Article

On the applicability of Landsat TM images to Mediterranean forest inventories

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  • Universitat Autònoma de Barcelona, Catalonia, Spain
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Abstract

Landsat TM images were used in combination with field measurements to create models for Mediterranean forests. Radiometric data from those images were related to field data from a forest inventory by means of regression analysis. Trials using plots with radiometrically homogeneous surroundings were carried out to evaluate the effect of high spatial heterogeneity frequently found in Mediterranean forests. Simple regression models were found to be consistent with the expected radiometric response of vegetation, and most of the multiple regression models fitted the observations sufficiently in order to make quantitative predictions for field variables from the remote sensing images. According to this study, however, these models should be regarded as exploratory models rather than fully operational ones. Spatially and non-spatially related factors are suggested as causes of the remaining dispersion of models created.

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... Firstly, universal direct relationships between canopy height and imagery are confounded by many factors, including shadow, density, and forest type [26,27], which result in site-specific relationships. A site-specific relationship then hinders large-scale wall-to-wall canopy height mapping [28]. For example, disparate relationships between Landsat spectral images and canopy heights have been reported in different studies, such as a negative relationship between spectral reflectance of the near-infrared and canopy height in [28], versus flat in [29,30], and positive relationships in [31]. ...
... A site-specific relationship then hinders large-scale wall-to-wall canopy height mapping [28]. For example, disparate relationships between Landsat spectral images and canopy heights have been reported in different studies, such as a negative relationship between spectral reflectance of the near-infrared and canopy height in [28], versus flat in [29,30], and positive relationships in [31]. The second limitation is spectral saturation to canopy height, which can lead to an underestimation of the canopy height in tall or dense forests. ...
... This paper employed a total of 49 ancillary images, which had a moderate to low correlation with canopy height (Appendix A Figure A1). The low correlation can potentially result in over-estimating short vegetation, but under-estimating tall vegetation [28]. Regardless of the under-estimation caused by spectral saturation, it is perhaps not surprising that spectral reflectance within a 30 m resolution pixel is from both over-story and under-story. ...
Article
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Spatially continuous canopy height is a vital input for modeling forest structures and functioning. The global ecosystem dynamics investigation (GEDI) waveform can penetrate a canopy to precisely find the ground and measure canopy height, but it is spatially discontinuous over the earth's surface. A common method to achieve wall-to-wall canopy height mapping is to integrate a set of field-measured canopy heights and spectral bands from optical and/or microwave remote sensing data as ancillary information. However, due partly to the saturation of spectral reflectance to canopy height, the product of this method may misrepresent canopy height. As a result, neither GEDI footprints nor interpolated maps using the common method can accurately produce spatially continuous canopy height maps alone. To address this issue, this study proposes a framework of point-surface fusion for canopy height mapping (FPSF-CH) that uses GEDI data to calibrate the initial wall-to-wall canopy height map derived from a sub-model of FPSF-CH. The effectiveness of the proposed FPSF-CH was validated by comparison to canopy heights derived from (1) a high-resolution canopy height model derived from airborne discrete point cloud lidar across three test sites, (2) a global canopy height product (GDAL RH95), and (3) the results of the FPSF-CH sub-model without fusing with the GEDI canopy height. The results showed that the RMSE and rRMSE of FPSF-CH were 3.82, 4.05, and 3.48 m, and 18.77, 16.24, and 13.81% across the three test sites, respectively. The FPSF-CH achieved improvement over GDAL RH95, with reductions in RMSE values of 1.28, 2.25, and 2.23 m, and reductions in rRMSE values of 6.29, 9.01, and 8.90% across the three test sites, respectively. Additionally, the better performance of the FPSF-CH compared with its sub-model further confirmed the effectiveness of integrating GEDI data for calibrating wall-to-wall canopy height mapping. The proposed FPSF-CH integrates GEDI LiDAR data to provide a new avenue for accurate wall-to-wall canopy height mapping critical to applications, such as estimations of biomass, biodiversity, and carbon stocks.
... These woks are mainly based on empirical models that relate reflectance provided by such images to the forest variable estimated from fieldwork. Owing to satellite images register continuous and complete information across a territory and such images are normally obtained at frequent intervals [23], [24], this methodology helps to overcome some of the problems of forest inventories based on extrapolation techniques using field plots that represent only a discrete sample in a continuous spatial dimension, reducing, moreover, the inventory cost in terms of time and money [9], [25]. Besides, remotely sensed data have facilitated construction of maps of forest attributes with accuracies and spatial resolutions that were not feasible some years ago [26]. ...
... In studies carried out in Mediterranean environments, the characteristics of their forest (e.g., heterogeneity in species composition, open structure, high fragmentation, irregular topography, etc.) induce high spectral variability between plots with the same forest parameter quantity, making it difficult to successfully employ predictive models. In addition, other sources of error in these environments are inaccuracies in the location of the plot over the satellite image and the small nature of the plot size [9], [10], [17], [27]. ...
... • Limitations related to the spectral, radiometric, and spatial resolution of the TM sensor. This sensor is widely used in estimates of biomass [53]; however, in highly heterogeneous environments such as Mediterranean forest, the sensor may be inadequate in terms of its resolution characteristics, including the broad spectral width of its bands, its radiometric resolution (256 ND), and its spatial resolution (30 m) [9], [10], [53]. Despite these limitations, the size of its scenes, effective marketing, and ease of distribution make Landsat TM the most suitable in terms of achieving the objective of estimate FRB at regional-scale in our study area with remote sensing data. ...
Chapter
The use of forest residual biomass (FRB) has grown slower than other biomass resources for renewable energy production, mainly because of a lack of methodologies to assess its quantity at regional scale. In this regard, previous works have demonstrated the utility of satellite images in estimating FRB, considering radiometric variables related to wetness as the most useful. In this context, the objective of this paper is to validate the utility of wetness variables obtained from Landsat TM images to estimate FRB, regardless of the image date in the summer period, which are the most suitable for estimating forest parameters. For this purpose, correlations between FRB data calculated using field work and the Second National Forest Inventory (NFI-2) plots, and spectral variables from three summer Landsat images, which were contemporary with the NFI-2 field work, were analyzed. As a result, it is concluded that both wetness variables considered, MID57 and TC3, are good predictors of FRB independently of the summer moment considered, and map of the study area is created. In addition, as complementary data, the moisture variation of the pine species considered were analyzed in the summer period by means of field work, verifying that significant differences do not exist.
... Cohen and Goward 2004). Salvador and Pons (1998a) point to the fact that the models derived from multiple regressions, although improving the values of determination coefficients relative to single regressions, should be considered as exploratory models that need further checking. In another study carried out with the same data, Salvador and Pons (1998b) emphasize the necessity of establishing robust statistical models before concluding that the information derived from TM images can be used successfully in the estimation of forest parameters, at least for Mediterranean forests. ...
... The number of plots may also be related to the range of habitats in which the plots are located. These factors could be related to the presence of uncontrolled variance sources in the vegetation types, which would increase the variability of the radiometric response (Salvador and Pons 1998a). This uncertainty makes it difficult to create regional models, as Foody et al. (2003) show in their analysis of the 5672 A. Vázquez de la Cueva transferability between regions of predictive relationships of forest biomass derived from Landsat TM images. ...
... These results, in the same way as in the RDA, do not support our second hypothesis. The results differ from those obtained by Salvador and Pons (1998a), who, in their analysis of conifer and sclerophyll Mediterranean forests in the northeast of Spain, found that the use of more homogeneous data sets according to the plots' environment improved the predictive models. These authors reported that the low and moderate values obtained from the univariate regression models performed by them were inadequate to the goal of obtaining quantitative estimations of the field variables under study, as this paper also seems to demonstrate. ...
Article
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Structural attributes of forest, such as canopy crown closure, stand height, stem density and basal area, derived from the third Spanish National Forest Inventory (IFN-3) were used in combination with spectral information derived from Landsat Enhanced Thematic Mapper Plus (ETM+) imagery and topographic information to evaluate their relationships. To deal with the variability found in the literature, three different types of vegetation, dominated by conifers, evergreen sclerophyll and broad-leaved deciduous trees, were analysed. In addition, the analyses were performed using three sets of plots filtered to be successively more homogeneous. A multivariate canonical ordination method, redundancy analysis (RDA), was used to enable the simultaneous evaluation of the two data sets and provide a useful graphical output highlighting the relationships between response (structural attributes) and explanatory (spectral and topographic) variables. Rank correlation analyses were also performed. The low percentage of explained variance at the multivariate analyses and low rank correlation coefficients made it difficult to derive practical empirical models. The strong influence of vegetation type on the results was confirmed, given that each type was sensitive to a different kind of spectral information. Finally, the results did not allow validation of the hypothesis that the relationship should be better when using a more homogeneous set of plots.
... Images derived from remote sensing register continuous and complete information across a landscape and such images can be obtained at frequent intervals. These characteristics help to overcome some of the problems associated with inventory methods exclusively based on field work, interpolation techniques and GIS (Franklin, 2001; Lu, 2006; Salvador & Pons, 1998a). Thus, remotely sensed data have not only facilitated an increase in the speed, cost efficiency, precision, and timeliness of inventories, but they have also allowed the construction of maps of forest attributes with spatial resolutions and accuracies that were not feasible even a few years ago (McRoberts & Tomppo, 2007). ...
... Among the different types of remote sensing data available to achieve the objective of this research, Landsat images were selected because they are one of the most common in forestry-related applications and estimates of aboveground biomass (AGB) at regional-local scales (i.e. Fazakas et al., 1999; Foody et al., 2001 Foody et al., , 2003 Gasparri et al., 2010; Hall et al., 2006; Labrecque et al., 2003 Labrecque et al., , 2006 Lu, 2005; Lu & Batistiella, 2005; Lu et al., 2004; Mäkelä & Pekkarinen, 2004; Mallinis et al., 2004; Meng et al., 2009; Powell et al., 2010; Roy & Ravan, 1996; Salvador & Pons, 1998a, 1998b Steininger, 2000; Tangki & Chappell, 2008; Wulder et al., 2008; Zheng et al., 2004 Zheng et al., , 2007). In addition, taking into account the research objectives, there were two other important reasons for using Landsat images. ...
... scrublands, farmlands) or because of high variability within the forested area (presence of different tree species, ages). In addition, other problems detected in previous research in Mediterranean forests are inaccuracies in the localization of inventory field plots, small plot sizes, and the small number of plots used in the analysis (Mallinis et al., 2004; Maselli & Chiesi, 2006; Salvador & Pons, 1998a, 1998b Shoshany, 2000; Vázquez de la Cueva, 2005). The present study tested three different methods to extract the radiometric data in order to overcome the problems outlined above and achieve accurate FRB regression models: (i) fixed pixel windows or kernels, (ii) visual analysis, and (iii) spectral segmentation. ...
... • Typically low dynamic range of the data [49], [160]; • Extensive geometric and radiometric corrections are needed; • Difficulty in reducing extraneous factors [144]; • Low spatial resolution relative to the objects under scrutiny -trees, and • Generally, small sample sizes resulting in fewer degrees of freedom than required for extensive use. ...
... Several studies established that multispectral satellite remote sensing data was related to tree density [39], basal area [146] and biomass [55], [199], diameter at breast height [160] and volume [3], [183]. These and other studies have led to the understanding that the effect of increasing or decreasing age, DBH, height, volume and so on, are actually all second-or third-order effects on remotely sensed image data. ...
... Volume estimates from satellite imagery are based on the weak relationship between spectral response and stand structure, as captured by imagery, in the differences in illumination and shadowing of forest canopies. A few studies have attempted direct standing volume assessment from multispectral satellite sensor data; almost all have commented that the very low dynamic range (e.g. a range of three DN for all measured volumes in the band [39]) in Landsat TM or SPOT satellite data is a significant factor limiting use for volume inventory at the level of detail required for forest management [41], [49], [160]. Even so, in areas without the resources to conduct forest volume inventory, rough or class-based volume estimates from satellite imagery can be very useful [58]. ...
