Environ Monit Assess (2015) 187:308 DOI 10.1007/s10661-015-4551-1
Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques Bechu K. V. Yadav & S. Nandy
Received: 14 August 2014 / Accepted: 21 April 2015 # Springer International Publishing Switzerland 2015
Abstract Mapping forest biomass is fundamental for estimating CO2 emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m×31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10–40, 40–70 and >70 % of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m3 ha−1. The total growing stock of the forest was found to be 2,024,652.88 m3. The AGWB ranged from 143 to 421 Mgha−1. Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After
B. K. V. Yadav Department of Forests, Kathmandu, Nepal S. Nandy (*) Forestry and Ecology Department, Indian Institute of Remote Sensing, ISRO, Dehradun 248001, India e-mail: [email protected]
validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE)=42.25 Mgha−1) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha−1 respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha−1. The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping. Keywords Biomass mapping . Forest inventory . Remote sensing . Direct radiometric relationships . knearest neighbours . Cokriging
Introduction Forest biomass is relevant for studying carbon cycle, climate change studies as well as for forest management. Mapping aboveground forest biomass is of fundamental importance for estimating CO2 emissions caused due to land use/land cover changes (Sales et al. 2007), bioenergy potentials and monitoring carbon stocks (Tuominen et al. 2010) and forest planning and management (Franco-Lopez et al. 2001). A pivotal requirement for the deforestation and forest degradation (REDD) initiative is a cost-effective biomass mapping method that is also operationally feasible at the national level (Baccini et al. 2008; Tuominen et al. 2010). Remotely sensed data have currently become an important data source for biomass estimation (Lu 2006). Remote sensing has given aboveground biomass
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(AGB) estimation a new perspective with large and repetitive coverage and with cost effectiveness. It has been demonstrated in several studies that the satellite spectral information has a good correlation with aboveground forest biomass and when combined with field measurements, is suitable for AGB estimation (Viana et al. 2012; Lu et al. 2012; Manna et al. 2014; Kushwaha et al. 2014). Direct statistical relationship between AGB and a remote sensing variable allows the production of a continuous AGB map for the area (Mitchard et al. 2012). In the present scenario, the geostatistical techniques are useful in providing estimates of sample attributes at locations with sparse information (Burrough and McDonnell 1998). They provide a set of tools and methods for modelling the spatial distribution and variability of forest attributes which have been used in forest inventory for the past decades (Nanos et al. 2004; Sales et al. 2007). Hence, the present study aimed to map and evaluate the aboveground woody biomass (AGWB) using forest inventory, remote sensing and three geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK). Multiple regression analysis may be the most frequently used approach for developing biomass estimation models (Lu 2005). The regression models assume that the biomass variable is linearly correlated with spectral responses and that limited correlations exist between independent variables (Lu et al. 2004). DRR technique establishes regression relationships between the satellite spectral information which include the individual spectral bands, band ratios, vegetation indices and other possible transformations as independent variables, and the measured biomass at each corresponding inventory sample plot position as dependent variable (Viana et al. 2012). Nearest neighbours technique is a non-parametric discriminant technique for classification into populations whose distributions are unknown (McRoberts 2012). k-NN is one of the most successful methods for mapping forest attributes using remotely sensed and other ancillary data (Chirici et al. 2007). Using data sampled in the field, the technique produces locally calibrated estimates for individual satellite image pixels and continuous maps of forest attributes (Tomppo 1991; McRoberts et al. 2007). Due to its merits, the k-NN technique was widely used for estimation of volume and AGB (Franco-Lopez et al. 2001; Holmström and Fransson 2003; Lu 2006; Labrecque et al. 2006; Chirici et al. 2008). CoK is a
process wherein several variables can be jointly estimated on the basis of intervariable and spatial structure information (Carr et al. 1985). In this method, both variables are spatially correlated and have a crossvariogram that is used to quantify cross-spatial autocovariance between the primary and secondary variable which in turn is used in the interpolation procedure (Webster and Oliver 2001). CoK works best where the primary variable of interest is less densely sampled than the others (Eldeiry and Garcia 2009). Various studies have used CoK for biomass estimation (Sales et al. 2007; Dwyer 2011).
