Descriptive statistics.
Abstract
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.
Keywords
- QuickBird
- multi‐resolution segmentation
- crown horizontal projection
- forest inventory
- regressions
1. Introduction
1.1. Role of forest inventories in biomass estimation
Forest covers a larger area on Earth and provides many products and services. Forest evaluation inventories were initiated when wood shortages arose. It can be said that they are the driving force to acquiring information on forest areas, stand composition and products. In the beginning (Middle Ages), it was focused on timber volume and forest planning. With time demands for products and services changed, shifting their focus [1].
Forest inventories are based on a sampling design for a given threshold error, with ground plot assessment, providing data sets, which enable the forest evaluation. The need for measuring an increasing number of variables to evaluate products and services expected from forests turns them increasingly expensive and labour intensive. Remote sensing enabling the evaluation of some of those variables (
Biomass, both above and below ground, estimation, distribution and dynamics have been acquiring an increasing importance, especially in the last couple of decades. It was powered by the use of wood for bioenergy and by the evaluation of carbon stocks, sequestration and losses. The estimation of biomass can be grouped in two main methods: direct and indirect. The
The tree‐level biomass functions are frequently used to make estimations at plot scale (usually summing the biomass of all stems). Their estimation for a given area is based on the inventory plots (which depend on the sampling design and intensity for a certain error threshold) and an extrapolation method. In the literature, different extrapolation methods are described [1] with accuracy decreasing with the increase in the forest area, the variability of stand composition and structure, topography, soil and climate [13].
The forest inventories have cycles of 5 or 10 years. In between remote sensing can be used to evaluate the forest dynamics, in general, and biomass in particular. On the other hand, advantages can be gained as it can work at different scales and time frames; all area is under evaluation no requiring extrapolation methods; and maps can be produced in a geographical information system (GIS) environment.
A wide range of studies of biomass estimation derived from remote sensing can be found in the literature. In the next subchapter (2), a brief description of the methods and techniques is given. A case study (subchapter 3) will be used to illustrate the development of a methodology which was aimed to be simple and to be used either by researches or by technicians. The challenge was to produce data from remote sensing images for two evergreen oaks (holm and cork oak), native of the Mediterranean basin, and develop accurate above‐ground biomass (AGB) allometric functions whose explanatory variable is crown horizontal projection, for pure and mixed stands of both species, in the latter analysing also the influence of the predictive ability of independent variables that differentiate species and composition.
The innovation of this study is the estimation of above‐ground biomass considering or not stand composition, which is identified by the processing of high spatial resolution of satellite image data. In earlier studies, AGB estimation functions using satellite images did not consider the composition as independent variable. In the studies in which medium and low spatial resolution images are used, the pixel size frequently does not allow the separation of the species. High spatial resolution satellite images data enable the identification and delimitation of crowns of different tree species. Nonetheless, the studies with these satellite images seem to be focused in pure stands. The case study presented demonstrates that stand composition improves the accuracy of AGB estimation functions.
1.2. Contribution of remote sensing data to biomass estimation
Remote sensing was under a strong development in the last three decades due to the rapid advancement of remote sensing technology, increasing the availability of satellite imagery with different spectral, spatial, radioactive and temporal resolutions. Their sensor technologies enable a wide range of Earth surface monitoring scales [14] of forest areas distribution, species, and physic and biochemical properties [15]. The data can be derived from two sensors: the passive and the active.
In the
Passive sensor, satellite imagery data, depends on its spatial resolution to determine directly the working scale and can be divided into three groups: low, medium and high.
The
The
The data of
The three referred spatial resolutions of the optical remote sensing imagery for the same region and their image processing techniques allow the monitoring of AGB with different degrees of detail which are able to decrease the processing time and costs [19, 27, 52].
The
In summary, the estimation of forest AGB with remote sensing data has advantages and disadvantages. To obtain very good accuracy, these approaches need a heavy field work in order to attain training data sets. However, many studies present methodologies with very good results at local, regional and global scale for AGB estimation. There is a trend towards the combination of several types of remote sensing imagery data to generate vegetation parameters and their relation with forest biomass.
2. Case study
2.1. Introduction
The data resulting from passive sensors, with low and medium spatial resolution, are usually used for the country or global scales. Due to their pixel size, it is not possible to identify and delimitate the tree crowns. Conversely, high spatial resolution satellite images enable it with good accuracy. The active sensors processing is more difficult than the passive sensors. Thus, when developing a methodology to be used both by researches and by technicians, the passive sensors data processing is more straightforward than that of the active sensors. The objectives of this study are the development of allometric functions for the estimation of AGB with crown horizontal projection, obtained with high spatial resolution satellite image, as independent variable with linear regression for (i) cork oak pure stands, (ii) mixed cork oak and holm oak stands (from this point forward referred as evergreen oaks) and (iii) both pure and mixed stands (from this point forward referred as all). Though with similar stand parameters there are some differences between pure stands of cork oak or holm oak and mixed stands of both species. Thus it was considered that the model for both species might be improved with the inclusion of dummy variables for species and composition as independent variables.
