminations can be obtained. Finally, higher classification accuracy of forested land can be achieved with the revised correction algorithm compared with the SCS correction and the original physical correction model.