Xiliang Ni

Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA

A methodology to generate spatially continuous fields of tree heights with an optimized Allometric Scaling and Resource Limitations (ASRL) model is reported in this first of a multi-part series of articles. Model optimization is performed with the Geoscience Laser Altimeter System (GLAS) waveform data. This methodology is demonstrated by mapping tree heights over forested lands in the continental USA (CONUS) at 1 km spatial resolution. The study area is divided into 841 eco-climatic zones based on three forest types, annual total precipitation classes (30 mm intervals) and annual average temperature classes (2 °C intervals). Three model parameters (area of single leaf, α, exponent for canopy radius, η, and root absorption efficiency, γ) were selected for optimization, that is, to minimize the difference between actual and potential tree heights in each of the eco-climatic zones over the CONUS. Tree heights predicted by the optimized model were evaluated against GLAS heights using a two-fold cross validation approach (R2 = 0.59; RMSE = 3.31 m). Comparison at the pixel level between GLAS heights (mean = 30.6 m; standard deviation = 10.7) and model predictions (mean = 30.8 m; std. = 8.4) were also performed. Further, the model predictions were compared to existing satellite-based forest height maps. The optimized ASRL model satisfactorily reproduced the pattern of tree heights over the CONUS. Subsequent articles in this series will document further improvements with the ultimate goal of mapping tree heights and forest biomass globally.

Allometric Scaling and Resource Limitations Model of Tree Heights: Part 2. Site Based Testing of the Model

The ultimate goal of this multi-article series is to develop a methodology to generate continuous fields of tree height and biomass. The first paper demonstrated the need for Allometric Scaling and Resource Limitation (ASRL) model optimization and its ability to generate spatially continuous fields of tree heights over the continental USA at coarse (1 km) spatial resolution. The objective of this second paper is to provide an assessment of that approach at site scale, specifically at 12 FLUXNET sites where more accurate data are available. Estimates of tree heights from the Geoscience Laser Altimeter System (GLAS) waveform data are used for model optimization. Amongst the five possible GLAS metrics that are representative of tree heights, the best metric is selected based on how closely the metric resembles field-measured and Laser Vegetation Imaging Sensor tree heights. In the optimization process, three parameters of the ASRL model (area of single leaf, α; exponent for canopy radius, η; and root absorption efficiency, γ) are simultaneously adjusted to minimize the difference between model predictions and observations at the study sites (distances to valid GLAS footprints ≤ 10 km). Performance of the optimized ASRL model was evaluated through comparisons to the best GLAS metric of tree height using a two-fold cross validation approach (R2 = 0.85; RMSE = 1.81 m) and a bootstrapping approach (R2 = 0.66; RMSE = 2.60 m). The optimized model satisfactorily performed at the site scale, thus corroborating results presented in part one of this series. Future investigations will focus on generalizing these results and extending the model formulation using similar allometric concepts for the estimation of woody biomass.

Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data

Spatially-detailed forest height data are useful to monitor local, regional and global carbon cycle. LiDAR remote sensing can measure three-dimensional forest features but generating spatially-contiguous forest height maps at a large scale (e.g., continental and global) is problematic because existing LiDAR instruments are still data-limited and expensive. This paper proposes a new approach based on an artificial neural network (ANN) for modeling of forest canopy heights over the China continent. Our model ingests spaceborne LiDAR metrics and multiple geospatial predictors including climatic variables (temperature and precipitation), forest type, tree cover percent and land surface reflectance. The spaceborne LiDAR instrument used in the study is the Geoscience Laser Altimeter System (GLAS), which can provide within-footprint forest canopy heights. The ANN was trained with pairs between spatially discrete LiDAR metrics and full gridded geo-predictors. This generates valid conjugations to predict heights over the China continent. The ANN modeled heights were evaluated with three different reference data. First, field measured tree heights from three experiment sites were used to validate the ANN model predictions. The observed tree heights at the site-scale agreed well with the modeled forest heights (R = 0.827, and RMSE = 4.15 m). Second, spatially discrete GLAS observations and a continuous map from the interpolation of GLAS-derived tree heights were separately used to evaluate the ANN model. We obtained R of 0.725 and RMSE of 7.86 m and R of 0.759 and RMSE of 8.85 m, respectively. Further, inter-comparisons were also performed with two existing forest height maps. Our model granted a moderate agreement with the existing satellite-based forest height maps (R = 0.738, and RMSE = 7.65 m (R2 = 0.52, and RMSE = 8.99 m). Our results showed that the ANN model developed in this paper is capable of estimating forest heights over the China continent with a satisfactory accuracy. Forth coming research on our model will focus on extending the model to the estimation of woody biomass.