Cork oak (
2.2. Materials and methods
2.2.1. Study area
The study area is in southern Portugal, region of Mora, with approximately 80 km2 (Figure 1). The area is mainly occupied by forest, composed predominantly by cork and holm oak in both pure and mixed stands. This region has a Mediterranean climate with a hot and dry summer and rainy winter with lower temperature. The terrain is characterised by small variations, with a mean elevation of about 200 m. The used QuickBird satellite image (August 2006) has a spatial resolution of 0.70 m, resulting from a fusion of panchromatic band with the four multi‐spectral bands, blue (B), green (G), red (R) and near infrared (NIR).
2.2.2. Processing satellite image
The image was geometric and radiometric corrected using ENVI4.8 [66]. The geometric correction was based on ground control points obtained with Global Navigation Satellite System (GNSS) and geodetic vertices, identified on the ground and in the images, with a Root Mean Square Error (RMSE) of 0.49 m. The pixel values that are the digital numbers of the image were converted to top‐of‐atmosphere reflectance and finally to soil reflectance, using the dark object subtraction method [67]. The Normalized Difference Vegetation Index (NDVI) [68] was calculated with the latter and used to generate a vegetation mask, isolating tree crowns from other land cover types. For this propose, the multi‐resolution segmentation method with the contrast split segmentation algorithm on eCognition, version 8.0.1 was applied [69]. Over this vegetation mask, forest species were identified using the nearest neighbour classification method, producing a map with the delimitation of trees crowns identified by specie [70].
The area was divided into a square grid of 45.5 m× 45.5 m (2070.25 m2). Grids were classified according to the species present. The crown cover obtained with satellite data (
2.2.3. Forest inventory data and biomass estimation
Forest inventory data set is composed of 17 plots of pure holm oak, 11 plots of pure cork oak and 23 mixed plots of holm oak and cork oak, with a total sampled area of 10.6 ha. In these plots, for all individuals with a breast height diameter ≥6 cm, the diameter at breast height, total height and crown radii in four directions (North, South, East and West) were measured [72]. Each tree geographical location was recorded by GNSS. Tree crown horizontal projection (
2.2.4. Statistical analysis
Statistical analysis included correlation analysis and linear and multiple regression, implemented in R statistical software [73]. As
As the goal was to develop functions able to estimate AGB as function of
As suggested by several authors [76, 77], the models were studied by the sum of squares of the residuals (SQR), the coefficient of determination (R2) and the adjusted coefficient of determination (R2aj). Validation tests entail an independent data set. To overtake the inexistence of an independent data set, Refs. [78–80] suggest using predicted residual error. The sum of its square values, PRESS (Eq. (4)), and the sum of its absolute values, APRESS (Eq. (5)), were used as the validation test. The closer to the null value of residuals, the better is the model. The per cent value of the estimated and calculated AGB defined the error.
2.3. Results and discussion
2.3.1. Multi‐resolution segmentation and object‐oriented classification
Figure 3 presents the vegetation mask resulting from the multi‐resolution segmentation and object‐oriented classification process (yellow line), over the QuickBird image with false colour composite for an area of Mora. The vegetation index NDVI enables a spectral signature sufficiently different to obtain a good distinction of the two forest species (cork and holm oak), with an agreement between the classification and ground truth using Kappa statistic [81, 82], of 78% and a global precision of 89%. Thus, the simultaneous analysis of these two evergreen oak species in pure and mixed stands can be useful for the estimation of AGB and also because, though they are visually similar, the methodology allows a mapping per species (cork and holm oak) with a good classification accuracy.
