Abstract
Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (PF_AGB) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate PF_AGB. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of PF_AGB estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the PF_AGB in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The PF_AGB in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated PF_AGB had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, p < 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series PF_AGB using satellite repetitive measures, particularly for arid or semi-arid areas.
Highlights
Forests play a key role in regional ecology, and have a crucial impact on the global carbon cycle [1]
According to the 1994 International Forest Resources Monitoring Program published by Food and Agriculture Organization (FAO), forest biomass is an important component of forest resource monitoring [5]
Forest biomass correlates with the optical spectrum measurements, such as the shortwave infrared (SWIR) band [38] or near-infrared (NIR) band [39], and other various remotely sensed parameters, such as leaf area index (LAI) [40], normalized difference vegetation index (NDVI), enhanced vegetation Index (EVI), perpendicular vegetation Index (PVI), soil adjusted vegetation index (SAVI), and the simple ratio index (SR) [7,41]
Summary
Forests play a key role in regional ecology, and have a crucial impact on the global carbon cycle [1]. Both direct and indirect methods can accurately predict single-tree AGB, especially the allometric equations, which provide highly accurate and efficient estimates of forest AGB for field sites Such methods provide the essential basis for expanding field measurements to larger scales [20]. The objective was to estimate AGB for planted forest in one typical area of the TNSFP To achieve this in an efficient manner, time. The obje3ctoifv1e8 was to estimate AGB for planted forest in one typical area of the TNSFP To achieve this in an efficient msearnienseLr,antidmseatsdeariteaswLaasnudsseadt dtoateastwimaasteussetadndtoaegsetiamt athtee sptiaxnedl sacaglee,aatntdhethpeinxeAl GscBalees,tiamnadtitohnenmAodGeBls ewsteirmeadteiovnelmopoeddelbsawseedreondesvtaenlodpaegdebaansdedenovnisrotannmdeangtealavnadrieanbvleirso. YYuulliinn iiss looccaatteedd iinn SShhaannxxii pprroovviinnccee oonn tthhee bboorrddeerr bbeettwweeeenn tthhee llooeessss ppllaatteeaauu aanndd tthhee IInnnneerr MMoonnggoolliiaann ppllaatteeaauu ((FFiigguurree 11)). LLooccaattiioonnoofftthheessttuuddyyaarreeaaaannddffiieelldd ssiitteess,, iinncclluuddiinngghhaarrvveesstteeddppllaanntteeddffoorreesstt aabboovveeggrroouunndd bbiioommaassss((PPFF__AAGGBB)) ssiitteess,, PPFF__AAGGBB ssuurrvveeyy ssiitteess,, aanndd llaanndd ccoovveerr ttyyppeess ssuurrvveeyy ssiitteess,,oovveerrllaaiiddoonnaaddiiggiittaall eelleevvaattiioonn mmooddeell ((DDEEMM)) ((mm)) aanndd rriivveerrss
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