Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

Abstract

Abstract: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). OPEN ACCESS Remote Sens. 2012, 4 136When 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.

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

“…VIF was normally employed to analyze multicollinearity and some variables indicating on collinearity (multicollinearity) might be removed, which resulted in model that explained less variance than the best possible full model with more variables. Therefore, more robust statistical methods, which did not need to make any assumptions about the data, such as artificial Neural Networks (ANN) [89][90][91], Classification and Regression Tree Analysis (CART) [22,92,93], and Random forests (RF) [88,94,95] were widely used to investigate complex relationship between forests stand variables and remotely sensed data. These robust statistical techniques should be given first priority in future remote sensing studies as many researches have already demonstrated that nonlinear interactions might exist between the observed data and remotely sensed data [88,90,96].…”

“…The panchromatic band is reported to be particularly well suited for the analysis of spatial relationships using image textural measures [22,[50][51][52]. As a result, we only extracted the second-order textural measures from the panchromatic band for each plot in comparison to the spectral and first-order textural measures.…”

“…Biodiversity is one of the most popular newly included variables as it is an essential prerequisite to support management decisions to maintain multiple forest ecosystem functions in the long term [19]. Forest inventory by field surveys can indeed provide us with the information about conventional forest stand variables and forest biodiversity for forest management planning as well as forest policy formulation, but they are time consuming, expensive and not spatially exhaustive [22,23]. Furthermore, these inventories are conducted periodically, e.g., five years for NFI and 10 years for FMPI, and therefore up-to-date information cannot be guaranteed.…”

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

“…[239]; (AGB), [240,241] Ordinary least squares (height, density, DBH), [242] Reduced major axis (AGB), [243]; (LAI), [244] Canonical Correlation Analysis (forest structural conditions), [222] Redundancy Analysis (forest structural conditions), [245,246] Trend analysis (growth), [247] Non-parametric regression kNN (AGB, carbon), [248] CART (tree cover), [249]; (basal area, no. of trees) [250] RF (AGB) [243,251] SVM (height, density, DBH), [242] Physical Radiative transfer/canopy reflectance model Geometric-Optical (LAI), [252]; (AGB), [253]; (Chlorophyll), [254] Turbid-medium (LAI), [255] hybrid (allometry), [256] Computer simulation…”

Tree number estimation with the use of VHR natural colour orthophotos over a heterogeneous landscape in northern Greece

“…Forest inventory information is required to support sustainable management, carbon accounting, and policy development activities (Gómez et al, 2012). 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.…”