... The most commonly used variables are spectral reflectance, crown diameter and crown horizontal projection [73][74][75][76][77][78], original bands, and/or vegetation indices [79][80][81][82]. Among the parametric models, the most frequently used are linear regression, both single [80,83,84] and multiple [80,83]; and nonlinear regression, power [84][85][86] and logistic [87]. The nonparametric models include regression k-nearest neighbor [88][89][90], artificial neural network [91], regression tree [35,92,93], random forest [18, [94][95][96], support vector machine [94], and maximum entropy [97]. ...
... The most commonly used variables are spectral reflectance, crown diameter and crown horizontal projection [73][74][75][76][77][78], original bands, and/or vegetation indices [79][80][81][82]. Among the parametric models, the most frequently used are linear regression, both single [80,83,84] and multiple [80,83]; and nonlinear regression, power [84][85][86] and logistic [87]. The nonparametric models include regression k-nearest neighbor [88][89][90], artificial neural network [91], regression tree [35,92,93], random forest [18, [94][95][96], support vector machine [94], and maximum entropy [97]. ...
Book
Full-text available
Forests are responsible for the largest net biomass carbon production. They store the most standing biomass and carbon and thus they are an important source of bioenergy. Their importance is linked to their relative abundance and uniformity worldwide and the neutrality of CO2 emissions from biomass conversion to energy. Yet, the use of biomass for energy presents risks related to forest system sustainability and demands for new environmentally sustainable strategies for its use. This book provides a comprehensive overview of the current state of the art in a multitude of subjects related to forest bioenergy, ranging from trees, forest stand management, and biomass assessment to waste management, conversion technologies, and routes and energy applications.
... The most commonly used variables are spectral reflectance, crown diameter and crown horizontal projection [73][74][75][76][77][78], original bands, and/or vegetation indices [79][80][81][82]. Among the parametric models, the most frequently used are linear regression, both single [80,83,84] and multiple [80,83]; and nonlinear regression, power [84][85][86] and logistic [87]. The nonparametric models include regression k-nearest neighbor [88][89][90], artificial neural network [91], regression tree [35,92,93], random forest [18, [94][95][96], support vector machine [94], and maximum entropy [97]. ...
... The most commonly used variables are spectral reflectance, crown diameter and crown horizontal projection [73][74][75][76][77][78], original bands, and/or vegetation indices [79][80][81][82]. Among the parametric models, the most frequently used are linear regression, both single [80,83,84] and multiple [80,83]; and nonlinear regression, power [84][85][86] and logistic [87]. The nonparametric models include regression k-nearest neighbor [88][89][90], artificial neural network [91], regression tree [35,92,93], random forest [18, [94][95][96], support vector machine [94], and maximum entropy [97]. ...
Chapter
Full-text available
As long as care is taken regarding stand and forest sustainability, forest biomass is an interesting alternative to fossil fuels because of its historical use as an energy source, its relative abundance and availability worldwide, and the fact that it is carbon-neutral. This study encompasses the revision of the state of the sources of forest biomass for energy and their estimation, the impacts on forests of biomass removal, the current demand and use of forest biomass for energy, and the most used energy conversion technologies. Forests can provide large amounts of biomass that can be used for energy. However, as the resources are limited, the increasing demand for biomass brings about management challenges. Stand structure is determinant for the amount of residues produced. Biomass can be estimated with high accuracy using both forest inventory and remote sensing. Yet, remote sensing enables biomass estimation and monitoring in shorter time periods. Different bioenergy uses and conversion technologies are characterized by different efficiencies, which should be a factor to consider in the choice of the best suited technology. Carefully analyzing the different options in terms of available conversion technologies, end-uses, costs, environmental benefits, and alternative energy vectors is of utmost importance.
... conditions, the Mediterranean landscape shows a low inter-class separability (Berberoglu et al., 2000;Rodriguez-Galiano and Chica-Olmo, 2012;Salvador and Pons, 1998). These climate conditions also determine soil moisture, vegetation diversity, and its density over the landscape. ...
... Despite the environmental and socio-economic importance of montado and its geographic representativeness in the Mediterranean Basin, the development of remote sensing-based approaches for mapping this ecosystem with the use of medium spatial and spectral resolution imagery is rarely addressed in the scientific literature (Carreiras et al., 2006;Godinho et al., 2014;Joffre and Lacaze, 1993;Salvador and Pons, 1998). ...
Thesis
Full-text available
The magnitude of montado change patterns over times, as well as their causes and effects on natural process, remains poorly understood. A comprehensive analysis of these spatio-temporal processes using an integrated and multidisciplinary approach was implemented in this doctoral thesis to better understand the main causes and impacts of montado landscape changes. The main goal of this doctoral thesis was to analyse the montado landscape dynamics by using cartographic information and remote sensing-derived data for assessing change patterns, its causes and impacts on biogeophysical processes. The central topic of this thesis was to study the usefulness and effectiveness of Earth Observation Satellites (EOS) in providing accurate and comparable montado land cover information to support detailed long-term landscape change analysis. To achieve such goal four specific research objectives were addressed: (i) determine the recent spatio-temporal patterns of montado changes in southern Portugal; and identifying the effects of selected environmental, land management, and spatial factors in these changes; (ii) explore the capability of EOS and advanced image classification techniques for producing accurate montado land cover maps; (iii) assess the effectiveness of existing remote sensing-based approaches for estimating the percentage of montado tree canopy cover at the pixel level; and (iv) develop an effective remote sensing-based methodological approach to understand the effects of montado canopy cover decrease in local land surface biogeophysical process dynamics. From the investigations conducted, a decline trend of montado ecosystem was clearly identified through the estimated montado area and tree canopy cover regression. Furthermore, the demonstrated usefulness and effectiveness of EOS was one of the most important outputs of this thesis towards a broader long-term montado change analysis which may also include the assessment of its effects on local biogeophysical processes.
... Numerosos trabajos han mostrado el potencial de las imágenes de satélite para las tareas de inventario forestal, permitiendo el carácter continuo, completo y recurrente de éstas superar las limitaciones de otros métodos de inventario basados exclusivamente en el trabajo de campo y la interpolación de datos puntuales y de técnicas SIG (Salvador y Pons, 1998, Franklin, 2001. Así, la teledetección ha contribuido a incrementar la velocidad, la eficiencia de coste y la precisión de los inventarios (McRoberts y Tomppo, 2007), convirtiéndose en una fuente primaria para la estimación de biomasa (Lu, 2006). ...
... Esto es debido a que esta heterogeneidad espacial se traduce en una elevada variabilidad espectral de las áreas ocupadas por estos bosques. Otros factores son el pequeño tamaño de las parcelas, la comisión de imprecisiones en su localización y el escaso número de parcelas usados (Salvador y Pons, 1998). Así, en el caso concreto de las parcelas utilizadas, dos de ellas con una misma cantidad de BRF pueden presentar valores medios de reflectividad distintos debido a la presencia de otro tipo de cubierta o a una alta variabilidad interna. ...
Article
Full-text available
El aprovechamiento energético de la biomasa residual forestalpresenta múltiples beneficios medioambientales y socioeconómicos;sin embargo, la falta de una metodología para estimar la cantidad presentea escala regional es una de las razones que impiden la mayor utilizaciónde este recurso renovable en España. El presente trabajo presentauna metodología para evaluar la biomasa residual de los pinaresde la provincia de Teruel (España) relacionando datos de esta fracciónde biomasa calculados sobre parcelas de inventario forestal de 1994 conuna imagen Landsat coetánea a las labores de campo del inventario.Para evitar la influencia que la heterogeneidad de los medios forestalesmediterráneos tiene en el ajuste de modelos de regresión, se ensayantres métodos distintos de extracción de la información espectral. Unavez validadas las ecuaciones obtenidas con estos tres métodos, la mejores aplicada sobre una imagen Landsat más reciente. Se obtiene así lacantidad potencial de este recurso (5.449.252 tons) y cartografía precisasobre su distribución en el territorio turolense.
... structure and species composition, satellite-based remote sensing can be adopted either as a stand-alone method or in conjunction with other traditional inventory/surveying techniques for forest inventory and assessment (Ripple et al. 1991, Ardo 1992, Danson and Curran 1993, Woodcock et al. 1994, Trotter et al. 1997. In contrast, under heterogeneous conditions, ground-based surveys and/or aerial photographs may be more appropriate than remote sensing (Salvador and Pons 1998). ...
... Normally, there is an inverse relationship between vegetation amount and reflectance in the visible and mid-infrared region of the electromagnetic spectrum because of the absorption from plant pigments and water content, respectively. In contrast, the relationship between vegetation amount and reflectance in the near-infrared region is positive (Salvador and Pons 1998). However, it has been found that this positive relationship does not always exist; it can be flat (Franklin 1986) or even inverse depending on understory or background reflectance (Ripple et al. 1991, Danson andCurran 1993). ...
Article
Satellite remote sensing provides new possibilities and challenges to forest managersformonitoringandmanagingforestecosystems.Thevalue/useofhigh-resolution satellite data of Landsat Thematic Mapper (TM) to estimate tree density, basal area, basal volume, and forest biomass was investigated under an operational perspective in a spatially heterogeneous Mediterranean landscape in northern Greece. Digital classification using Fisher’s linear discriminant functions produced an overall accuracy of 82%. A series of multispectral transformations were also performed, and the derivative synthetic channels wereusedalongwiththeoriginalonesasexplanatoryvariablesinthemodeldeveloped.The forest stand parameters that were investigated in the study presented a similar but weak correlation with the Landsat-5 TM spectral channels. Among them, TM4 and TM7 presented the highest correlation with forest stand parameters, while the integration of the derivative synthetic channels increased the correlation coefficients and resulted in statistically signif- icant predictive models.
... These coefficients are equivalent to those obtained for the group of percentile 4 at the 3 3 pixel window level, but the number of plots (131) is nearly double that in the group of percentile 4 (68). This is a positive result because an increase in sample size reduces the probability of constructing an over-fitted model [32], [51]. ...
... Validation of cartography obtained from the estimation model was confirmed at the pixel level using NFI-2 plots that had not been employed in the regression equation, ensuring independence. The following factors explain remaining errors in the estimation of crown biomass: i) inaccuracies in fieldwork undertaken to establish the allometric equations; ii) problems in relating NFI plots to satellite data (as the placement of field plots in the Spanish NFI involves georeferenced 1:30 000 air photographs and topographic cartography, such inaccuracies may occur); iii) limitations of the TM sensor (because in highly heterogeneous environments such as Mediterranean forest, the sensor can be inadequate in resolution characteristics [32], [33], [50]); and iv) inaccuracies related to heterogeneity, despite efforts made to combat this problem. This is because four different pine species were considered in this study. ...
Article
Remote sensing has been shown to be an efficient tool in the study of forest-fire processes. However, a lack of information on the amount of biomass burnt reduces the accuracy of fire severity and emission models. In this study, we use imagery from the Landsat Thematic Mapper to map crown biomass and burn severity for a large Mediterranean area. Considering the specific characteristics of the Mediterranean environment, two methods to extract useful remote sensing data were employed; both sought to analyze relationships between crown biomass and spectral information. As a result, a crown biomass map of Pinus spp . was created for the entire study area, applying nonlinear regression using the variable MID57 (TM5 + TM7) (R2 = 0.651). Considering only P. halepensis pixels that were burnt in the selected fire scar, the relationships between crown biomass and burn severity were found to be high and significant, yielding an R2 value of 0.516. Finally, a logistic regression model was constructed to map the presence or otherwise of high burn severity levels using crown biomass as the independent variable, yielding in the confusion matrix an overall percentage of data points correctly classified of 77% and a Kappa statistic in the validation sample of 0.554.
... Mediterranean forest areas are known for the high spatio-temporal heterogeneity of their vegetation patterns with respect to species composition and stand structure (Salvador and Pons, 1998;Shoshany, 2000). Human influences, the richness of tree species, topographical variability and climate conditions generate complex and fragmented landscapes where patches of closed forest stands are often interspersed with other land cover types (Scarascia-Mugnozza et al., 2000). ...