Allometric Scaling and Resource Limitations Model of Tree Heights: Part 3. Model Optimization and Testing over Continental China

The ultimate goal of our multi-article series is to demonstrate the Allometric Scaling and Resource Limitation (ASRL) approach for mapping tree heights and biomass. This third article tests the feasibility of the optimized ASRL model over China at both site (14 meteorological stations) and continental scales. Tree heights from the Geoscience Laser Altimeter System (GLAS) waveform data are used for the model optimizations. Three selected ASRL parameters (area of single leaf, α; exponent for canopy radius, η; and root absorption efficiency, γ) are iteratively adjusted to minimize differences between the references and predicted tree heights. Key climatic variables (e.g., temperature, precipitation, and solar radiation) are needed for the model simulations. We also exploit the independent GLAS and in situ tree heights to examine the model performance. The predicted tree heights at the site scale are evaluated against the GLAS tree heights using a two-fold cross validation (RMSE = 1.72 m; R2 = 0.97) and bootstrapping (RMSE = 4.39 m; R2 = 0.81). The modeled tree heights at the continental scale (1 km spatial resolution) are compared to both GLAS (RMSE = 6.63 m; R2 = 0.63) and in situ (RMSE = 6.70 m; R2 = 0.52) measurements. Further, inter-comparisons against the existing satellite-based forest height maps have resulted in a moderate degree of agreements. Our results show that the optimized ASRL model is capable of satisfactorily retrieving tree heights over continental China at both scales. Subsequent studies will focus on the estimation of woody biomass after alleviating the discussed limitations.

The retrieval of shrub fractional cover based on a geometric-optical model in combination with linear spectral mixture analysis

Vegetation fractional cover, which defines the amount of vegetation on the surface of the land, is a key parameter in land surface models. Based on a geometric-optical model in combination with a linear spectral mixture analysis, the retrieval of shrub fractional cover in Wushen Banner of Inner Mongolia in the Mu Us Sandland using HJ-1B multispectral images is discussed. We acquired the surface reflectance based on geometric correction and atmospheric correction of the HJ-1B image. Then we assumed that the reflectance of a mixed pixel is a simple linear combination of two components, namely illuminated background and illuminated canopy, and further calculated the areal proportion of the illuminated background within each pixel based on the linear spectral mixture analysis. Then, combined with the measured shrub structural parameters, the shrub fractional cover was estimated using an inverted geometric-optical model. Finally, the result was validated through the measured shrub cover of 13 sample plots and a comparison study was done with the NDVI regression method and simple linear spectral mixture analysis. The R 2 of the three methods are 0.898, 0.614, and 0.659, with corresponding root-mean-squared errors of 0.136, 0.154, and 0.175, which indicate the reliability of the combined method.

Application of CCD data of HJ-1 satellite in PM 10 evaluation in Shenzhen, China

The main missions of HJ-1 satellite are to monitor pollution, ecosystem destruction and natural disasters. In recent years, Inhalable particle (PM10) has become the primary pollutant of major cities in China, which seriously affects the living environment of its residents. In this paper, we made use of high-resolution CCD data of HJ-1 satellite to monitor atmospheric inhalable particle (PM10) in Shenzhen. Firstly, we inversed the aerosol optical depth (AOD) of CCD data of HJ-1 satellite. Then we analyzed the correlation between AOD and PM10 using PM10 data in six districts of Shenzhen and the AOD data. The conclusions are as follows. 1) The AOD and PM10 are significantly related in spring-summer. 2) The correlation between AOD and PM10 has been improved greatly after considering the variation of aerosol scale height.

