Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models

The Sierra Madre Occidental mountain range (Durango, Mexico) is of great ecological interest because of the high degree of environmental heterogeneity in the area. The objective of the present study was to estimate the biomass of mixed and uneven-aged forests in the Sierra Madre Occidental by using Landsat-5 TM spectral data and forest inventory data. We used the ATCOR3 ® atmospheric and topographic correction module to convert remotely sensed imagery digital signals to surface reflectance values. The usual approach of modeling stand variables by using multiple linear regression was compared with a hybrid model developed in two steps: in the first step a regression tree was used to obtain an initial classification of homogeneous biomass groups, and multiple linear regression models were then fitted to each node of the pruned regression tree. Cross-validation of the hybrid model explained 72.96% of the observed stand biomass variation, with a reduction in the RMSE of 25.47% with respect to the estimates yielded by the linear model fitted to the complete database. The most important variables for the binary classification process in the regression tree were the albedo, the corrected readings of the short-wave infrared band of the satellite (2.08-2.35 µm) and the topographic moisture index. We used the model output to construct a map for estimating biomass in the study area, which yielded values of between 51 and 235 Mg ha-1. The use of regression trees in combination with stepwise regression of corrected satellite imagery proved a reliable method for estimating forest biomass.This research was supported by SEPPROMEP (Project: Seguimiento y Evaluación de Sitios Permanentes de Investigación Forestal y el Impacto Socioeconómico del anejo Forestal en el Norte de México)S

Does topographic normalization of landsat images improve fractional tree cover mapping in tropical mountains?

Volume: Volume XL-7/W3Fractional tree cover (Fcover) is an important biophysical variable for measuring forest degradation and characterizing land cover. Recently, atmospherically corrected Landsat data have become available, providing opportunities for high-resolution mapping of forest attributes at global-scale. However, topographic correction is a pre-processing step that remains to be addressed. While several methods have been introduced for topographic correction, it is uncertain whether Fcover models based on vegetation indices are sensitive to topographic effects. Our objective was to assess the effect of topographic correction on the accuracy of Fcover modelling. The study area was located in the Eastern Arc Mountains of Kenya. We used C-correction as a digital elevation model (DEM) based correction method. We examined if predictive models based on normalized difference vegetation index (NDVI), reduced simple ratio (RSR) and tasseled cap indices (Brightness, Greenness and Wetness) are improved if using topographically corrected data. Furthermore, we evaluated how the results depend on the DEM by correcting images using available global DEM (ASTER GDEM, SRTM) and a regional DEM. Reference Fcover was obtained from wall-to-wall airborne LiDAR data. Landsat images corresponding to minimum and maximum sun elevation were analyzed. We observed that topographic correction could only improve models based on Brightness and had very small effect on the other models. Cosine of the solar incidence angle (cos i) derived from SRTM DEM showed stronger relationship with spectral bands than other DEMs. In conclusion, our results suggest that, in tropical mountains, predictive models based on common vegetation indices are not sensitive to topographic effects.Peer reviewe

THE EFFECT OF TOPOGRAPHIC CORRECTION METHODS SUN CANOPY SENSOR + C CORRECTION (SCS + C) ON THE ACCURACY OF THE RESULTS OF VARIOUS CLASSIFICATION METHODS IN LANDSAT 8 SURFACE REFLECTANCE IMAGE

In the land cover classification process using the optical system remote sensing satellite data, there are problems in hilly areas where the lighting on the slopes facing or backward from the sun produces different spectral responses. In this study, we will analyze the effect of topographic correction on the Sun Canopy Sensor + C Correction (SCS + C) method on the accuracy of the classification results on the LANDSAT 8 surface reflectance image using Google Earth Engine (GEE). The results showed an increase in classification accuracy after topographic correction using the Support Vector Machine (SVM) method, Classification and Regression Tree (CRT), Random Forest (RF), and Minimum Distance (MD), respectively 4.45%, 3.33%, 2.23%, and 2.22%. The topographic correction applied to the Maximum Entropy (ME) classification methods failed to improve accuracy. It can be concluded that topographic correction can improve the accuracy of land cover classification results, especially in hilly area

Topographyc shadow influence on optical image acquired by satellite in the southern hemisphere