... While this heterogeneity makes them aesthetically attractive, accurate mapping of these areas has been the weak point of applied remote sensing technology, especially for local scale mapping. The relatively low spatial resolution of the available satellite data, in relation to the spatial configuration of the landscape, results into the L-resolution scene model (Strahler et al., 1986) that has been a restricting factor towards the operational use of remote sensing (Salvador and Pons, 1998;Mallinis et al., 2004;Maselli et al., 2005). However, the improved spatial characteristics of satellite data, starting with the launch of IKONOS in 1999 (Goetz et al., 2003), increase the potential of this technology to fulfill the needs of national governments to implement environmental legislation on a basis of detailed and accurate information. ...
Article
A multi-scale, object-based analysis of a Quickbird satellite image has been carried out to delineate forest vegetation polygons in a natural forest in Northern Greece. Following a multi-resolution segmentation, a classification tree was developed and compared using a nearest neighbour classifier for the assignment of image segments to classes. Additionally, texture images derived from local indicators of spatial association were calculated and used to improve the classification.The best results were obtained when texture images were considered in the classification sequence, however, the accuracy of the final map did not exceed 80%. The classification tree yielded better results than the nearest neighbour algorithm. Overall, the object-based classification approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping Mediterranean forest ecosystems.
... La extracción de información biofísica constituye una de las líneas más fructíferas en el ámbito de las aplicaciones forestales de la teledetección espacial (Bergen, et al., 2000; Dobson, 2000; Goetz, 2002), siendo muy numerosos los trabajos orientados a la estimación de LAI y biomasa, principalmente con imágenes Landsat (Curran et al., 1992; Todd et al., 1998; Fazakas et al., 1999; Eklundh, L. et al., 2001; Mickler et al., 2002; Reese et al., 2002; Foddy et al. 2003; Phua y Saito, 2003; Lu et al., 2004; Zheng et al., 2004). No obstante, los ámbitos de aplicación han sido, mayoritariamente, bosques boreales densos, homogéneos y de topografía poco compleja, siendo escasas las experiencias en ámbitos mediterráneos (Salvador y Pons, 1998a Pons, , 1998b Mallinis et al., 2004). Además, hay una carencia importante de estudios acerca de la posibilidad de estimar biomasa residual forestal mediante imágenes de satélite. ...
... Esta parte de la varianza que queda sin explicar puede estar relacionada con la alta heterogeneidad de estos bosques mediterráneos: estructura abierta, fragmentación, presencia de otros elementos de paisaje, etc., y, por otra parte, con imprecisiones en el tratamiento de los datos y en la localización de los puntos del IFN-2. Estas circunstancias hacen que parcelas con una misma cantidad de biomasa puedan presentar una alta variabilidad espectral entre ellas, situaciones ya observadas en otros trabajos referentes a la estimación de variables forestales mediante teledetección en ambientes mediterráneos (Salvador y Pons, 1998a Pons, , 1998b Mallinis et al., 2004). Además, no hay que olvidar la posible comisión de errores en la fase de trabajo de campo de las regresiones estimativas de biomasa residual. ...
Article
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Diversos trabajos han puesto de manifiesto la existencia de correlaciones entre la biomasa forestal y la información de imágenes de satélite. La aplicación de la teledetección para cuantificar esta biomasa presenta ventajas respecto a la de los inventarios tradicionales. En este contexto, existe una falta de trabajos de teledetección dirigidos al estudio de la biomasa residual forestal, cuyo aprovechamiento energético presenta beneficios medioambientales y socio-económicos. El objetivo es desarrollar una metodología para evaluar -mediante regresión logística- la biomasa residual de los bosques de pináceas de Teruel a partir de una imagen Landsat TM, de información topográfica y de variables derivadas del Mapa Forestal de Aragón, tomando como referencia las parcelas del Inventario Forestal Nacional y trabajo de campo. Los resultados indican que el neocanal MSI, TM4 y la variable nivel de madurez son los predictores más importantes para evaluar estos recursos.
... Remote sensing techniques have been mainly applied to overcome the shortcomings of classical inventory methods, and they resulted in a large variety of inventory data and derivative products (Ababou et al, 2010;McRoberts & Tomppo, 2007). Since the launching of the first Landsat dealing with a Multispectral Scanner System (MSS) dated to 1972, remote sensing and different modeling methods have been used in forest inventory studies: regression analysis (Ardö, 1992;West, 1995 ;Lu, 2005;Salvador & Pons, 1998;); k-nearest neighbor method (Franco-Lopez et al., 2001;McRoberts & Tomppo, 2007 ;Tomppo et al., 2008;Gasparri et al., 2010;Dandan Xu, Xilun Guo 2014;Khader et al., 1014) ; neural networks (Foody et al., 2003); and fuzzy logic (Triepke et al., 2008). Remote sensing data, geographic information systems, and modeling are now often used in ecological studies (Cohen & Goward, 2004). ...
Article
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The current study is interested in the pilot region of Djelfa steppe located in northern Algeria, where a sizable pastoral activity serves as the foundation of the social organisation and the primary source of income. Today's courses experience a severe degradation that menaces the future of pastoral activity due to the disorganisation of social fabric, the phenomenon of turning into a desert, and eolian erosion. It is in this concern that we worked out a methodological approach aiming at characterising the current ecological situation, by using efficient tools namely: remote sensing and GIS. Thus a card of occupation of the ground containing a characterisation of the various courses of the area was carried out by the use of the Landsat imagery. The results so obtained and other inherent data in the middle of study and with our problems were integrated in a Database with the purpose of placing at the disposal of the decision makers and the specialist’s rational management tools in the fodder resources.
... GIS applications in forestry are diverse due to the excellent capabilities of GIS to visualize the spatial data, perform vegetation analysis, e.g. using remote sensing Landsat TM/ETM+ data, as reflected in various applications and previous studies (e.g. Foody & Hill 1996;Salvador & Pons, 1998;Miletić et al. 2016;Valjarević et al., 2018a). However, the use of GUI-based software has certain limitations compared to the scripting-based approach. ...
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In this research, an integrated framework on the big Earth data analysis has been developed in the context of the geomorphology of Jordan. The research explores the correlation between several thematic datasets, including machine learning and multidisciplinary geospatial data. GIS mapping is widely used in geological mapping as the most adequate technical tool for data visualization and analysis. GIS applications encourage geological prospective modeling by visualizing data aimed at the prognosis of mineral resources. However, automatization using machine learning for big Earth data processing provides the speed and accurate processing of multisource massive datasets. This is enabled by the application of scripting and programming in cartographic techniques. This study presents the combined machine learning methods of cartographic analysis and big Earth data modeling. The objective is to analyze a correlation between the factors affecting the geomorphological shape of Jordan with respects to the Dead Sea Fault and geological evolution. The technical methodology includes the following three independent tools: 1) Generic Mapping Tools (GMT); 2) Selected libraries of R programming language; 3) QGIS. Specifically, the GMT scripting program was used for topographic, seismic and geophysical mapping, while QGIS was used for geologic mapping and R language for geomorphometric modeling. Accordingly, the workflow is logically structured through these three technical tools, representing different cartographic approaches for data processing. Data and materials include multisource datasets of the various resolution, spatial extent, origin and formats. The results presented cartographic layouts of qualitative and quantitative maps with statistical summaries (histograms). The novelty of this approach is explained by the need to close a technical gap between the traditional GIS and scripting mapping, which is wider for big data mapping and where the crucial factors are speed and precision of data handling, as well as effective visualization achieved by the machine graphics. The paper analyzes the underlying geologic processes affecting the formation of geomorphological landforms in Jordan with a 3D visualization of the selected fragment of the Dead Sea Fault zone. The research presents an extended description in methodology, including the explanations of code snippets from the GMT modules and examples of the use of R libraries 'raster' and 'tmap'. The results revealed strong correlation between the geological and geophysical settings which affect geomorphologi-cal patterns. Integrated study of the geomorphology of Jordan was based on multisource datasets processed by scripting. A thorough analysis presented regional correlations between the geomorphological, geological and tectonic settings in Jordan. The paper contributed both to the development of cartographic engineering by introducing scripting techniques and to the regional studies of Jordan including the Dead Sea Fault as a special region of Jordan. The results include 12 new thematic maps including a 3D model.
... Therefore, when analyzing the wood volume data set across all available ages range (1.25 to 6.33 years), i.e., when grouping together all data, an inverse relationship prevailed. This behavior is not generally expected when VIs are associated with forest biophysical variables (Tucker, 1979;Peterson et al., 1986;Mcdonald et al., 1998;Salvador and Pons, 1998;Foody et al., 2003;Gonçalves et al., 2010;Berra et al., 2012), however, it can happen after the total closure of the canopy and due to forest aging (Franklin, 1986;Ardo, 1992;Danson and Curran, 1993;Puhr and Donoghue, 2000). ...
Article
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This study aimed to increase satellite-derived Normalized Difference Vegetation Index (NDVI) sensitivity to biophysical parameters changes with aid of a forest age-based adjustment factor. This factor is defined as a ratio between stand age and age of rotation, which value multiplied by Landsat-5/TM-derived NDVI generated the so-called adjusted index NDVI_a. Soil Adjusted Vegetation Index (SAVI) was also calculated. The relationship between these vegetation indices (VI) with Eucalyptus and Pinus stands’ wood volume was investigated. The adjustment factor caused an increase in NDVI dynamic range values, since older stands tended to be assigned with highest NDVI values, while younger ones tended to be forced to assume lower NDVI values. As a result, direct and significant relationship between NDVI_a and wood volume could be maintained for wider ranges of wood volume. However, it was observed that NDVI_a was only statistically superior to NDVI and SAVI when a detailed age dataset is available. It is conclude that, the stand age has potential to improve NDVI sensitivity to biophysical parameters allowing that quantitative estimates could be made since young to adult stands.
... In Spain, similar to other Mediterranean countries, a National Forest Inventory (NFI) provides periodic detailed data for the assessment of biomass and carbon pools through sampling and reporting supported by statistics (MMA 2008). The NFI's 10 year re-measurement cycle enables comparison of data over time, but similar to other sample-based NFIs, has some known limitations, including the discrete character of the sampling, which obliges extrapolation of data (Salvador and Pons 1998), and the use of different basic cartography in subsequent updates of the NFI database (Villaescusa et al. 2001). For areas undergoing rapid change that require up-to-date information on change events, a decade might be too long as a reporting interval (FAO 2010). ...
Chapter
The amount of biomass in forest ecosystems is critical information for global carbon cycle modelling. Determination of forest function as a sink or source of carbon is likewise relevant for both scientific applications and policy formulation. The quantity and function of forest biomass in the global carbon cycle is dynamic and changes as a result of natural and anthropogenic processes. This dynamism necessitates monitoring capacity that enables the characterization of changes in forest biomass over time and space. By combining field inventory and remotely sensed data, it is possible to characterize the quantity of biomass for a single date, or to characterize trends in quantity and function of forest biomass through time. Field inventory data provides accurate information for calibration of spatially extensive remotely sensed data models and for model validation as well. Historical, repeat measures of the same field plots facilitate the estimation of temporal trends in biomass accrual or removal, as well as carbon pooling processes. Remotely sensed data enable the inference of trends over large areas, and historical data archives can support retrospective analyses and the establishment of a baseline for future monitoring efforts. This chapter describes some of the opportunities provided by synergies between field measures and remotely sensed data for biomass and carbon assessment over large areas, and describes a case study in the Mediterranean pines of Spain, in which biomass and carbon pooling for the period 1984 to 2009 are estimated with a time series of Landsat imagery supported with data from the Spanish National Forest Inventory.
... High relations are established between spectral reflectance and/or vegetation indices, and canopy structure, LAI or biomass [16] from tropical [29, 30] to Mediterranean evergreen oaks and shrubland [17, 26, 31, 32] landscapes. Vegetation indices, condensing satellite imagery data in a quantitative numeric form, are related to some forest parameter's estimation, such as the number of trees per hectare [33], canopy cover [26, 34] and volume, basal area and biomass [35]. The aforementioned indices show a better sensitivity when compared with the spectral reflectance [36]. ...
