Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area

Tian, Xin; Su, Zhongbo; Chen, Erxue; Li, Zengyuan; Tol, Christiaan van der; Guo, Jianping; He, Qisheng

doi:10.1016/j.jag.2011.09.010

Cited by 69 publications

(46 citation statements)

References 89 publications

(89 reference statements)

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“…In particular, Landsat images have been the most widely used for forest aboveground biomass (AGB) estimation in the past three decades [5,6,20,24,26,[28][29][30][31][32][33][34][35][36], mainly because they are freely downloadable, have a long history, and have medium spatial resolution. The studies deal with different climate zones and forest ecosystems, from tropical to subtropical, temperate, and boreal forests [4][5][6][7][12][13][14][15]20,28,32,[37][38][39][40][41][42][43]. However, one common problem is the data saturation in Landsat imagery; that is, spectral reflectance values are not sensitive to the change in biomass of dense and multilayer canopy forests, which results in low accuracy of AGB estimation, especially when AGB is high, such as greater than 130 Mg/ha [5,6,29].…”

Section: Introductionmentioning

confidence: 99%

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

et al. 2016

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Abstract:The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg/ha. Relatively low AGB (e.g., less than 40 Mg/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80-120 Mg/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation.

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Section: Introductionmentioning

confidence: 99%

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

et al. 2016

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“…Most studies rely on stepwise variable selection (Naesset, 2002;Heurich and Thoma, 2008) or sensitivity analyses (Tian et al, 2012). Nevertheless, studies such as these are based on statistical assumptions of regressions that may not be suitable for non-parametric methods that normally contain flexible assumptions, especially for spectral signatures with spatial variability.…”

Section: Discussionmentioning

confidence: 99%

“…Another important advantage of k-NN is that missing values can easily be assigned (Fazakas et al, 1999;Li et al, 2011;McRoberts et al, 2007). The performance of k-NN is impacted by the following factors: (i) feature space variables; (ii) multidimensional distance measures (very common distances include the Euclidean Distance (ED), the Mahalanobis Distance (MD), and the Fuzzy Distance (FD)); (iii) the number of k nearest neighbors; and (iv) the size of the sampling window (Chirici et al, 2008;Tian et al, 2012). To constitute the optimal configuration of k-NN, various feature types and the mathematical setup should be added to the algorithm.…”

Section: K-nearest Neighbormentioning

confidence: 99%

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

Tian

Chen

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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A B S T R A C TIn this work, we present a strategy for obtaining forest above-ground biomass (AGB) dynamics at a fine spatial and temporal resolution. Our strategy rests on the assumption that combining estimates of both AGB and carbon fluxes results in a more accurate accounting for biomass than considering the terms separately, since the cumulative carbon flux should be consistent with AGB increments. Such a strategy was successfully applied to the Qilian Mountains, a cold arid region of northwest China.Based on Landsat Thematic Mapper 5 (TM) data and ASTER GDEM V2 products (GDEM), we first improved the efficiency of existing non-parametric methods for mapping regional forest AGB for 2009 by incorporating the Random Forest (RF) model with the k-Nearest Neighbor (k-NN). Validation using forest measurements from 159 plots and the leave-one-out (LOO) method indicated that the estimates were reasonable (R 2 = 0.70 and RMSE = 24.52 tones ha −1 ). We then obtained one seasonal cycle ( ) using the MODIS MOD_17 GPP (MOD_17) model that was calibrated to Eddy Covariance (EC) flux tower data (2010). After that, we calibrated the ecological process model (BiomeBioGeochemical Cycles (Biome-BGC)) against above GPP estimates (for 2010) for 30 representative forest plots over an ecological gradient in order to simulate AGB changes over time. Biome-BGC outputs of GPP and net ecosystem exchange (NEE) were validated against EC data (R 2 = 0.75 and RMSE = 1. 27 gC m −2 d −1 for GPP, and R 2 = 0.61 and RMSE = 1.17 gC m −2 d −1 for NEE). The calibrated Biome-BGC was then applied to produce a longer time series for net primary productivity (NPP), which, after conversion into AGB increments according to site-calibrated coefficients, were compared to dendrochronological measurements (R 2 = 0.73 and RMSE = 46.65 g m −2 year −1 ). By combining these increments with the AGB map of 2009, we were able to model forest AGB dynamics. In the final step, we conducted a Monte Carlo analysis of uncertainties for interannual forest AGB estimates based on errors in the above forest AGB map, NPP estimates, and the conversion of NPP to an AGB increment.

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“…Multivariate linear regression has been widely applied in geographical studies, including in studying land use change and land expansion (Li et al 2007;Liu et al 2016;Soares-Filho et al 2013;Xian & Homer 2010), air temperature measurement (Mann & Schmidt 2003), soil studies and degradation (Gomez et al 2013;Luo et al 2016;Rayegani et al 2016;Sun et al 2013), air pollution (Fischer et al 2007;Sozanska et al 2002;Vallius et al 2003;Zhou et al 2014) and urban development and urban studies (Akpinar 2016a(Akpinar , 2016bFarrell et al 2015;Onishi et al 2010;Tran 2016). It is also combined with other geographical imagery and analysis methods such as remote sensing (Ahmad et al 2010;García et al 2008;Hou et al 2011;Isenstein & Park 2014;Liu et al 2010;Ma et al 2014;Rayegani et al 2016;Tian et al 2012aTian et al , 2012bYu et al 2015) and geographical information systems (GIS) (Howard et al 2012;Lee et al 2016;Sozanska et al 2002). Thus, it has been broadly trusted by scholars for examining geographical phenomena.…”

Section: Methodsmentioning

confidence: 99%

Will Distance to the Capital City Matter When Supplying New Cities in Egypt?

Ibrahim

Masoumi

2016

GeoScape

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Unlike other developing countries, the housing market in Egypt is characterized by densely populated urban areas in old cities and the peripheral urban agglomeration. In contrast, a high rate of vacancy along most of the new cities that have been established since the 1980s is seen. Regardless of such high rate of vacancies, still the variation in occupancy rates among those new cities is notable. Questions arising include: Does proximity to old cities or Greater Cairo affect the size of the population of the new cities? Is the size of the city or the year of establishment plays roles in attracting more inhabitants? The factors of spatial characteristics of new cities in Egypt remain questionable. This research aims to reveal the association between occupancy rate and six factors related to the spatial characteristics of new cities and their geographical locations, such as; current inhabitants, the estimated size of the target group, the size of new cities, total number of housing units, distance to nearby old city, and distance to Greater Cairo. Key words:Egyptian new cities, spatial analysis, distance to the capital city, population change Highlights for public administration, management and planning:• Occupancy rates of new cities established after 1990 are associated with city size.• Proximity to the nearby old city, or to Greater Cairo has no association with occupancy rate.• Central approach in planning new cities in Greater Cairo agglomeration is questioned.

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Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area

Cited by 69 publications

References 89 publications

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

Modeling of forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the Qilian mountains

Will Distance to the Capital City Matter When Supplying New Cities in Egypt?

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