Materials and methods Study area The study area lies between 77° 38 to 77° 47 E longitude and 30° 20 to 30° 26 N latitude covering an area of 58.75 km2 (Fig. 1). It forms a part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India. The forests of the study area have been classified as Bhabar-dun sal forest (3C/C2b(i)) by Champion and Seth (1968). Shorea robusta is the dominant top storey species of the forest and often grows as a pure stand. The main associated tree species are Mallotus philippensis, Terminalia tomentosa, Terminalia bellirica and Lagerstroemia parviflora. The dominant shrubs are Lantana camara, Clerodendrum infortunatum and Murraya koenigii. The temperature of the study area varies from 2 to 40 °C. The area receives an average annual rainfall of 2073.3 mm. The rainy season mainly extends from June to September. Humidity varies from about 40 to 80 % throughout the year (Anon., 2010). Methods IRS P6 LISS-III satellite data of December 1, 2012 was used for this study. Radiometric correction of remotely sensed data was performed to remove the topographic and atmospheric effects. The radiance was converted to reflectance using Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model of ENVI software. Image to image registration was carried out using orthorectified image of Landsat TM of the study area as a master image. Well-distributed ground control points (GCPs) were chosen throughout the imagery and re-sampling of the image was made using the first order
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Fig. 1 Location of study area in India
polynomials and nearest neighbourhood algorithm in ERDAS IMAGINE software. Image registration was conducted with UTM 43 N projection and WGS 84 datum. Finally, the subset of the study area was extracted from the LISS-III image. The false colour composite (FCC) of the study area (Fig. 2a) was taken to the field to relate the image characteristics with the vegetation types and the canopy density; this information was used later for stratification of different vegetation types/land uses and canopy density categories through on-screen visual image interpretation of the image on 1:50,000 scale. The reflectance values of each band and the Vegetation Index (VI) (Pearson and Miller 1972) and the Normalized Difference Vegetation Index (NDVI) (Rouse et al. 1974) images generated from the satellite imagery, were used as independent variables for biomass estimation. Stratified random sampling was applied to lay out sample plots for field data collection. Square plot of
sample size 0.1 ha (31.62 m×31.62 m) was laid down in the field randomly dividing the whole study area into three different strata based on canopy densities, i.e. 10–40, 40–70 and >70 %. To represent the whole population, samples were selected from each strata based on probability proportional sampling (PPS). A pilot survey was conducted in the study site to calculate the number of sampling units using the following formula (Chacko 1965): N¼
t 2 CV2 ðSE%Þ2
where N is the number of sample plots, t is the statistical value at 95 % significance level, CV is the coefficient of variation and SE% is the standard error percentage. It was found that a total of 36 plots were required to be laid in different strata. Out of which, 70 % (26) of the
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Fig. 2 a FCC of study area. b Forest-type density map
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Table 1 Growing stock and woody biomass in different forest-type density classes Forest-type density
Average volume (m3 ha−1)
Growing stock (m3)
Average biomass (Mgha−1)
Total biomass (Mg)
S. robusta (10–40 %)
343.17
309,668.03
243.36
219,601.98
S. robusta (40–70 %)
356.37
633,269.49
254.51
452,264.27
S. robusta (>70 %)
381.65
1,081,715.37
271.43
Total
2,024,652.89
2002). To convert biomass into carbon, 0.47 was multiplied as recommended by IPCC (2006). For DRR, the reflectance values of each band and the vegetation indices values (independent variables) corresponding to the sample plot locations were regressed with the respective AGWB values (dependent variable) of the sample plots. A biomass map was generated using the best relationship between the individual independent variables and the dependent variable. For k-NN technique, the same independent and dependent variables were used to generate biomass
plots were randomly selected as training data for the analyses and the remaining 30 % plots (10) were used for validation. At each sample plot, species composition, diameter at breast height (DBH) of all trees (≥10 cm), height and crown cover were noted down. The general characteristics of the plot like, location, slope, aspect and evidence of disturbances were also recorded. Volume for each tree was calculated using volumetric equations developed by the Forest Survey of India (FSI 1996). Biomass for each tree was calculated using volume multiplied by specific gravity (FRI
350