Chapter
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Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment.
... Former studies of wood volume estimation utilizing remote sensing techniques and of individual tree crown delineation utilizing passive remote sensing (Ke and Quackenbush 2011) have been conducted mainly in northern European and northern American ecosystems. This study examines wood volume estimation and tree crown delineation in the Mediterranean ecosystem, which is characterized by heterogeneity as a result of factors such as climate, socio-economic conditions and overexploitation (Salvador and Pons 1998;Scarascia-Mugnozza et al. 2000). In this study, satellite imagery is utilized, which has the advantage of covering large areas; so acquisition of information for forest management is facilitated. ...
Article
The objective of this research was to evaluate wood volume estimates of Pinus nigra trees in forest stands, which were derived utilizing Geographic Object-Based Image Analysis. Information on forest parameters such as wood volume and number of trees is useful for forest management facilitating forest sustainability. Most of the existing approaches used to estimate wood volume of forest trees require field measurements, which are laboursome. In this study, the collected field data were utilized only in order to investigate the results. Wood volume was estimated based on an individual tree crown approach and using monoscopic satellite images in combination with allometric data. The study area is the Pentalofo forest, which is located in Kozani prefecture in western Macedonia, Northern Greece. About 1 plot surface of 0.1143 ha was utilized. During the preprocessing, a pansharpened image was produced from two Quickbird satellite images (one multispectral image of 2.4 m spatial resolution and one panchromatic image of 0.6 m spatial resolution). Bands of this image were utilized single or in combination in order to delineate the tree crowns individually. The allometric equation served in order to calculate the tree Diameter at Breast Height (DBH) utilizing the detected tree crowns. The evaluation was conducted on three levels: (i) number of trees, (ii) DBH class distribution and (iii) wood volume. On the third level, the evaluation procedure was conducted twice; once using field height and once without. The difference between the results and the field data for the wood volume reached a maximum of approximately 30%. The total number of trees was exactly the same as counted in the field and the DBH distribution showed a tendency for the trees to move to a higher DBH class, resulting in an overestimation of the wood volume. http://www.tandfonline.com/eprint/Veh9uMd7qCqHHEWBkBGa/full
... Another problem of Landsat based approaches is related to the impact of shadows caused by canopy in complex stand structures and relatively complex topography [27,28]. Furthermore, interference by understory vegetation and soil in open woodlands may require correction when spectral indices such as NDVI are used [29]. Yet, in savannah-type open Mediterranean evergreen woodlands, models with spectral indices including NDVI had comparable or better predictive capabilities of tree canopy cover [30]. ...
Article
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Background: A functional forest carbon measuring, reporting and verification (MRV) system to support climate change mitigation policies, such as REDD+, requires estimates of forest biomass carbon, as an input to estimate emissions. A combination of field inventory and remote sensing is expected to provide those data. By linking Landsat 8 and forest inventory data, we (1) developed linear mixed effects models for total living biomass (TLB) estimation as a function of spectral variables, (2) developed a 30 m resolution map of the total living carbon (TLC), and (3) estimated the total TLB stock of the study area. Inventory data consisted of tree measurements from 500 plots in 63 clusters in a 15,700 km(2) study area, in miombo woodlands of Tanzania. The Landsat 8 data comprised two climate data record images covering the inventory area. Results: We found a linear relationship between TLB and Landsat 8 derived spectral variables, and there was no clear evidence of spectral data saturation at higher biomass values. The root-mean-square error of the values predicted by the linear model linking the TLB and the normalized difference vegetation index (NDVI) is equal to 44 t/ha (49 % of the mean value). The estimated TLB for the study area was 140 Mt, with a mean TLB density of 81 t/ha, and a 95 % confidence interval of 74-88 t/ha. We mapped the distribution of TLC of the study area using the TLB model, where TLC was estimated at 47 % of TLB. Conclusion: The low biomass in the miombo woodlands, and the absence of a spectral data saturation problem suggested that Landsat 8 derived NDVI is suitable auxiliary information for carbon monitoring in the context of REDD+, for low-biomass, open-canopy woodlands.
... Remote sensing has potential to provide, at lower cost, robust forest information with greater coverage and more limited time extent than is attainable using field sampling. However, forest areas in the Mediterranean region are known for the high spatiotemporal heterogeneity of their vegetation patterns with respect to species composition and stand (Salvador and Pons 1998;Shoshany, 2000). While this heterogeneity makes them aesthetically attractive, accurate mapping of these areas has been the weak point of applied remote sensing technology, especially for local scale mapping (Mallinis et al., 2008). ...
Conference Paper
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Spatial explicit knowledge regarding the quantity and the spatial distribution of forest parameters is crucial for sustainable forest management, as well as in fulfilling national reporting needs in the framework of international treaties (i.e. Kyoto Protocol, FAO, EFFIS etc). Especially, tree number which can be used for assessing forest tree density (tree number/ha), is among the most important and laboursome parameters to be measured in the field. The aim of this study is to estimate tree number based on the use of nationwide, freely available, very high spatial resolution orthophotos acquired from Greek National Cadastre and Mapping Agency during the 2007-2009 period. The study area is the University Forest of Taxiarchis, which is located in central Halkidiki, Northern Greece. The dominant species of the forest includes both broadleaves (oak, beech) and coniferous species (Black pine, Calabrian pine), which are found in both pure and mixed stands. Tree crown detection was tested on natural color orthophoto bands in several plots. The principal components and intensity-hue-saturation transformations were also applied in order to enhance tree detection accuracy. Local maxima technique was utilized for tree crown detection. Accuracy results were evaluated based on field plot data available from the official forest management plan of the area completed in 2011. Overall, the detection accuracy exceeded 50% which is deemed satisfactory considering also the heterogeneity of the Mediterranean landscape and the limited spectral resolution of the remote sensing data available.
... Remote sensing has potential to provide, at lower cost, robust forest information with greater coverage and more limited time extent than is attainable using field sampling. However, forest areas in the Mediterranean region are known for the high spatiotemporal heterogeneity of their vegetation patterns with respect to species composition and stand (Salvador and Pons 1998;Shoshany, 2000). While this heterogeneity makes them aesthetically attractive, accurate mapping of these areas has been the weak point of applied remote sensing technology, especially for local scale mapping (Mallinis et al., 2008). ...
Article
Full-text available
Spatial explicit knowledge regarding the quantity and the spatial distribution of forest parameters is crucial for sustainable forest management, as well as in fulfilling national reporting needs in the framework of international treaties (i.e. Kyoto Protocol, FAO, EFFIS etc). Especially, tree number which can be used for assessing forest tree density (tree number/ha), is among the most important and laboursome parameters to be measured in the field. The aim of this study is to estimate tree number based on the use of nationwide, freely available, very high spatial resolution orthophotos acquired from Greek National Cadastre and Mapping Agency during the 2007-2009 period. The study area is the University Forest of Taxiarchis, which is located in central Halkidiki, Northern Greece. The dominant species of the forest includes both broadleaves (oak, beech) and coniferous species (Black pine, Calabrian pine), which are found in both pure and mixed stands. Tree crown detection was tested on natural color orthophoto bands in several plots. The principal components and intensity-hue-saturation transformations were also applied in order to enhance tree detection accuracy. Local maxima technique was utilized for tree crown detection. Accuracy results were evaluated based on field plot data available from the official forest management plan of the area completed in 2011. Overall, the detection accuracy exceeded 50% which is deemed satisfactory considering also the heterogeneity of the Mediterranean landscape and the limited spectral resolution of the remote sensing data available.
... Gracias a la correlación que existe entre los parámetros forestales que definen a un árbol (diámetro del tronco, altura…) y a una masa (densidad de copas, volumen total, etc.) con la información radiométrica contenida en las imágenes de satélite y a las características de éstas, la teledetección se ha convertido en una herramienta adecuada para la realización de tareas de inventario forestal y para la estimación de la biomasa (Tomppo et al., 2008;Powell, 2010). La obtención de información en las imágenes de satélite de forma continua permite reducir gran parte del trabajo necesario en inventarios tradicionales de muestreo de parcelas de campo, eliminado además, las imprecisiones debidas a asociar un determinado valor a zonas para las que no se tiene información directa a partir del uso de interpolaciones obtenidas con un conjunto más o menos importante de esas parcelas (Salvador y Pons, 1998;Hyyppä e Hyyppä, 2001;Franklin, 2001). No obstante, las aplicaciones desarrolladas en el seno de la teledetección para estimar biomasa presentan un carácter muy local, no siendo enteramente reproducibles a escalas regionales (Muukkonen y Heiskanen, 2005), localizándose los mayores problemas en medios forestales heterogéneos como los tropicales y mediterráneos (Lu, 2006). ...
Article
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Este trabajo presenta una metodología para determinar las zonas de la provincia de Teruel (España) más adecuadas para la extracción de residuos forestales con fines energéticos respetando los principios de sostenibilidad ecológica de los bosques. Para ello, se modelizan mediante teledetección y SIG los cuatro factores territoriales que intervienen en la viabilidad técnica y económica de la extracción de este recurso renovable: la cantidad de biomasa disponible, la pendiente, la superficie de la masa y la distancia a caminos y pistas de desembosque. Con posterioridad, estos cuatro factores se combinan en dos índices cuyo resultado sintetiza la aptitud de cada punto del territorio con una resolución espacial de 25 m, identificándose las zonas más adecuadas en la provincia. De los dos índices desarrollados, el que se basa en la suma ponderada de las variables resulta el más adecuado, ya que es más sencillo y permite considerar mejor los criterios técnicos de los gestores. Palabras clave: residuos forestales, energía renovable, imágenes de satélite, SIG, Teruel.
... haze, cloud), vegetation phenology, and topographic characteristics (Gemmell, 1998), but also from intrinsic forest structure and variability (Gemmell, 1995;Lu et al., 2005). In Mediterranean forests, typically characterized by rugged locations and structural heterogeneity (Salvador and Pons, 1998), the relationship between AGB and spectral response has proven difficult to characterize (e.g. Maselli et al., 2005;Vázquez de la Cueva, 2008). ...
Article
Estimation of forest aboveground biomass (AGB) is informative of the role of forest ecosystems in local and global carbon budgets. There is a need to retrospectively estimate biomass in order to establish a historical baseline and enable reporting of change. In this research, we used temporal spectral trajectories to inform on forest successional development status in support of modelling and mapping of historic AGB for Mediterranean pines in central Spain. AGB generated with ground plot data from the Spanish National Forest Inventory (NFI), representing two collection periods (1990 and 2000), are linked with static and dynamic spectral data as captured by Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors over a 25 year period (1984–2009). The importance of forest structural complexity on the relationship between AGB and spectral vegetation indices is revealed by the analysis of wavelet transforms. Two-dimensional (2D) wavelet transforms support the identification of spectral trajectory patterns of forest stands that in turn, are associated with traits of individual NFI plots, using a flexible algorithm sensitive to capturing time series similarity. Single-date spectral indices, temporal trajectories, and temporal derivatives associated with succession are used as input variables to non-parametric decision trees for modelling, estimation, and mapping of AGB and carbon sinks over the entire study area. Results indicate that patterns of change found in Normalized Difference Vegetation Index (NDVI) values are associated and relate well to classes of forest AGB. The Tasseled Cap Angle (TCA) index was found to be strongly related with forest density, although the related patterns of change had little relation with variability in historic AGB. By scaling biomass models through small (∼2.5 ha) spatial objects defined by spectral homogeneity, the AGB dynamics in the period 1990–2000 are mapped (70% accuracy when validated with plot values of change), revealing an increase of 18% in AGB irregularly distributed over 814 km2 of pines. The accumulation of C calculated in AGB was on average 0.65 t ha−1 y−1, equivalent to a fixation of 2.38 t ha−1 y−1 of carbon dioxide.
... Most AGB studies have been carried out in uniform boreal forests of coniferous plantations (Häme et al., 1997;Muukkonen and Heiskanen, 2005) and temperate and tropical forests (Phua and Saito, 2003;Cutler et al., 2012). There is a lack of experience for estimating AGB in Mediterranean environments (Salvador and Pons, 1998). However, the study of Sevillano-Marco et al. (2013) can be mentioned. ...