Environmental factor analysis of cholera in China using remote sensing and geographical information systems

Cholera is one of a number of infectious diseases that appears to be influenced by climate, geography and other natural environments. This study analysed the environmental factors of the spatial distribution of cholera in China. It shows that temperature, precipitation, elevation, and distance to the coastline have significant impact on the distribution of cholera. It also reveals the oceanic environmental factors associated with cholera in Zhejiang, which is a coastal province of China, using both remote sensing (RS) and geographical information systems (GIS). The analysis has validated the correlation between indirect satellite measurements of sea surface temperature (SST), sea surface height (SSH) and ocean chlorophyll concentration (OCC) and the local number of cholera cases based on 8-year monthly data from 2001 to 2008. The results show the number of cholera cases has been strongly affected by the variables of SST, SSH and OCC. Utilizing this information, a cholera prediction model has been established based on the oceanic and climatic environmental factors. The model indicates that RS and GIS have great potential for designing an early warning system for cholera.

Estimation of tree heights using remote sensing data and an Allometric Scaling and Resource Limitations (ASRL) model

In this study, we developed the Allometric Scaling and Resource Limitations (ASRL) model by using the best GLAS tree heights to optimize the ASRL. At first, we obtained the best metric of GLAS tree heights by comparing with LVIS tree heights in six sites. Then, the best metric GLAS tree heights were separately used to optimize ASRL model and test the accuracy of prediction heights from optimized ASRL model in sites scale and country scale. Validation result showed that predicted tree heights from optimized ASRL model had high accuracy.

Long-term snow disasters during 1982–2012 in the Tibetan Plateau using satellite data

ABSTRACT Taking the Tibetan Plateau (TP) as a study area, we developed an algorithm to generate long-term four-level snow disaster products (1982–2012) using a new daily snow depth product with a spatial resolution of 0.05° using AVHRR archival reflectance products (AVH09C1-version4) from Land Long-Term Data Record and passive microwave snow depth products. The total classification agreement of our products reached 83.6%, improved from 69.1%. R-square reached 0.62, which showed a good agreement with field data. Based on the products, we obtained annual snow disaster results during 1982–2012. The results indicated that in 1983, 1985, 1997, 1998 and 2008, a large part of the TP suffered extremely severe snow disaster. The annual variation of light and moderate snow disaster areas is much stable than severe and extremely severe areas. After 1999, annual extremely severe areas are more stable and smaller than before. Some areas suffered severe snow disaster in 1985, 1997 and 1998, while in other years they presented a normal status. A large part of the middle-east TP suffered extremely severe snow disaster almost every year. The information within population-filtered counties was extracted to support the development of the husbandry and the urban and rural planning for government.

Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data

ABSTRACT Most terrestrial carbon is stored in forest biomass, which plays an important role in local, regional, and global climate change. Monitoring of forests and their status, and accurate estimation of forest biomass are important in mitigating the impacts of climate change. Empirical models developed using remote-sensing and field-measured forest data are commonly used to estimate forest biomass. In the present study, we used a mechanistic model to estimate height and biomass in the Three Gorges reservoir region (China) based on the allometric scale and resource limits (ASRL) model. The forests in the Three Gorges reservoir region are important and unique in view of the vertical distribution of vegetation and mixed needleleaf. Detailed information about the forest in this region is available from the Geoscience Laser Altimeter System (GLAS) and field measurements from 714 forest plots. The ASRL model parameters were adjusted using GLAS-derived forest tree height to reduce the deviation between modelled and observed forest height. The predicted maximum forest tree height from the optimized ASRL model was compared to measured tree heights, and a good correlation (R2 = 0.566) was found. The allometric scale function between forest height and diameter at breast height (DBH) is developed and the maximum forest tree height from the optimized ASRL model transferred to DBH. Moreover, the forest biomass was estimated from DBH according to the allometric scale function that was determined using DBH and biomass data. The results of maximum forest biomass using the ASRL model and the allometric scale function show a good accuracy (R2 = 0.887) in the Three Gorges reservoir region. Here, we present the forest biomass estimation approach following allometric theory for accurate estimation of maximum forest tree height and biomass. The proposed approach can be applied to forest species in all types of environmental conditions.