Biomass (Mg/ha)
y = 3.4589x + 95.565 R² = 0.0091
300 250 200 150
c
b Biomass (Mg/ha)
400
42
44 46 48 Reflectance (Green Band)
d
400
y = 4.535x - 227.66 R² = 0.1508
350 300 250 200 150
95
100 105 110 115 Reflectance (NIR Band)
120
400 y = 8.7972x + 38.023 R² = 0.0387 350 300 250 200 150
50
Biomass (Mg/ha)
Biomass (Mg/ha)
a
769,317.44 1,441,183.69
22
400
24 26 Reflectance (Red Band)
28
y = 20.511x - 1307.7 R² = 0.374
350 300 250 200 150
73
75 77 79 Reflectance (SWIR Band)
81
Fig. 3 Correlation between a Reflectance (green band) and biomass. b Reflectance (red band) and biomass. c Reflectance (NIR band) and biomass. d Reflectance (SWIR band) and biomass
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Fig. 4 Correlation between a VI and biomass and b NDVI and biomass
a
400
y = 75.598x - 47.077 R² = 0.1499
Biomass (Mg/ha)
350
300
250
200
150 3.6
b
400
3.8
4
4.2 VI
4.4
4.6
4.8
y = 1026.9x - 360.43 R² = 0.1543
Biomass (Mg/ha)
350
300
250
200
150 0.56
maps using K-NN FOREST software. K-NN FOREST implements the k-NN technique to generate spatially explicit estimations (maps) of a dependent variable acquired in the field by sampling through the use of remotely sensed data and other ancillary variables (Chirici et al. 2012). The classification criterion of the k-NN is the minimum spectral distance computed using three different metrics viz. Euclidean, Mahalanobis and Fuzzy (Franco-Lopez et al. 2001; Labrecque et al. 2006). For CoK, exponential, Gaussian and circular semivariograms were checked for best fit. Cross-variogram between dependent and independent variables was calculated for linear model of coregionalization. For fitting a regionalization model, all direct semi-variograms and cross-semivariograms were estimated for the same number of lags and the same lag distances. Then, the number and types of
0.57
0.58
0.59
0.60
0.61 NDVI
0.62
0.63
0.64
0.65
0.66
elementary models and their ranges were postulated. The sills (co-regionalization matrix) were fitted by trial and error. Finally, the spatial distribution of biomass was predicted using CoK in ArcGIS Software. The biomass maps produced by the described methods were evaluated and compared based on the root mean square error (RMSE) (Fazakas et al. 1999). vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n ⌢ 2 uX e i −ei RMSE ¼ t n i¼1
where, i is the estimated biomass, ei is the biomass value measured on the validation plots and n is the number of validation plots.
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Fig. 5 Biomass (Mgha−1) based on the relationship between NDVI and biomass
Results and discussion Three forest-type density classes, viz. S. robusta (10– 40 %), S. robusta (40–70 %) and S. robusta (>70 %), and four non-forest classes viz. scrub, agriculture, stream and settlement were delineated from the IRS P6 LISS-III imagery (Fig. 2b). The forest is mainly dominated by S. robusta. At some places, it is mixed with Mallotus philippensis which grows mainly as understorey to S. robusta. In the open areas, Lantana camara was growing profusely. It was found that 93.25 % of the area was covered with forests. S. robusta (10–40 %), S. robusta (40–70 %) and S. robusta (>70 %) covered 14.15, 32.73 and 46.37 % of the study area, respectively. The volume in different forest-type density strata ranged from 189.84 to 484.36 m3 ha−1. The average volume were 343.17, 356.37 and 381.65 m3 ha−1 in S. robusta (10–40 %), S. robusta (40–70 %) and
S. robusta (>70 %), respectively (Table 1). The AGWB ranged from 143 to 421 Mgha−1 indicating high biomass in the study area. The average biomass was found to be 243.36, 254.51 and 271.43 Mgha−1 for S. robusta (10– 40, 40–70 and >70 %), respectively (Table 1). Correlation was established between biomass and reflectance values of individual bands of LISS-III (Fig. 3). There were no significant relationships found between band reflectance and biomass. These might be due to high biomass contained in the forest and so, spectral bands might have saturated. The relationships of biomass with VI and NDVI (Fig. 4) also showed no significant correlation. They showed positive correlation, but it was very low. Anaya et al. (2009) reported that optical remote sensing-based biomass estimation performs better at low biomass levels. The same has also been reported by Kushwaha et al. (2014) and Manna et al. (2014). After observing relationships of AGB with individual bands and the vegetation indices in the present study, NDVI
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Fig. 6 Biomass (Mgha−1) derived using k-NN with a Euclidean distance, b Mahalanobis distance and c Fuzzy distance
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Fig. 7 Best fit semivariogram model
was found to have some relation with biomass, though it was very low, may be because of high biomass content.
Fig. 8 Biomass (Mgha−1) derived using CoK
Using this relation, a biomass map was generated (Fig. 5) and RMSE of DRR was 67.17 Mgha−1.