... However, land cover recognition techniques that may be suitable to wide forest stands (i.e., limited numbers of tree species and of mixed forests spatially organized in large homogeneous patches) have limited success in Mediterranean regions with more complex landscapes and where land covers and forest types vary frequently over small areas (Lucas et al., 2008). This is due to the characteristically high rate of spatio-temporal ecological heterogeneity (Di Castri, 1981) and high fragmentation of land cover in the Mediterranean area, which Salvador and Pons (1998) attributed to the forest species heterogeneity and to the stand structure due to factors such as location, climate, and human impact. In particular, the variability of the Mediterranean climate, which may experience severe summer droughts, frequently hinders the development of complete forest canopy cover and allows for the growth of a heterogeneous understory. ...
Data
We analyze the capability of Hyperion spaceborne hyperspectral data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000). For this purpose, we used Hyperion imagery acquired over Pollino National Park (Italy). Hyperion pre-processed data (30 m spatial resolution) were classified at the pixel level using common parametric supervised classification methods. The algorithms' performance and class level accuracy were compared with those obtained for the same area using airborne hyperspectral MIVIS data (7 m spatial resolution). Moreover, in selected test areas characterized by heterogeneous land cover (as mapped by MIVIS classification) a Linear Spectral Unmixing (LSU) technique was applied to Hyperion data to derive the abundance fractions of land cover endmembers. The accuracy of the LSU analysis was evaluated using the Residual Error parameter, by comparing Hyperion LSU results with land cover fractional abundances achieved from reference data (i.e., MIVIS and air-photo classification). The results show the potential of Hyperion spaceborne hyperspectral imagery in mapping land cover and vegetation diversity up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem such as that of Pollino National Park. Moreover, we defined a criterion for evaluating the Hyperion accuracy in retrieving land cover abundances at the sub-pixel scale. Sub-pixel analysis allowed us to determine the optimal threshold to select the areas on which consistent fractional land cover monitoring can be achieved using the Hyperion sensor.
... Remote sensing methods have been developed to overcome the limitations of traditional inventory techniques, and they have led to a growing variety of inventory data and derivative products (McRoberts and Tomppo, 2007). Since the first Landsat carrying a Multispectral Scanner System (MSS) was launched in 1972, remote sensing and various modeling methods have been used in forest inventory studies: regression analysis (Ardö, 1992;Gasparri et al., 2010;Lu, 2005;Salvador and Pons, 1998;West, 1995); k-nearest neighbor method (Franco- Lopez et al., 2001;McRoberts and Tomppo, 2007;Mäkelä and Pekkarinen, 2004;Tomppo et al., 2008); neural networks ( Foody et al., 2003); and fuzzy logic ( Triepke et al., 2008). Remote sensing data, geographic information systems, and modeling are now often used in ecological studies ( Cohen and Goward, 2004). ...
... Most AGB studies have been carried out in uniform boreal forests of coniferous plantations ( Häme et al., 1997;Muukkonen and Heiskanen, 2005) and temperate and tropical forests (Phua and Saito, 2003;Cutler et al., 2012). There is a lack of experience for estimating AGB in Mediterranean environments (Salvador and Pons, 1998). However, the study of Sevillano-Marco et al. (2013) can be mentioned. ...
... Pero los ámbitos de aplicación han sido, en su mayoría , bosques boreales densos, homogéneos y de topografía simple, siendo pocas las experiencias en ámbitos mediterráneos. Las características intrínsecas de éstos (estructura abierta, fragmentación, presencia de otros elementos de paisaje, topografía irregular…) hacen que parcelas con una misma cantidad de biomasa puedan presentar alta variabilidad espectral, dificultando el ajuste de los modelos (Salvador y Pons, 1998a, 1998b; Mallinis et al., 2004). Aunque la biomasa no puede ser medida directamente mediante imágenes de satélite, la reflectividad puede relacionarse con la biomasa estimada a partir de trabajo de campo (Dong et al., 2003); este principio se puede aplicar a la BRF. ...
Article
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This paper evaluates the influence of heterogeneity -mediterranean forest characteristic which induces difficulty on forest parameters estimation using remote sensing- in the performance of logistic regressions to estimate residual biomass using Landsat TM images and ancillary data (DEM and forest map) in the pine forest of Teruel. The Pearson's coefficient of variation (CV) was calcula- ted in the six spectral reflectance TM bands applying a 3x3 pixel window centred at each of the 482 plots of the 2 nd Spanish Forest Inventory. Several models according to the degree of heterogeneity were performed.
... Other settings guiding the segmentation routine include color-shape 0.8-0.2 and smoothness-compactness 0.5-0.5. The homogeneity criteria included the visible and NIR bands with similar weight, and an aspect layer derived from the DEM to incorporate topographic information as one of the possible structural driving factors [59] was weighted 0.1. ...
Article
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Forest structural parameters such as quadratic mean diameter, basal area, and number of trees per unit area are important for the assessment of wood volume and biomass and represent key forest inventory attributes. Forest inventory information is required to support sustainable management, carbon accounting, and policy development activities. Digital image processing of remotely sensed imagery is increasingly utilized to assist traditional, more manual, methods in the estimation of forest structural attributes over extensive areas, also enabling evaluation of change over time. Empirical attribute estimation with remotely sensed data is frequently employed, yet with known limitations, especially over complex environments such as Mediterranean forests. In this study, the capacity of high spatial resolution (HSR) imagery and related techniques to model structural parameters at the stand level (n = 490) in Mediterranean pines in Central Spain is tested using data from the commercial satellite QuickBird-2. Spectral and spatial information derived from multispectral and panchromatic imagery (2.4 m and 0.68 m sided pixels, respectively) served to model structural parameters. Classification and Regression Tree Analysis (CART) was selected for the modeling of attributes. Accurate models were produced of quadratic mean diameter (QMD) (R 2 = 0.8; RMSE = 0.13 m) with an average error of 17% while basal area (BA) models produced an average error of 22% (RMSE = 5.79 m 2 /ha). When the measured number of trees per unit area (N) was categorized, as per frequent forest management practices, CART models correctly classified 70% of the stands, with all other stands classified in an adjacent class. The accuracy of the attributes estimated here is expected to be better when canopy cover is more open and attribute values are at the lower end of the range present, as related in the pattern of the residuals found in this study. Our findings indicate that attributes derived from HSR imagery captured from space-borne platforms have capacity to inform on local structural parameters of Mediterranean pines. The nascent program for annual national coverages of HSR imagery over Spain offers unique opportunities for forest structural attribute estimation; whereby, depletions can be readily captured and successive annual collections of data can support or enable refinement of attributes. Further, HSR imagery and associated attribute estimation techniques can be used in conjunction, not necessarily in competition to, more traditional forest inventory with synergies available through provision of data within an inventory cycle and the capture of forest disturbance or depletions.
... Alves et al., 2003) is remote sensing. Based on the spectral reflectance of different vegetation types, the use of multispectral images provide a means for vegetation classification and mapping (Salvador and Pons, 1998;Lillesand and Kiefer, 2000). The basic features and suitability of satellite imagery as a source of data for vegetation mapping have been discussed in Scott et al. (2002) as well as Alexander and Millington (2000). ...
... Thirdly, for the assessment of extensive forest areas, occupying tens or hundreds of thousands of square kilometres in area (Chambers et al., 2007;Saatchi et al., 2007), it can be considered impractical to organize plot surveys. Because of these limitations, researchers in the last 20 years have attempted to correlate the spectral characteristics of the forest canopy (observed from satellite sensors) with plot-based, biophy-sical data (e.g., Lucas et al., 1993;Ekstrand, 1994;Imhoff, 1995;Foody et al., 1996Foody et al., , 2001Salvador and Pons, 1998;Hyyppä et al., 2000;Steininger, 2000;Labreque et al., 2006;Meng et al., 2007). Within tropical rain forest regions, such of Borneo Island, much remote sensing of biomass has been undertaken using the Landsat 'Thematic Mapper' (TM) sensor (see e.g., Jusoff and Hassan, 1998;Foody et al., 2001Foody, 2003;. ...
Article
Estimates of biomass integrated over forest management areas such as selective logging coupes, can be used to assess available timber stocks, variation in ecological status and allow extrapolation of local measurements of carbon stocks. This study uses fifty 0.1 ha plots to quantify mean tree biomass of eight logging coupes (each 450–2500 ha) and two similarly sized areas in un-logged forest. These data were then correlated with the spectral radiance of individual Landsat-5 TM bands over the 15 km × 15 km study area. Explanation of the differences in radiance between the ten forest sites was aided by measurements of the relative reflectance of selected leaves and canopies from ground and helicopter platforms.The analysis showed a marked variation in the stand biomass from 172 t ha−1 in coupe C88 that was disturbed by high-lead logging to 506 t ha−1 in a similarly sized area of protection forest. A two-parameter linear model of Landsat TM radiance in the near-infrared (NIR) band was able to explain 76% of the variation in the biomass at this coupe-scale. The local-scale measurements indicated that the differences in the mean radiance of each coupe (in cloud-free areas) may relate to a change in the proportion of climax tree canopy relative to a cover of either pioneer trees or ginger/shrubs; the canopies of climax trees have the lowest NIR radiance of the vegetation characteristic of selectively logged forest. The coupe harvested following ‘Reduced Impact Logging’ guidelines had a residual biomass and NIR radiance more like that of undisturbed lowland dipterocarp forest than coupes disturbed by ‘conventional’ selection felling. The predictability of tree biomass (at the coupe-scale) by such a parsimonious model makes remote sensing a valuable tool in the management of tropical natural forests.
... This methodology is time-consuming, expensive and requires a large number of sample plots to be observed. As a result, the data obtained represent only a discrete sample in a continuous spatial domain (Salvador and Pons, 1998). In order to overcome the discontinuity problem, the possibility of estimating forest characteristics from remotely sensed data has been investigated for more than two decades (e.g. ...
Article
Full-text available
This paper describes evaluation of forest stand density combining satellite imagery with forest inventory data set. The degree of canopy cover is described in terms of fractional vegetation cover (FVC) obtained by a linear mixture model applied on multi-spectral IKONOS image and canopy cover (CC). CC was calculated from field measurements of crown width of 646 standing trees sited within 72 circular (200 m2) plots. A comparison between CC and FVC shows that the former can be accurately represented by the latter linking in-situ measured forest characteristics with surface reflectance measured by a satellite.Stand density expressed as an absolute term (number of trees per unit area) showed high and significant positive correlation to FVC (R2 = 0.96) and to relative density measure (Crown Competition Factor; R2 = 0.89).In order to show the applicability of the presented approach for managerial practices, a map of the spatial distribution of stand density within the forest was produced using the above-mentioned correlations. Its quality was verified against an independent data set of ground measurements. The correlation between field- and map-based number of trees per unit area was found to be satisfactory (R2 = 0.4; p < 0.05), even though a slight lack of sensitivity was evident for low-density stands.
... Numerous studies have been conducted for a variety of canopy cover estimation based on various remote sensing data such as optical image, LiDAR. Landsat TM imagery combined with fieldwork were used to estimate tree canopy cover in forest inventories in the Mediterranean region, relying on regression models [1] . A cross-comparison of forest canopy cover estimation using field, spectral and LiDAR has been researched, and the statistical equivalence tests indicate that spectral and field data are not equivalent, while the LiDAR and field data are within the acceptable error margin of most forest inventory assessments [2] . ...
Conference Paper
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Forest canopy cover is retrieved Synchronous by the airborne LiDAR data and SPOT-5 HRG data, using the method of linear spectrum decomposition model combined with Li-Strahler geometric-optical model. First, the airborne LiDAR data is used to retrieve forest parameters and then the proportion of pixel not covered by crown or shadow Kg of each pixel in the sample was calculated using Li-Strahler geometric-optical model, while the linear spectrum decomposition model is used to extract the proportion of pixel not covered by crown or shadow Kg of each pixel in the whole imagery.