2.3.2. Individual trees and plot characteristics and above‐ground biomass functions with inventory data
The descriptive statistics: minimum (
All plots ( |
Evergreen oaks mixed plots ( |
Cork oak pure plots ( |
|||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Variables | min | max | mean | SD | CV | min | max | mean | SD | CV | min | max | mean | SD | CV |
19 | 140 | 70 | 28 | 40.5 | 19 | 140 | 61 | 28 | 46.5 | 53 | 135 | 88 | 24 | 27.7 | |
2.6 | 15.4 | 7.1 | 2.7 | 38.1 | 2.6 | 13.1 | 6.2 | 2.1 | 34.3 | 5.2 | 15.4 | 9.8 | 3.0 | 31.2 | |
8.7 | 49.3 | 25.3 | 9.2 | 36.5 | 8.7 | 49.3 | 20.9 | 8.2 | 39.3 | 18.7 | 42.3 | 27.5 | 7.4 | 26.8 | |
13.7 | 70.5 | 35.0 | 14.7 | 41.9 | 14.3 | 58.8 | 28.5 | 11.2 | 39.2 | 23.6 | 70.5 | 44.9 | 13.5 | 30.0 | |
284.2 | 1460.2 | 725.3 | 303.7 | 41.9 | 296.9 | 1216.7 | 589.3 | 230.9 | 39.2 | 489.0 | 1460.2 | 928.7 | 278.7 | 30.0 | |
10.4 | 62.7 | 30.1 | 11.1 | 36.8 | 10.4 | 51.5 | 26.0 | 8.6 | 33.1 | 23.8 | 62.7 | 40.7 | 11.9 | 29.2 |
The strongest positive correlations are found for pure plots of cork oak between
Model | Plots | Equation | SQR | R2 | R2aj | PRESS | APRESS |
---|---|---|---|---|---|---|---|
Inventory data | |||||||
1 | QS | 23576825 | 0.972 | 0.969 | 0.00000019 | 0.00101252 | |
2 | QRQS | 74401387 | 0.899 | 0.894 | 0.00000065 | 0.00313966 | |
3 | All | 187602462 | 0.916 | 0.915 | 0.00000113 | 0.00631091 | |
4 | QS | 16220130 | 0.9995 | 0.999 | 0.00000002 | 0.00034940 | |
5 | QRQS | 292087434 | 0.997 | 0.997 | 0.00000004 | 0.00049897 | |
6 | All | 3066829502 | 0.998 | 0.998 | 0.00000001 | 0.00049770 | |
7 | QS | 40091932 | 0.953 | 0.948 | 0.00000016 | 0.00106716 | |
8 | QRQS | 72931734 | 0.901 | 0.896 | 0.00000061 | 0.00317184 | |
9 | All | 163946315 | 0.927 | 0.925 | 0.00000120 | 0.00656152 | |
10 | QS | 43379144 | 0.999 | 0.998 | 0.00000001 | 0.00025905 | |
11 | QRQS | 104841889 | 0.999 | 0.999 | 0.00000005 | 0.00068011 | |
12 | All | 1722352935 | 0.999 | 0.999 | 0.00000001 | 0.00050963 | |
13 | All | 104975100 | 0.953 | 0.949 | 0.00000151 | 0.00661458 | |
14 | All | 1235713930 | 0.991 | 0.999 | 0.00000007 | 0.00070861 |
The statistical properties and the PRESS and APRESS statistics of the linear models for plot
2.3.3. Above‐ground biomass allometric functions with satellite image data
The statistical properties and the validation statistics of the linear models for
Model 10, for cork oak pure stands, has larger errors up to 4000 m2 and an irregular trend afterwards. For error thresholds of 10 and 5%
The inclusion of independent variables identifying the plot composition originated a generalised improvement in the statistical properties of the models (Table 2) for
All the fitted models (models 7–14) have a high goodness of fit, with more than 90% of the variability explained. It seems that crown horizontal projection as better predictive abilities with linear functions than the shadow fraction processed from QuickBird images [49]. The models developed in this study show also better performances than multiple regression functions with crown diameter and total height as independent variables, derived from a panchromatic band of QuickBird satellite [50] or than exponential functions with reflectance of band 3 or NDVI for IKONOS image [47]. Likewise, biomass estimation functions derived from medium spatial resolution satellite image data do not show as good performance as those obtained in this study. This is probably related to dissimilarity between ground and satellite data [27]. The former was confirmed in a study with Landsat 5 TM and MODIS images for P
2.4. Conclusions
Remote sensing gives a good contribution to estimate the above‐ground biomass. The high spatial resolution satellite image allows mapping with high accuracy the above‐ground biomass at local and regional scale.
The overestimation of crown horizontal projection per plot using high spatial resolution satellite image, when compared with that calculated using inventory data, is related to the inclusion of mixed pixels in the boundary of tree crown delimitation in the multi‐resolution segmentation and trees’ spatial distribution. Though the cork and holm oak are visually similar, their spectral signature is sufficiently different to obtain a very good classification by forest specie.
In general, above‐ground biomass allometric functions for plot and their cumulative values showed a good performance, though better for pure stands than for mixed stands, which might be explained by the larger variability observed in the latter. However, the inclusion of dummy variables, which reflect the differences between the species and the stand structure, originates a generalised improvement in the functions performance. The same threshold errors of 10 and 5% are attained by the latter with less 33 and 70% of the stand areas. Considering that to a 10% error correspond stand areas of 1.1 ha, it includes about 90% of the area occupied by these species in Portugal [65]. This method has the advantage of enabling the overall area evaluation, not requiring forest inventory or extrapolation procedures. It can be used when species composition is not (models 9 and 12) and is (models 13 and 14) differentiated.
Acknowledgments
The authors would like to thank the forest producers for allowing plot installation and to Paulo Mesquita for satellite image processing. The work was financed by Programa Operativo de Cooperação Transfronteiriço Espanha—Portugal (POCTEP); project Altercexa—Medidas de Adaptación y Mitigación del Cambio Climático a Través del Impulso de las Energías Alternativas en Centro, Alentejo y Extremadura (Refa 0317_Altercexa_I_4_E and 0406_ALTERCEXA_II_4_E); TrustEE—innovative market based Trust for Energy Efficiency investments in industry (Project ID: H2020—696140); and National Funds through FCT—Foundation for Science and Technology under the Project UID/AGR/00115/2013.
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