During image acquisition, is usually chosen scenes with a lesser cloud cover to avoid loss of spectral information. However, when training samples are collected for image classification, the user finds shadowed areas. Such situation is similar to the presence of clouds since spectral information of these classes is the same in all optical bands of the sensor. This fact becomes more pronounced in mountainous relief areas due to shadow projection on the terrain, which can vary among all seasons during the solar year. With the goal to obtain images with a lower presence of shadow, it was simulated, under the same relief conditions, shading variation in function of latitude (0° to 40° S). Solar radiation models were processed for the days and times passages of the Landsat TM and ETM+ satellite on the Southern Hemisphere. It was verified that over 30° S and 40° S latitudes, a loss of shading area varying between 27% to 91 % and that images should be preferentially taken between October and February. For latitudes comprising 0° and 10° S, the loss was considered negligible, when we set a 10% threshold of loss in the total valid area in an image. According to the amount of radiation in a terrain, South and West areas received less direct solar radiation over the year for all analyzed latitudes in the modeling

Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring

The Sentinel-2 data by European Space Agency were recently made available for free. Their technical features suggest synergies with Landsat-8 dataset by NASA (National Aeronautics and Space Administration), especially in the agriculture context were observations should be as dense as possible to give a rather complete description of macro-phenology of crops. In this work some preliminary results are presented concerning geometric and spectral consistency of the two compared datasets. Tests were performed specifically focusing on the agriculture-devoted part of Piemonte Region (NW Italy). Geometric consistencies of Sentinel-2 and Landsat-8 datasets were tested “absolutely” (in respect of a selected reference frame) and “relatively” (one in respect of the other) by selecting, respectively, 160 and 100 well distributed check points. Spectral differences affecting at-the-ground reflectance were tested after images calibration performed by dark object subtraction approach. A special focus was on differences affecting derivable NDVI and NDWI spectral indices, being the most widely used in the agriculture remote sensing application context. Results are encouraging and suggest that this approach can successfully enter the ordinary remote sensing-supported precision farming workflow

ANÁLISE DO EFEITO TOPOGRÁFICO SOBRE ÍNDICES DE VEGETAÇÃO UTILIZANDO IMAGENS RAPIDEYE NA SERRA DO MAR – PR

Ao explorar dados espectrais da vegetação deve-se considerar eliminar ou reduzir os efeitos da geometria de iluminação e observação, bem como as implicações associadas aos efeitos topográficos. O objetivo deste estudo foi analisar o efeito topográfico sobre índices de vegetação (SR, NDVI, EVI e RENDVI) na região da Serra do Mar, no estado do Paraná, utilizando dados da constelação Rapideye, após o emprego dos métodos semiempíricos de correção topográfica Minnaert e Correção-C. Observou-se que os dois métodos de correção empregados diminuíram o efeito topográfico nas bandas, proporcionando diminuição no valor do desvio padrão, o que propiciou uma menor correlação entre o fator cosseno e a refletância. Para o método Minnaert observou-se uma diferença significativa (95% confiança) entre todas as bandas não corrigidas e as corrigidas topograficamente. Para o método da Correção-C, a diferença não foi significativa (p-value > 0,05) para as bandas Red-Edge e NIR. Na avaliação sobre os índices de vegetação calculados com as imagens sem e com correção topográfica, foi aplicado o teste não paramétrico de Wilcoxon, atestando que apenas a correção Minnaert apresentou uma diferença significativa de 95%, sendo para este estudo o método mais eficaz na redução do efeito topográfico. Apesar da magnitude do efeito das diferenças da métrica de Cohen (r) ter sido baixa, se faz necessária aplicação de métodos de correção topográfica para uma redução consistente do efeito topográfico nos índices de vegetação de terrenos acidentados, proporcionando a extração de informações espectrais mais fidedignas. Percebeu-se ainda que índices normalizados são menos sensíveis aos efeitos topográficos

The added value of stratified topographic correction of multispectral images

Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the evaluation of the results obtained make it unclear how to implement them. The objective of this study was to compare different stratification strategies with a non-stratified approach using several evaluation criteria. For that purpose, Statistic-Empirical and Sun-Canopy-Sensor + C algorithms were applied and six different stratification approaches, based on vegetation indices and land cover maps, were implemented and compared with the non-stratified traditional option. Overall, this study demonstrates that for this particular case study the six stratification approaches can give results similar to applying a traditional topographic correction with no previous stratification. Therefore, the non-stratified correction approach could potentially aid in removing the topographic effect, because it does not require any ancillary information and it is easier to implement in automatic image processing chains. The findings also suggest that the Statistic-Empirical method performs slightly better than the Sun-Canopy-Sensor + C correction, regardless of the stratification approach. In any case, further research is necessary to evaluate other stratification strategies and confirm these results.The authors gratefully acknowledge the financial support provided by the Public University of Navarre (UPNA). Part of the research presented in this paper is funded by the Spanish Ministry of Economy and Competitiveness in the frame of the ESP2013-48458-C4-2-P project