... Nevertheless, they succeeded in developing landscape-scale models for spatially explicit assessment of potential and present habitat suitability for eight rare plant species. Salvador and Pons (1997) reported that models derived from Landsat TM images should be regarded as exploratory models rather than fully operational ones. However, satellite images have been used successfully in vegetation or single plant species mapping in several studies (Ramirez-Garcia and others 1997;Gould 2000;Armenteras and others 2003;Mü ller and others 2003;Berberoglu and others 2004). ...
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Habitat loss and modification is one of the major threats to biodiversity and the preservation of conservation values. We use the term ''conservation value'' to mean the benefit of nature or habitats for species. The importance of identifying and preserving conservation values has increased with the decline in biodiversity and the adoption of more stringent environmental legislation. In this study, conservation values were considered in the context of land-use planning and the rapidly increasing demand for more accurate methods of predicting and identifying these values. We used a k-nearest neighbor interpreted satellite (Landsat TM) image classified in 61 classes to assess sites with potential conservation values at the regional and landscape planning scale. Classification was made at the National Land Survey of Finland for main tree species, timber volume, land-use type, and soil on the basis of spectral reflectance in satellite image together with broad numerical reference data. We used the number and rarity of vascular plant species observed in the field as indicators for potential conservation values. We assumed that significant differences in the species richness, rarity, or composition of flora among the classes interpreted in the satellite image would also mean a difference in conservation values among these classes. We found significant differences in species richness among the original satellite image classes. Many of the classes examined could be distinguished by the number of plant species. Species composition also differed correspondingly. Rare species were most abundant in old spruce forests (>200 m3/ha), raising the position of such forests in the ranking of categories according to conservation values. The original satellite image classification was correct for 70% of the sites studied. We concluded that interpreted satellite data can serve as a useful source for evaluating habitat categories on the basis of plant species richness and rarity. Recategorization of original satellite image classification into such new conservation value categories is challenging because of the variation in species composition among the new categories. However, it does not represent a major problem for the purposes of early-stage land-use planning. Benefits of interpreted satellite image recategorization as a rapid conservation value assessment tool for land-use planners would be great.
Thesis
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O objetivo deste trabalho foi estimar o volume de madeira de um povoamento jovem de Pinus elliottii, localizado no litoral sudeste do Rio Grande do Sul, com imagens dos sensores LISS-III/ResourceSat-1 e TM/Landsat 5, comparando o desempenho destes para tal. Obtiveram-se imagens de setembro de 2010, mês coincidente com o inventário florestal feito na área de estudo. Os valores de reflectância espectral de superfície foram recuperados das imagens originais. Após o georreferenciamento, dos pixels coincidentes com a localização das unidades amostrais do inventário florestal foram extraídos os valores das reflectâncias nas quatro bandas espectrais equivalentes aos dois sensores, cujas respostas foram comparadas. Além das bandas espectrais foram utilizados os índices de vegetação (IV’s) SR, NDVI, SAVI, MVI e GNDVI. Também, foi proposto o ajuste destes IV’s originais pela idade do povoamento, os quais foram identificados por SR_i, NDVI_i, MVI_i e GNDVI_i. A aplicação do logaritmo nas bandas espectrais melhorou os valores dos coeficientes de correlação linear (r), à exceção do IVP, retornando valores entre 0,69 (IVP) a 0,83 (Verde) para o LISS-III e entre 0,68 (Vermelho) a 0,79 (IVM) para o TM; Com os IV’s o logaritmo melhorou os valores de r somente para os IV’s originais, retornando valores de r entre 0,77 (NDVI) a 0,84 (GNDVI) com o LISS-III e entre 0,73 (NDVI) a 0,82 (MVI) para o TM. Com os IV’s ajustados pela idade do povoamento a logaritimização não se mostrou necessária para melhorar a associação linear, retornando valores de r entre 0,79 (NDVI_i) a 0,82 (MVI_i) com o LISS-III e entre 0,74 (SR_i) a 0,80 (MVI_i) com o TM. Além disso, o ajuste pela idade aumentou o intervalo dinâmico dos IV’s ajustados, e, aparentemente, aumentou a sensibilidade nos povoamentos de maior volume. Diferenças significativas na associação linear entre os dados espectrais do TM e LISS-III com o volume só foram encontradas na banda equivalente do verde. Com dados TM, a equação melhor ajustada explicou 68% da variabilidade do volume; com dados LISS-III a equação explicou 72% da variabilidade. Estas equações geraram dois mapas de volume de madeira, onde as médias das estimativas obtidas com LISS-III estiveram dentro do intervalo de confiança da média do inventário florestal em 70% dos talhões considerados; para o TM a coincidência foi de 65% dos talhões. Conclui-se que os sensores LISS-III e TM apresentam alta similaridade e que a metodologia empregada pode ser utilizada para auxiliar no inventário florestal dos povoamentos jovens de P. elliottii na área de estudo principalmente pelo fato das estimativas obtidas pelas imagens cobrirem todo o talhão, ao passo que a amostragem do inventário florestal contempla menos de 2% da área.
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Procurou-se estabelecer a relação entre o Índice de área foliar no campo (IAFc) de plantios clonais de Eucalyptus saligna Smith e três diferentes Índices de Vegetação (IV) obtidos de uma imagem Landsat 8/OLI: Índice de Vegetação da Diferença Normalizada (NDVI), Índice da Razão Simples (SRI) e Índice de Vegetação Ajustado para o Solo (SAVI), com o objetivo de selecionar o melhor estimador do IAF por sensoriamento remoto (IAFSR), obtendo assim a espacialização IAF nos talhões. O IAFc foi obtido utilizando o equipamento LAI-2000 e seu comportamento foi analisado em diferentes idades. Os índices de vegetação foram obtidos por meio de aritmética das bandas 4 e 5 do sensor. A análise de regressão linear simples foi utilizada para ajustar o modelo de estimativa de IAFSR (IAFSRi= β0 + β1 . IVi + εi), sendo os critérios de escolha as estatísticas de R2, Syx% e análise de resíduos. Os resultados mostraram que o índice que melhor estimou o IAFSR foi o SRI (IAFSR=-5,6159 + 0,9716 . SRI), com R2=0,68 e Syx%=12,5. Todos os modelos ajustados mostraram tendência em subestimar e superestimar o IAF. As equações obtidas para as diferentes idades não produziram melhora nas estimativas de IAFSR.
Conference Paper
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https://elibrary.ru/item.asp?id=26663916 The study region is located in western Turkey, Izmir surroundings. The research examines spatiotemporal changes in landscapes of Izmir region, during the decade 1995-2005. Methods entail raster processing, spatial analysis and mapping by means of geospatial techniques and Erdas Imagine software. The Landsat TM images were geo-processed for detection of changes in the land cover types. It demonstrated effective use of the remote sensing data and spatial analysis for vegetation studies: a combination of digital satellite images, GIS cartographic tools and methods of spatial analysis of vegetation coverage are highly suitable and efficient for the monitoring of highly heterogeneous landscapes located in the area of intensive anthropogenic activities (western Turkey). The study contributes towards technical development of cartographic methods of the environmental monitoring.
Article
Spatial patterns of Mediterranean woodlands are largely unknown. An exploratory analysis of the spatial structure of basal area was carried out in this type of community. Specifically, an area of non-disturbed woodlands located in the north-east of the Iberian peninsula was analysed. Direct and indirect evidences for the hypothesis stating that environmental factors lead to the establishment of spatial patterns on the basal area of non-disturbed Mediterranean woodlands were investigated. The results showed that the variability of this parameter occurs within really short spatial intervals (of less than 100m) and neither radiation nor the slope seemed to have an important effect on it. In consequence, results did not support the previously mentioned hypothesis. Among others, long term effects of forest management, an uneven presence of relief conditions and a complex dynamics of basal area within plots were suggested as explanations for the results found. Finally, some recommendations and practical advises were set for future inventories and studies of spatial patterns in Mediterranean woodlands.
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Variation in forest structure provides information on vegetation complexity and provides insights on biodiversity. Characterizing forest structural diversity with remotely sensed data supports reporting, monitoring, and policy development. We explored the relationship between forest structural diversity in Mediterranean pines of the Spanish Central Range and variables derived from imagery captured with a commercial high spatial resolution satellite (QuickBird-2; with pixels sided 2.4 m multispectral and 0.68 m panchromatic). To combine multiple aspects of tree conditions at a stand level, "structural diversity'' was characterized at the plot level (N = 1022) as a linear combination of the median of absolute differences of individual trees' bole diameter, height, and crown diameter measured on the field from the local median equivalents. Spectral reflectance variations in the visible and near-infrared, as well as image co-occurrence texture metrics from the panchromatic imagery at various window sizes were generated. All relationships to image-derived values were assessed against circular 0.3 ha areas corresponding with the field measured plots. Canonical correlation analysis aided in identification of combinations of reflectance and texture metrics most highly related with forest structural diversity (R = 0.89). Reflectance diversity was found to be more important than co-occurrence texture features in describing forest structural diversity when forest structure was limited (R = 0.47 vs. R = 0.39), whereas texture was more informative to the model when the forest structural diversity was high (R = 0.88 vs. R = 0.63), relating more complex forest conditions. Our results, although empirically defined by the local conditions and image acquisition characteristics, demonstrated the potential in high spatial resolution imagery for description of forest structural diversity in forests of the Mediterranean environment, especially important for Spain where a national high spatial resolution image data base has been collected.
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Mediterranean pines are subject to continuous change under the influence of natural and human factors. Remotely sensed data provide a means to characterize these changes over large areas. In this study we used a time series of Landsat imagery to capture 25 years (1984–2009) of change in the pine-dominated forests of the Central Range in Spain. Object-based image analysis methods were used to identify landscape-level changes in the area and the distribution of forests. We also propose that in the absence of disturbance, biomass accrual is occurring (or depletion in cases where removal is evident) and may be related to changes to the carbon stock; we describe the detected spectral changes in terms of biomass changes as the carbon stocking process. The primary inputs for the identification of changes in the area and distribution of pine stands were Landsat bands 3, 4 and 5 and the Tasseled Cap Angle (TCA) – a metric derived from the greenness and brightness components of the Tasseled Cap Transformation (TCT). In the identification of carbon stocking processes the temporal derivative of the TCA, the Process Indicator (PI), was used to inform on the rate and directionality of the change present. Our results show that the total area of pine forest has increased by 40%, from 1211 km to 1698 km, during this period, with a variable rate of change. The distribution of pine-dominated forest has changed as well: there is an area of 765 km permanently covered with pines and 945 km found to be temporarily occupied. Following the logic of carbon stocking processes, our findings show that at the end of the analysis period, 20% of the potential pine area is increasing its carbon stock and 40% of this area is experiencing a decrease.
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Satellite remote sensing provides new possibilities and challenges to forest managers for monitoring and managing forest ecosystems. The value/use of high-resolution satellite data of Landsat Thematic Mapper (TM) to estimate tree density, basal area, basal volume, and forest biomass was investigated under an operational perspective in a spatially heterogeneous Mediterranean landscape in northern Greece. Digital classification using Fisher's linear discriminant functions produced an overall accuracy of 82%. A series of multispectral transformations were also performed, and the derivative synthetic channels were used along with the original ones as explanatory variables in the model developed. The forest stand parameters that were investigated in the study presented a similar but weak correlation with the Landsat-5 TM spectral channels. Among them, TM4 and TM7 presented the highest correlation with forest stand parameters, while the integration of the derivative synthetic channels increased the correlation coefficients and resulted in statistically significant predictive models. FOR. SCI. 50(4):450–460.
Chapter
Forest types can be adopted as a suitable reference for classifying survey units within multipurpose forest resources inventories, at the properly considered level. This kind of hierarchical classification approach integrates an ecologically meaningful per-habitat perspective with practical survey, planning and management requirements. Advanced remote sensing technologies can be valuable tools for a cost-effective implementation of such an approach. In the present paper, data from high (Landsat 7 ETM+) and very high (Ikonos) spatial resolution satellite sensors were tested to understand their potential contribution supporting stand-level forest type mapping under Mediterranean conditions. Ikonos and Landsat images were used to differentiate forest coverages by so called soft classifiers: fuzzy maximum likelihood procedure for Ikonos and subpixel unmixing procedure for Landsat. Fuzzy classified images are then contrasted with forest type map made by photointerpretation of Ikonos imagery. Perfomances are showed and drawbacks discussed.