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In the present study, biomass maps using k-NN method with Euclidean distance, Mahalanobis distance and fuzzy distance were generated (Fig. 6). Individual bands of LISS-III and vegetation indices were given as inputs (independent variables) and point-biomass as dependent variable to the K-NN FOREST software to generate biomass maps. After generating the biomass maps using k-NN, RMSEs were calculated and compared. The RMSE of k-NN with Mahalanobis distance was found to be 42.25 Mgha−1 followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha−1, respectively. As semivariogram and cross-variogram are the basis of CoK, cross-variogram for the present study was plotted and observed (Fig. 7). To find the best fit for the cross-variogram, different models were observed. Exponential model was found to be the best fit model for the cross-variogram. The sill was observed at 4721.43 at the range of 1579.49 m which meant cross-variogram was stable after several iterations for the given range and correlation was also found to be stable. Nugget was at zero which meant there was complete spatial structure. Semivariogram showed that structure was an isotropy which showed variance was same in all directions. After the best fit semivariogram, crossvalidation was conducted. Mean error and RMSE were found to be 0.64 and 52.2 Mgha−1, respectively. Lastly the biomass map was generated using CoK (Fig. 8). k-NN could map wall to wall biomass for the inventory area with high accuracy. k-NN can estimate several response variables, both continuous and categorical simultaneously. It is a versatile technique with potential for combining different sources of information, not only from outside of a region of interest, but even from different forest inventory designs (Franco-Lopez et al. 2001). DRR is the relationship of dependent variable with independent variables and correlation coefficients of determination (R2) is checked. Spectral bands and vegetation indices used for DRR might get saturated at high forest biomass and hence produced least accurate biomass map. Theoretically, CoK needs more training data representing the whole area of interest. In the present study, there was probably insufficient training data for CoK and therefore the map was less accurate.
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geostatistical techniques, i.e. DRR, k-NN and CoK. k-NN method with Mahalanobis distance was found to be the best technique for biomass mapping in this study. DRR approach uses spectral band values, band ratios, vegetation indices and textures from satellite imagery as independent variables and the sample plot biomass measure as the dependent variable. The regression models assume that the biomass variable is linearly correlated with spectral responses and that limited correlations exist between independent variables (Lu et al. 2012). But, this hypothesis is not usually applicable as spectral variables are often highly correlated to each other (Lu 2005) and selected variables may have a nonlinear relationship with forest biomass (Li et al. 2010). An alternative to these limitations is to use nonparametric approaches such as kNN. In the k-NN approach, it is assumed that the spectral responses are only dependent on the state of forests. The estimate of each location was computed as a weighted mean of k spectrally nearest neighbours by inverse distance weighting (Lu et al. 2012). Spatial distribution of forest biomass is important for planning and management of forest, carbon accounting, global change monitoring and forest productivity modelling. Hence, there is a need for reliable method of mapping and monitoring of forest biomass. As forests play an important role in the carbon cycle, variation in biomass quantity can be a good indicator of climatic change. The capability to map forest biomass is thus important for monitoring changes in forest structure and carbon stock. Acknowledgments The authors sincerely thank the Director, Indian Institute of Remote Sensing, Dehradun and the Director, Centre for Space Science and Technology Education in Asia and the Pacific (CSSTEAP), Dehradun, for the encouragement and support for this study. The first author wish to acknowledge Ministry of Forests and Soil Conservation, Nepal for nomination and CSSTEAP, Dehradun, India, to carry out this study as a partial fulfilment of Post Graduate course. Grateful thanks are due to the forest officers and staff of Timli Forest Range, Kalsi Soil and Water Conservation Division, Uttarakhand, India, for field support.
References Conclusions The present study demonstrated the utility of IRS P6 LISS-III data for biomass estimation and compared
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Page 11 of 12 308 Inventories Programme, Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds) Published: IGES, Japan. Kushwaha, S. P. S., Nandy, S., & Gupta, M. (2014). Growing stock and woody biomass assessment in Asola-Bhatti Wildlife Sanctuary, Delhi, India. Environmental Monitoring and Assessment, 186(9), 5911–5920. Labrecque, S., Fournier, R. A., Luther, J. E., & Piercey, D. (2006). A comparison of four methods to map biomass from LandsatTM and inventory data in western Newfoundland. Forest Ecology and Management, 226, 129–144. Lu, D. (2005). Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. International Journal of Remote Sensing, 26(12), 2509–2525. Lu, D. (2006). The potential and challenge of remote sensingbased biomass estimation. International Journal of Remote Sensing, 27(7), 1297–1326. Lu, D., Mausel, P., Brond’ızio, E., & Moran, E. (2004). Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198(1–3), 149–167. Lu, D., Chen, Q., Wang, G., Moran, E., Batistella, M., Zhang, M., Laurin, G. V., & Saah, D. (2012). Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. International Journal of Forest Research, 2012, 1–16. doi:10.1155/2012/436537. Manna, S., Nandy, S., Chanda, A., Akhand, A., Hazra, S., & Dadhwal, V. K. (2014). Estimating aboveground biomass in Avicennia marina plantation in Indian Sundarbans using high-resolution satellite data. Journal of Applied Remote Sensing, 8, 1–13. McRoberts, R. E. (2012). Estimating forest attribute parameters for small areas using nearest neighbors techniques. Forest Ecology and Management, 272, 3–12. McRoberts, R. E., Tomppo, E. O., Finley, A. O., & Heikkinen, J. (2007). Estimating areal means and variances of forest attributes using the k-nearest neighbors technique and satellite imagery. Remote Sensing of Environment, 111, 466– 480. Mitchard, E. T. A., Saatchi, S. S., White, L. J. T., Abernethy, K. A., Jeffery, K. J., Lewis, S. L., Collins, M., Lefsky, M. A., Leal, M. E., Woodhouse, I. H., & Meir, P. (2012). Mapping tropical forest biomass with radar and space borne LiDAR in Lop’e National Park, Gabon: overcoming problems of high biomass and persistent cloud. Biogeosciences, 9, 179–191. Nanos, N., Calama, R., Montero, G., & Gil, L. (2004). Geostatistical prediction of height/diameter models. Forest Ecology and Management, 195(1–2), 221–235. Pearson, R. L., & Miller, L. D. (1972). Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie, Pawnee National Grasslands, Colorado. Proceedings of the 8th International Symposium on Remote Sensing of the Environment, 2, 1355–1379. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS, Third Earth Resources Technology Symposium, NASA SP-351, NASA, Washington, DC, 1, 309–317. Sales, M. H., Souza, C. M., Jr., Kyriakidis, P. C., Roberts, D. A., & Vidal, E. (2007). Improving spatial distribution estimation of forest biomass with geostatistics: a case study for rondˆ Onia, Brazil. Ecological Modeling, 205, 221–230.
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Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques Bechu K. V. Yadav & S. Nandy
Received: 14 August 2014 / Accepted: 21 April 2015 # Springer International Publishing Switzerland 2015
Abstract Mapping forest biomass is fundamental for estimating CO2 emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m×31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10–40, 40–70 and >70 % of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m3 ha−1. The total growing stock of the forest was found to be 2,024,652.88 m3. The AGWB ranged from 143 to 421 Mgha−1. Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After
B. K. V. Yadav Department of Forests, Kathmandu, Nepal S. Nandy (*) Forestry and Ecology Department, Indian Institute of Remote Sensing, ISRO, Dehradun 248001, India e-mail: [email protected]
validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE)=42.25 Mgha−1) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha−1 respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha−1. The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping. Keywords Biomass mapping . Forest inventory . Remote sensing . Direct radiometric relationships . knearest neighbours . Cokriging
Introduction Forest biomass is relevant for studying carbon cycle, climate change studies as well as for forest management. Mapping aboveground forest biomass is of fundamental importance for estimating CO2 emissions caused due to land use/land cover changes (Sales et al. 2007), bioenergy potentials and monitoring carbon stocks (Tuominen et al. 2010) and forest planning and management (Franco-Lopez et al. 2001). A pivotal requirement for the deforestation and forest degradation (REDD) initiative is a cost-effective biomass mapping method that is also operationally feasible at the national level (Baccini et al. 2008; Tuominen et al. 2010). Remotely sensed data have currently become an important data source for biomass estimation (Lu 2006). Remote sensing has given aboveground biomass
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(AGB) estimation a new perspective with large and repetitive coverage and with cost effectiveness. It has been demonstrated in several studies that the satellite spectral information has a good correlation with aboveground forest biomass and when combined with field measurements, is suitable for AGB estimation (Viana et al. 2012; Lu et al. 2012; Manna et al. 2014; Kushwaha et al. 2014). Direct statistical relationship between AGB and a remote sensing variable allows the production of a continuous AGB map for the area (Mitchard et al. 2012). In the present scenario, the geostatistical techniques are useful in providing estimates of sample attributes at locations with sparse information (Burrough and McDonnell 1998). They provide a set of tools and methods for modelling the spatial distribution and variability of forest attributes which have been used in forest inventory for the past decades (Nanos et al. 2004; Sales et al. 2007). Hence, the present study aimed to map and evaluate the aboveground woody biomass (AGWB) using forest inventory, remote sensing and three geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK). Multiple regression analysis may be the most frequently used approach for developing biomass estimation models (Lu 2005). The regression models assume that the biomass variable is linearly correlated with spectral responses and that limited correlations exist between independent variables (Lu et al. 2004). DRR technique establishes regression relationships between the satellite spectral information which include the individual spectral bands, band ratios, vegetation indices and other possible transformations as independent variables, and the measured biomass at each corresponding inventory sample plot position as dependent variable (Viana et al. 2012). Nearest neighbours technique is a non-parametric discriminant technique for classification into populations whose distributions are unknown (McRoberts 2012). k-NN is one of the most successful methods for mapping forest attributes using remotely sensed and other ancillary data (Chirici et al. 2007). Using data sampled in the field, the technique produces locally calibrated estimates for individual satellite image pixels and continuous maps of forest attributes (Tomppo 1991; McRoberts et al. 2007). Due to its merits, the k-NN technique was widely used for estimation of volume and AGB (Franco-Lopez et al. 2001; Holmström and Fransson 2003; Lu 2006; Labrecque et al. 2006; Chirici et al. 2008). CoK is a
process wherein several variables can be jointly estimated on the basis of intervariable and spatial structure information (Carr et al. 1985). In this method, both variables are spatially correlated and have a crossvariogram that is used to quantify cross-spatial autocovariance between the primary and secondary variable which in turn is used in the interpolation procedure (Webster and Oliver 2001). CoK works best where the primary variable of interest is less densely sampled than the others (Eldeiry and Garcia 2009). Various studies have used CoK for biomass estimation (Sales et al. 2007; Dwyer 2011).