Article
We analyze the capability of Hyperion spaceborne hyperspectral data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000). For this purpose, we used Hyperion imagery acquired over Pollino National Park (Italy).Hyperion pre-processed data (30 m spatial resolution) were classified at the pixel level using common parametric supervised classification methods. The algorithms' performance and class level accuracy were compared with those obtained for the same area using airborne hyperspectral MIVIS data (7 m spatial resolution).Moreover, in selected test areas characterized by heterogeneous land cover (as mapped by MIVIS classification) a Linear Spectral Unmixing (LSU) technique was applied to Hyperion data to derive the abundance fractions of land cover endmembers. The accuracy of the LSU analysis was evaluated using the Residual Error parameter, by comparing Hyperion LSU results with land cover fractional abundances achieved from reference data (i.e., MIVIS and air-photo classification).The results show the potential of Hyperion spaceborne hyperspectral imagery in mapping land cover and vegetation diversity up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem such as that of Pollino National Park. Moreover, we defined a criterion for evaluating the Hyperion accuracy in retrieving land cover abundances at the sub-pixel scale. Sub-pixel analysis allowed us to determine the optimal threshold to select the areas on which consistent fractional land cover monitoring can be achieved using the Hyperion sensor.
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The montado/dehesa landscapes of the Iberian Peninsula are savannah-type open woodlands dominated by evergreen oak species (Quercus suber L. and Q. ilex ssp. rotundifolia). Scattered trees stand over an undergrowth of shrubs or herbaceous plants. To partition leaf area index between trees and the herbaceous/shrubby understorey requires good estimates of tree canopy cover and is of key importance to understand the ecology and the changes in land cover. The two vegetation components differ in phenology as well as in radiation and rainfall interception, water and CO2 fluxes. The main goal of this study was to estimate tree canopy cover in a montado/dehesa region of southern Portugal (Alentejo) using remote sensed data. For this purpose we developed empirical models combining measurements obtained through the analysis of aerial photos and reflectance from Landsat Thematic Mapper (TM) individual channels, vegetation indices, and the components of the Kauth–Thomas (K–T) transformation. A set of 142 plots was designed, both in the aerial photos and in the satellite data. Several simple and multiple linear regression models were adjusted and validated. A subset of 75% of the data (n = 106) was used for model fitting, and the remainder (n = 36) was used for model assessment. The best linear equation includes Landsat TM channels 3, 4, 5 and 7 (r2 = 0.74), but the Normalised Difference Vegetation Index (NDVI), the components of the K–T transformation, and the Atmospherically Resistant Vegetation Index (ARVI) also performed well (r2 = 0.72, 0.70, and 0.69, respectively). The statistics of prediction residuals and tests of model validation indicates that these were also the models with better predictive capability. These results show that detection of low/medium tree canopy cover in this type of land cover (i.e. evergreen oak woodlands) can be accomplished with the help of high and medium spatial resolution satellite imagery.
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Multispectral SPOT imagery is used to map percentage vegetation cover in the Mackenzie Basin of the South Island, New Zealand. Twenty ground measurements of quadrats 60 by 60 m2 relate percentage vegetation cover to normalized vegetation index. The non-linear calibration curve is C=50 tanh (6.1(NVI-0.22)) + 50 where C is percentage vegetation cover and NVI is normalized vegetation index. The vertical spread of quadrat points about the calibration curve gives the accuracy of a percentage cover prediction from NVI. In the linear part of the calibration curve the upper and lower prediction limits (80% confidence interval) are plus or minus 15% cover. -Authors
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This study focuses on the geometrical deformations introduced by relief in images captured by the TM sensor of Landsat satellites and by the HRV sensor of SPOT satellites. Different correction alternatives are presented in order to incorporate altitude data into correction procedures based on first-degree polynomial models. Column and row determinations from the corresponding map coordinates are carried out independently. Three different models for columns and two for rows are proposed. The results have been contrasted with those obtained using classic first- and second-degree polynomial calculations, and with those obtained using an orbital model (for SPOT images). The models presented are easy to implement and provide a level of precision similar to that of the orbital model used, while they are much more efficient in calculation time. In view of the results, the model which integrates altimetric data into a single first-degree polynomial seems of particular interest.
Article
Two problems are identified in the use of linear regression to relate remotely sensed data to ground variables: a specification problem and an errors problem. The extent of the errors is examined for commonly measured remotely sensed variables and ground variables. Three alternative methods of line fitting are examined: Wald's grouping methods, the reduced major axis, and a least- squares procedure. The least-squares method is recommended if the data are available.-Authors
Article
Remote sensing of forest biophysical properties has concentrated upon forest sites with a wide range of green vegetation amount and thereby leaf area index and canopy cover. However, coniferous forest plantations, an important forest type in Europe, are managed to maintain a large amount of green vegetation with little spatial variation. Therefore, the strength of the remotely sensed signal will, it is hypothesized, be determined more by the structure of this forest than by its cover. Airborne Thematic Mapper (ATM) and SPOT-1 HRV data were used to determine the effects of this structural variation on the remotely sensed response of a coniferous forest plantation in the United Kingdom. Red and near infrared radiance were strongly and negatively correlated with a range of structural properties and with the age of the stands but weakly correlated with canopy cover. A composite variable, related to the volume of the canopy, accounted for over 75% of the variation in near infrared radiance. A simple model that related forest structural variables to the remotely sensed response was used to understand and explain this response from a coniferous forest plantation.
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Effective forest management requires reliable forecasts of timber growth. In New Brunswick, the growth of spruce-fir stands is greatly affected by annual and cumulative defoliation caused by the spruce budworm. This study was designed to determine whether the Thematic Mapper (TM) could detect defoliation in a way which was correlated with stand growth well enough to be used as a predictive tool. Regression analysis was used to explore relationships between stand reflectance in the Thematic Mapper bands and stand volume and growth measured in permanent sample plots. Good (R 2>0.8) relationships were found between net annual spruce-fir volume change and vegetation condition indices incorporating TM near-infrared and shortwave infrared reflectances. Good relationships were also found between live spruce-fir volume and TM band 4 (near-infrared) reflectance. These results have encouraged us to plan a province-wide calibration and measurement programme aimed at improved stand development forecasts for the 1992 New Brunswick timber inventory and allocation.
Article
The purpose of this study is to assess the potential of estimating the area, age and changes of tropical secondary forest regrowth from the Landsat Thematic Mapper (TM). Sites of mature forest, agriculture, pasture, pasture with remnant trees, and stands of secondary forest regrowth from 2 to 19 years of age were surveyed in two study areas near Manaus, Amazonas, Brazil, and mapped to the TM imagery. Considerable changes in spectral reflectance were observed over the first 19 years of regrowth, and these can be summarized by indices related to canopy brightness and greenness. The near-infrared reflectance, the difference index, Kauth-Thomas greenness, and percentage leaf cover all increase over the first 4 years after abandonment, peak from 4 to 8 years, and decrease from 8 to 13 years. The Normalized Difference Vegetation Index (NDVI) rapidly rises over the first 4 years, and displays no apparent relation to stand age thereafter. The brightness of regrowth canopies decreases from 8 to 13 years. Stands from 13 to 19 years-old are spectrally similar to mature forest. All regrowth age classes are spectrally distinct from agriculture and pasture, and all regrowth age classes younger than 14 years are spectrally distinct from mature forest. Spectral indices of canopy brightness are significantly correlated with regrowth stand age (r 2≤0·48, p<0·001). Based on field research in several areas of Amazonia, these spectral patterns can be explained in terms of temporal changes in canopy geometry and leaf area. The estimated rates of regrowth clearance and reversion to regrowth between 1988 and 1991 suggest the intensity of rotational agriculture practised by both communities. These results suggest that monitoring tropical secondary regrowth with TM imagery could substantially improve estimates of carbon sequestration subsequent to tropical deforestation.
Article
Models using NOAA AVHRR data for estimating the areal distribution of biomass over the Boreal coniferous zone are developed. The method uses corresponding models that are estimated using Landsat TM data as input, and includes a mosaicing scheme for AVHRR images to cover very extensive areas. The estimation method may be adjusted to some other forest characteristics too.
Article
In this study we evaluate the possibilities of the airborne sensor CASI (Compact Airborne Spectrographs Imager) when applied to mapping of variables characteristic of an ecologic and forestry inventory, i.e., wood biomass, leaf biomass, leaf area index (LAI), canopy cover, etc. The use of multiple regression techniques has made possible to obtain correlation values between 0.76 and 0.94 (p<0.05), between radiometrical and forestry data. The extrapolation ability of the models obtained has been verified starting from the comparison among different flight lines and several groups of sample areas.
Article
In order to obtain a model equation for the calculation of percentage plant cover by multi-spectral radiances remotely-sensed by satellites, a regression procedure is used to connect space remote-sensing data to ground plant cover measurement. A traditional linear regression model using the normalized difference vegetation index (NDVI) is examined by remote-sensing data of the SPOT satellite and ground measurement of LCTA project for a test site at Hohenfels. Germany. A relaxation vegetation index (RVI) is proposed in a non-linear regression modelling to replace the NDVI in linear regression modelling to get a better calculation of percentage plant cover. The definition of the RVI iswhere Xi is raw remote-sensing data in channel i. Using the RVI, the correlation coefficient between calculated and observed percentage plant cover for a test scene in 1989 reaches 0·9 while for the NDVI it is only 0·7; the coefficient of multiple determination R reaches 0·8 for the RVI while it is only 0·5 for the NDVI. Numerical testing shows that the ability of using the RVI to predict percentage plant cover by space remote-sensing data for the same scene or the scene in other years is much stronger than the NDVI.
Article
This study aims to (i) investigate the relationship between spectral radiance recorded by the Landsat Thematic Mapper and the volume (m ha) of forest compartments in a coniferous forest area in southern Sweden and (ii) establish and evaluate a regression for volume estimation of forest compartments. A regression of volume on spectral radiance of TM5 was established with Wald's method.Spectral radiance in Landsat TM band 5 had the largest correlation with field measurements of volume, r =−0·79. The correlation coefficient between observed volume and estimated volume was 0·83 and the standard error of estimate was 46·5mha. Accepting an error of ± 50mha in the estimation resulted in a classification accuracy of 77 per cent. It was concluded that there is a stronger relationship between spectral radiance and volume for compartments with small volumes than for compartments with large volumes.
Article
The utility of satellite data in forest decline assessment is influenced by effects associated with variations in stand characteristics such as species composition, age, and density. In this study, the spectral effects of stand variations were determined for forest dominated by Norway spruce. The usefulness of digitized stand data for potentially reducing these effects were investigated. It is suggested that a damage estimation algorithm based solely on Landsat TM Band 4 is more appropriate than earlier proposed ratio algorithms in areas with moderate defoliation symptoms but no chlorosis. The two factors with the strongest negative effect on the defoliation assessment were varying hardwood and pine component. A hardwood component of 20% completely neutralized a defoliation of 20%. Age had a clear spectral effect up to 70 years, but above that the response was stable. There was no confusion between defoliation classes in stands with moderate to high density. A spruce defoliation model that used stand data from digitized forest maps to modify the intensity values of TM Band 4 prior to estimation of defoliation was developed. The resulting assessment of moderate defoliation in forest areas on level ground was of adequate accuracy when the spruce component was larger than 75%.