Materials and methods Study area The study area lies between 77° 38 to 77° 47 E longitude and 30° 20 to 30° 26 N latitude covering an area of 58.75 km2 (Fig. 1). It forms a part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India. The forests of the study area have been classified as Bhabar-dun sal forest (3C/C2b(i)) by Champion and Seth (1968). Shorea robusta is the dominant top storey species of the forest and often grows as a pure stand. The main associated tree species are Mallotus philippensis, Terminalia tomentosa, Terminalia bellirica and Lagerstroemia parviflora. The dominant shrubs are Lantana camara, Clerodendrum infortunatum and Murraya koenigii. The temperature of the study area varies from 2 to 40 °C. The area receives an average annual rainfall of 2073.3 mm. The rainy season mainly extends from June to September. Humidity varies from about 40 to 80 % throughout the year (Anon., 2010). Methods IRS P6 LISS-III satellite data of December 1, 2012 was used for this study. Radiometric correction of remotely sensed data was performed to remove the topographic and atmospheric effects. The radiance was converted to reflectance using Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model of ENVI software. Image to image registration was carried out using orthorectified image of Landsat TM of the study area as a master image. Well-distributed ground control points (GCPs) were chosen throughout the imagery and re-sampling of the image was made using the first order
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Fig. 1 Location of study area in India
polynomials and nearest neighbourhood algorithm in ERDAS IMAGINE software. Image registration was conducted with UTM 43 N projection and WGS 84 datum. Finally, the subset of the study area was extracted from the LISS-III image. The false colour composite (FCC) of the study area (Fig. 2a) was taken to the field to relate the image characteristics with the vegetation types and the canopy density; this information was used later for stratification of different vegetation types/land uses and canopy density categories through on-screen visual image interpretation of the image on 1:50,000 scale. The reflectance values of each band and the Vegetation Index (VI) (Pearson and Miller 1972) and the Normalized Difference Vegetation Index (NDVI) (Rouse et al. 1974) images generated from the satellite imagery, were used as independent variables for biomass estimation. Stratified random sampling was applied to lay out sample plots for field data collection. Square plot of
sample size 0.1 ha (31.62 m×31.62 m) was laid down in the field randomly dividing the whole study area into three different strata based on canopy densities, i.e. 10–40, 40–70 and >70 %. To represent the whole population, samples were selected from each strata based on probability proportional sampling (PPS). A pilot survey was conducted in the study site to calculate the number of sampling units using the following formula (Chacko 1965): N¼
t 2 CV2 ðSE%Þ2
where N is the number of sample plots, t is the statistical value at 95 % significance level, CV is the coefficient of variation and SE% is the standard error percentage. It was found that a total of 36 plots were required to be laid in different strata. Out of which, 70 % (26) of the
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Fig. 2 a FCC of study area. b Forest-type density map
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Table 1 Growing stock and woody biomass in different forest-type density classes Forest-type density
Average volume (m3 ha−1)
Growing stock (m3)
Average biomass (Mgha−1)
Total biomass (Mg)
S. robusta (10–40 %)
343.17
309,668.03
243.36
219,601.98
S. robusta (40–70 %)
356.37
633,269.49
254.51
452,264.27
S. robusta (>70 %)
381.65
1,081,715.37
271.43
Total
2,024,652.89
2002). To convert biomass into carbon, 0.47 was multiplied as recommended by IPCC (2006). For DRR, the reflectance values of each band and the vegetation indices values (independent variables) corresponding to the sample plot locations were regressed with the respective AGWB values (dependent variable) of the sample plots. A biomass map was generated using the best relationship between the individual independent variables and the dependent variable. For k-NN technique, the same independent and dependent variables were used to generate biomass
plots were randomly selected as training data for the analyses and the remaining 30 % plots (10) were used for validation. At each sample plot, species composition, diameter at breast height (DBH) of all trees (≥10 cm), height and crown cover were noted down. The general characteristics of the plot like, location, slope, aspect and evidence of disturbances were also recorded. Volume for each tree was calculated using volumetric equations developed by the Forest Survey of India (FSI 1996). Biomass for each tree was calculated using volume multiplied by specific gravity (FRI
350
Biomass (Mg/ha)
y = 3.4589x + 95.565 R² = 0.0091
300 250 200 150
c
b Biomass (Mg/ha)
400
42
44 46 48 Reflectance (Green Band)
d
400
y = 4.535x - 227.66 R² = 0.1508
350 300 250 200 150
95
100 105 110 115 Reflectance (NIR Band)
120
400 y = 8.7972x + 38.023 R² = 0.0387 350 300 250 200 150
50
Biomass (Mg/ha)
Biomass (Mg/ha)
a
769,317.44 1,441,183.69
22
400
24 26 Reflectance (Red Band)
28
y = 20.511x - 1307.7 R² = 0.374
350 300 250 200 150
73
75 77 79 Reflectance (SWIR Band)
81
Fig. 3 Correlation between a Reflectance (green band) and biomass. b Reflectance (red band) and biomass. c Reflectance (NIR band) and biomass. d Reflectance (SWIR band) and biomass
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Fig. 4 Correlation between a VI and biomass and b NDVI and biomass
a
400
y = 75.598x - 47.077 R² = 0.1499
Biomass (Mg/ha)
350
300
250
200
150 3.6
b
400
3.8
4
4.2 VI
4.4
4.6
4.8
y = 1026.9x - 360.43 R² = 0.1543
Biomass (Mg/ha)
350
300
250
200
150 0.56
maps using K-NN FOREST software. K-NN FOREST implements the k-NN technique to generate spatially explicit estimations (maps) of a dependent variable acquired in the field by sampling through the use of remotely sensed data and other ancillary variables (Chirici et al. 2012). The classification criterion of the k-NN is the minimum spectral distance computed using three different metrics viz. Euclidean, Mahalanobis and Fuzzy (Franco-Lopez et al. 2001; Labrecque et al. 2006). For CoK, exponential, Gaussian and circular semivariograms were checked for best fit. Cross-variogram between dependent and independent variables was calculated for linear model of coregionalization. For fitting a regionalization model, all direct semi-variograms and cross-semivariograms were estimated for the same number of lags and the same lag distances. Then, the number and types of
0.57
0.58
0.59
0.60
0.61 NDVI
0.62
0.63
0.64
0.65
0.66
elementary models and their ranges were postulated. The sills (co-regionalization matrix) were fitted by trial and error. Finally, the spatial distribution of biomass was predicted using CoK in ArcGIS Software. The biomass maps produced by the described methods were evaluated and compared based on the root mean square error (RMSE) (Fazakas et al. 1999). vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n ⌢ 2 uX e i −ei RMSE ¼ t n i¼1
where, i is the estimated biomass, ei is the biomass value measured on the validation plots and n is the number of validation plots.
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Fig. 5 Biomass (Mgha−1) based on the relationship between NDVI and biomass
Results and discussion Three forest-type density classes, viz. S. robusta (10– 40 %), S. robusta (40–70 %) and S. robusta (>70 %), and four non-forest classes viz. scrub, agriculture, stream and settlement were delineated from the IRS P6 LISS-III imagery (Fig. 2b). The forest is mainly dominated by S. robusta. At some places, it is mixed with Mallotus philippensis which grows mainly as understorey to S. robusta. In the open areas, Lantana camara was growing profusely. It was found that 93.25 % of the area was covered with forests. S. robusta (10–40 %), S. robusta (40–70 %) and S. robusta (>70 %) covered 14.15, 32.73 and 46.37 % of the study area, respectively. The volume in different forest-type density strata ranged from 189.84 to 484.36 m3 ha−1. The average volume were 343.17, 356.37 and 381.65 m3 ha−1 in S. robusta (10–40 %), S. robusta (40–70 %) and
S. robusta (>70 %), respectively (Table 1). The AGWB ranged from 143 to 421 Mgha−1 indicating high biomass in the study area. The average biomass was found to be 243.36, 254.51 and 271.43 Mgha−1 for S. robusta (10– 40, 40–70 and >70 %), respectively (Table 1). Correlation was established between biomass and reflectance values of individual bands of LISS-III (Fig. 3). There were no significant relationships found between band reflectance and biomass. These might be due to high biomass contained in the forest and so, spectral bands might have saturated. The relationships of biomass with VI and NDVI (Fig. 4) also showed no significant correlation. They showed positive correlation, but it was very low. Anaya et al. (2009) reported that optical remote sensing-based biomass estimation performs better at low biomass levels. The same has also been reported by Kushwaha et al. (2014) and Manna et al. (2014). After observing relationships of AGB with individual bands and the vegetation indices in the present study, NDVI
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Fig. 6 Biomass (Mgha−1) derived using k-NN with a Euclidean distance, b Mahalanobis distance and c Fuzzy distance
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Fig. 7 Best fit semivariogram model
was found to have some relation with biomass, though it was very low, may be because of high biomass content.