Article
This fourth and full colour edition updates and expands a widely-used textbook aimed at advanced undergraduate and postgraduate students taking courses in remote sensing and GIS in Geography, Geology and Earth/Environmental Science departments. Existing material has been brought up to date and new material has been added. In particular, a new chapter, exploring the two-way links between remote sensing and environmental GIS, has been added. New and updated material includes: A website at www.wiley.com/go/mather4 that provides access to an updated and expanded version of the MIPS image processing software for Microsoft Windows, PowerPoint slideshows of the figures from each chapter, and case studies, including full data sets, Includes new chapter on Remote Sensing and Environmental GIS that provides insights into the ways in which remotely-sensed data can be used synergistically with other spatial data sets, including hydrogeological and archaeological applications, New section on image processing from a computer science perspective presented in a non-technical way, including some remarks on statistics, New material on image transforms, including the analysis of temporal change and data fusion techniques, New material on image classification including decision trees, support vector machines and independent components analysis, and Now in full colour throughout. This book provides the material required for a single semester course in Environmental Remote Sensing plus additional, more advanced, reading for students specialising in some aspect of the subject. It is written largely in non-technical language yet it provides insights into more advanced topics that some may consider too difficult for a non-mathematician to understand. The case studies available from the website are fully-documented research projects complete with original data sets. For readers who do not have access to commercial image processing software, MIPS provides a licence-free, intuitive and comprehensive alternative.
Article
Providing a wide-ranging introduction to the use of linear models in analyzing data, this text presents a vector space and projections approach to the subject. The topics covered include ANOVA, estimation, hypothesis testing, multiple comparison, regression analysis, and experimental design. Also covered are: testing for lack of fit; models with singular covariance matrices; variance component estimation; best linear prediction; colinearity; and variable selection.
Article
Landsat TM data may be of use for forest timber and vitality monitoring. Literature study reveals inconsistent results of techniques based on spectral indices and regression. Existing models appear incomplete because they do not account for shadowing between trees, or because they treat trees as opaque bodies. For this reason a new forest light interaction model has been developed. It accounts for both shadowing and crown transparency. The inverse model has been implemented as an image processing algorithm. Model results are studied and compared with ground data from the Kootwijk forest in The Netherlands. Results indicate that the model gives a fair representation of reality. In this respect, the utility of the NDVI is also discussed. More research is necessary for further validation.
Article
Recent research has shown that general trends in forest leaf area index along regional climatic gradients can be adequately characterized by using ratios of near-infrared and red reflectances. However it has proven difficult to represent properly the spatial distribution of Leaf Area Index (LAI) at subregional scales such as small catchments. The key problem at Thematic Mapper scale is the variation in canopy closure and understorey contribution, which dramatically influences near-infrared reflectance from conifer forests. In this paper, a new spectral index is presented to estimate LAI of conifer forests using a combination of Red, NIR and mid-IR reflectances from the Landsat Thematic Mapper (TM). A simulation system (RHESSys) was used first, to generate potential vegetation patterns around a watershed in order to test them against remotely-sensed vegetation patterns, and secondly, to test the sensitivity of forest ecosystem processes to LAI estimated from combinations of the Thematic Mapper data. The relation between Normalised Difference Vegetation Index (NDVI) and LAI is poorly defined at TM scale because of the outsized contribution of understorey vegetation and background materials to the NIR reflectance in open canopies. The mid-IR correction factor acting as a scalar for canopy closure scaled down the inflated NDVI in the open canopies, resulting in an improved relation between NDVI and LAI. LAI estimates from the MIR corrected NDVI better represented the vegetation patterns in Soup Creek watershed than those from uncorrected NDVI both in terms of magnitude and spatial patterns. Simulations using LAIs derived from corrected NDVI showed lower rates evapotranspiration and net photosynthesis. Differences in mean responses of evapotranspiration and photosynthesis were as large as 8 cm and 2 ton C ha-1 yr-1 respectively between simulation runs using LAIs from corrected and uncorrected NDVI.
Article
The use of field measures of slope angle, slope aspect, cover type, crown size and crown density is evaluated in appraising the variability of Landsat Multispectral Scanner (MSS) spectral responses for 182 sample sites within Crater Lake National Park, Oregon. Multiple linear regression models indicate that 73, 72, 71 and 57 percent of the variation in the mean response of MSS bands 4, 5, 6 and 7, respectively, was explained by the environmental variables entered into the models. In general, crown size and crown density are less important in altering spectral response than terrain orientation. This type of analysis is useful in guiding field work for remote sensing studies into areas that are environmentally diverse and which are, therefore, capable of significantly altering the spectral response of cover types.
Article
The aim of the study was to establish remote sensing models for the estimation of canopy cover in Acacia woodlands. The models were established using Landsat-TM and MSS data and SPOT HRV XS data and based on field data from eastern Sudan. The models were derived using the Reduced Major Axis (RMA) method. Correlation coefficients between NDVI and canopy cover are for Landsat-TM 0-552, for Landsat-MSS 0-698 and for SPOT HRV XS 0-718. The confidence intervals of predicted canopy cover are also presented.
Article
The prime objective of this study was to propose and test a method to identify the optimal spatial resolutions for detection and discrimination of coniferous classes in a temperate forested environment. The approach is based on the paradigm that there is an intricate relationship between the definition and the measurement of geographical entities and implies the following steps: 1) a priori define the geographical entities under investigation, 2)determine an optimization criterion for the choice of a sampling system, 3) progressively aggregate data acquired from a fine spatial sampling grid, 4) apply the optimization criterion on the series of spatially aggregated data, and 5) verify the validity of the results obtained in relation to the goal of the study. Airborne MEIS-II data, acquired at 0.5 m in eight spectral bands of the visible spectrum, were used for the study. Fourteen forest classes, at the stand level, were defined on the basis of four attributes: species, density, height, and organization of the trees. Representative sites for each forest class were selected. From the center of each site, the spatial resolution of the original data was degraded to 29.5 m, with an increment of 1 m, using an averaging window algorithm. The intraclass variance was calculated for each forest class, at every spatial resolution and for the eight spectral bands. The minimal variance was used as the indicator of the optimal spatial resolution. To evaluate the importance of the optimal resolution for class discrimination, a bivariate test of variance was performed for each pair of forest class considered at their optimal spatial resolution. Profiles of spectral separability were also established in relation to the whole series of spatial resolutions. The results show that, for all coniferous classes and for the eight spectral bands considered in the study, there is a minimal value in intraclass variance that indicates the optimal spatial resolution for each class, varying between 2.5 m and 21.5 m. The optimal spatial resolution is primarily affected by the spatial and structural parameters of the forest stands. The analysis of variance between each pair of forest classes considered at their respective optimal spatial resolution reveals that all classes are significantly different in at least two spectral bands, except for 10 pairs. The spectral separability of the forest classes is at a maximum at, or very close to, their optimal spatial resolution. The study confirms the validity of the concept of optimal spatial resolution and proposes an original solution to the problem of the adequate scale of measurement for geographical entities.
Article
A simplified model for radiometric corrections has been used to improve nonsupervised classification of vegetation cover in a hilly area near Barcelona, Spain. A digital elevation model and standard parameters for exoatmospheric solar irradiance, atmospheric optical depth, and sensor calibration are the only inputs required. Radiometric classes obtained by cluster classification of Landsat TM images from nonradiometrically corrected images include several classes related to terrain illumination, but not to vegetation or thematic cover differences. The use of radiometric correction allows identifying all radiometric classes obtained as vegetation or thematic classes with 83.3% global accuracy. Classes obtained include Pinus halepensis, Quercus ilex, and Quercus cerrioides forests, shrublands, grasslands, urban areas with vegetation, urban areas without vegetation, and denuded areas. Radiometric correction helps in estimating surfaces and spectral features of these classes. The results are discussed considering botanical composition, date (phenology), and vegetation dynamics.
Article
The leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than an annual average LAI. To determine if remotely sensed data can be used to estimate LAI at times throughout the year (herein termed seasonal LAI), field measurements of LAI were compared to normalized difference vegetation index (NDVI) values, derived using Landsat Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on three dates. Linear relationships existed between NDVI and LAI with R2 values of 0.35, 0.75, and 0.86 for February 1988, September 1988and March 1989, respectively. Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6% of the mean LAI. This demonstrates the potential use of Landsat TM data for studying seasonal dynamics in forest canopies.
Article
The main objective of this study was to establish a method of estimating tree density in savanna-like vegetation systems using the highest spatial resolution available from satellite data (SPOT-1 panchromatic = 10 m resolution) based on the assumption that for sparse trees on a contrasting herbaceous background, spatial filters may provide a direct mapping of tree cover. The study was performed in the 'dehesas' oak-woodland of southern Spain. This particular landscape is characterized by the presence of scattered evergreen oak trees (Quercus ilex and Q. suber) whose density ranges from 0 to 80 even-aged mature trees per hectare which gives the appearance of a savanna-like vegetation. Tree density can be accurately estimated by SPOT-1 panchromatic data after numerical filtering. This method allows the mapping of tree density of the dehesas, a key parameter reflecting the functional vegetation-soil-climate equilibrium which exists for both woody and herbaceous strata.
Article
Substantially revised and expanded, this new edition includes a discussion of the radiative transfer equation, atmospheric sounding techniques and interferometric radar, an expanded list of problems (with solutions), and a discussion of the Global Positioning System (GPS). This book forms the basis of an introductory course in remote sensing. The main readership will be students and researchers in remote sensing, geography, cartography, surveying, meteorology, earth sciences and environmental sciences generally, as well as physicists, mathematicians and engineers.
Article
Ground data from the Central Plains Experimental Range in northeast Colorado and Landsat satellite images of that area acquired in August 1989, June 1990, and September 1990 were used to evaluate the level of association that can be expected from a univariate model relating spectrally derived vegetation indices (difference, ratio, and normalized difference vegetation indices) and dried green vegetation biomass. The vegetation indices were related to the ground sample estimates using a sample point, spectral class, and greenness strata approach. No strong relationships were found between the vegetation indices and sample estimates of dried green biomass using the sample point approach. The spectral class approach produced significant results only for the June 1990 sample period (r=0.96). Significant relationships were found for the August 1989, June 1990, and September 1990 samples periods (r2=0.95, 0.71, and 0.95, respectively) when the data were aggregated by greenness strata. The high degree of association between green biomass and the NDVI, obtained when the data were combined into greenness strata, indicated that it is possible to predict green biomass levels on semiarid rangelands using univariate regression models.
Article
A text detailing critical aspects of geographic information systems for land resources assessment. Nine chapters cover themes on: 1. Overview of GIS components - computer mapping, data bases, future directions. 2. Data structures for thematic maps, including files, spatial data definitions, vector and raster structures, database facilities. 3. Digital elevation models. 4. Data input, verification, storage and output. 5. Methods of spatial data analysis and modelling, ranging from basic map overlay, to natural language processing. 6. Data quality, errors, and the nature of spatial data on maps. 7. Classification techniques - multivariate, and using expert systems. 8. Spatial interpolation methods. 9. Selecting a GIS. Each chapter has a list of references, and two appendices give a glossary of terms, and a list of selected information sources.-after Author
Article
Consideration is given to the effects of canopy closure, understory vegetation, and background reflectance on the relationship between Landsat TM data and the leaf area index (LAI) of temperate coniferous forests in the western U.S. A methodology for correcting TM data for atmospheric conditions and sun-surface-sensor geometry is discussed. Strong inverse curvilinear relationships were found between coniferous forest LAI and TM bands 3 and 5. It is suggested that these inverse relationships are due to increased reflectance of understory vegetation and background in open stands of lower LAI and decreased reflectance of the overstory in closed canopy stands with higher LAI.
Article
We assessed the statistical relations between Spectral Vegetation Indices (SVI's) derived from SPOT multi-spectral data and semi-arid shrub cover at the Jornada LTER site in New Mexico. Despite a limited range of shrub cover in the sample the analyses resulted in r(sup 2) values as high as 0 central dot 77. Greenness SVI's (e.g., Simple Ratio, NDVI, SAVI, PVI and an orthogonal Greenness index) were shown to be more sensitive to shrub type and phenology than brightness SVis (e.g., green, red and near-infrared reflectances and a Brightness index). The results varied substantially with small-scale changes in plot size (60m by 60m to 100m by 100m) as a consequence of landscape heterogeneity. The results also indicated the potential for the spectral differentiation of shrub types, and shrubs from grass, using multi-temporal, multi-spectral analysis.
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