Fig. 8 Biomass (Mgha−1) derived using CoK
Using this relation, a biomass map was generated (Fig. 5) and RMSE of DRR was 67.17 Mgha−1.
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In the present study, biomass maps using k-NN method with Euclidean distance, Mahalanobis distance and fuzzy distance were generated (Fig. 6). Individual bands of LISS-III and vegetation indices were given as inputs (independent variables) and point-biomass as dependent variable to the K-NN FOREST software to generate biomass maps. After generating the biomass maps using k-NN, RMSEs were calculated and compared. The RMSE of k-NN with Mahalanobis distance was found to be 42.25 Mgha−1 followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha−1, respectively. As semivariogram and cross-variogram are the basis of CoK, cross-variogram for the present study was plotted and observed (Fig. 7). To find the best fit for the cross-variogram, different models were observed. Exponential model was found to be the best fit model for the cross-variogram. The sill was observed at 4721.43 at the range of 1579.49 m which meant cross-variogram was stable after several iterations for the given range and correlation was also found to be stable. Nugget was at zero which meant there was complete spatial structure. Semivariogram showed that structure was an isotropy which showed variance was same in all directions. After the best fit semivariogram, crossvalidation was conducted. Mean error and RMSE were found to be 0.64 and 52.2 Mgha−1, respectively. Lastly the biomass map was generated using CoK (Fig. 8). k-NN could map wall to wall biomass for the inventory area with high accuracy. k-NN can estimate several response variables, both continuous and categorical simultaneously. It is a versatile technique with potential for combining different sources of information, not only from outside of a region of interest, but even from different forest inventory designs (Franco-Lopez et al. 2001). DRR is the relationship of dependent variable with independent variables and correlation coefficients of determination (R2) is checked. Spectral bands and vegetation indices used for DRR might get saturated at high forest biomass and hence produced least accurate biomass map. Theoretically, CoK needs more training data representing the whole area of interest. In the present study, there was probably insufficient training data for CoK and therefore the map was less accurate.
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geostatistical techniques, i.e. DRR, k-NN and CoK. k-NN method with Mahalanobis distance was found to be the best technique for biomass mapping in this study. DRR approach uses spectral band values, band ratios, vegetation indices and textures from satellite imagery as independent variables and the sample plot biomass measure as the dependent variable. The regression models assume that the biomass variable is linearly correlated with spectral responses and that limited correlations exist between independent variables (Lu et al. 2012). But, this hypothesis is not usually applicable as spectral variables are often highly correlated to each other (Lu 2005) and selected variables may have a nonlinear relationship with forest biomass (Li et al. 2010). An alternative to these limitations is to use nonparametric approaches such as kNN. In the k-NN approach, it is assumed that the spectral responses are only dependent on the state of forests. The estimate of each location was computed as a weighted mean of k spectrally nearest neighbours by inverse distance weighting (Lu et al. 2012). Spatial distribution of forest biomass is important for planning and management of forest, carbon accounting, global change monitoring and forest productivity modelling. Hence, there is a need for reliable method of mapping and monitoring of forest biomass. As forests play an important role in the carbon cycle, variation in biomass quantity can be a good indicator of climatic change. The capability to map forest biomass is thus important for monitoring changes in forest structure and carbon stock. Acknowledgments The authors sincerely thank the Director, Indian Institute of Remote Sensing, Dehradun and the Director, Centre for Space Science and Technology Education in Asia and the Pacific (CSSTEAP), Dehradun, for the encouragement and support for this study. The first author wish to acknowledge Ministry of Forests and Soil Conservation, Nepal for nomination and CSSTEAP, Dehradun, India, to carry out this study as a partial fulfilment of Post Graduate course. Grateful thanks are due to the forest officers and staff of Timli Forest Range, Kalsi Soil and Water Conservation Division, Uttarakhand, India, for field support.
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