Yoshio Awaya

Estimation of the global net primary productivity using NOAA images and meteorological data: changes between 1988 and 1993

A model for net primary productivity (NPP) estimation was developed based on a relationship between NPP estimated by the Chikugo model and the intensity-sum of the normalized difference vegetation index (NDVI) multiplied by the solar radiation during growth periods. There was a clear linear relationship between the estimated NPP and the intensity-sum (R 2=0.845), whose slope indicated the average light use efficiency (LUE) of global plants. The NPP estimation model (NDVI-based model), which included growth multipliers of optimum air temperature and soil water stress on vegetation growth with LUE, was developed. NDVI anomalies caused by scattering of volcanic ash from Mt Pinatubo were reduced by a correction based on intensity matching of channels 1 and 2 individually. NDVI retrieved a seasonal change pattern in 1991 and 1992 after the correction. Global NPP between 1988 and 1993 was estimated using the NDVI-based model, corrected NDVI, air temperature and soil water content data. There was a linear relationship between the estimated NPP and NPP observed in forests in China. The average global NPP during the 6 years was about 123 Pg dry weight per year, and the maximum and minimum NPP appeared in 1991 and 1988, respectively.

Stand volume estimation by combining low laser-sampling density LiDAR data with QuickBird panchromatic imagery in closed-canopy Japanese cedar (Cryptomeria japonica) plantations

This study proposes a simple method for stand volume estimation by combining low laser-sampling density Light Detection and Ranging (LiDAR) data (i.e. 1 hit per 4 m2) with high-resolution optical imagery (i.e. 0.6 m) in coniferous plantations. The study area was in closed-canopy, mountainous, Japanese cedar (Cryptomeria japonica) plantations on undulating terrain at an elevation of 135–391 m above sea level. A total of 25 circular plots (0.04 ha) were established and stand volumes within plots were investigated in the field. The field-measured, plot-level stand volume ranged from 262.8 to 984.0 m3 ha−1 and the average value was 555.7 m3 ha−1. We used the measurements as validation data to evaluate estimates derived from an empirical regression model that was constructed on the basis of the allometry between crown diameter and diameter at breast height (DBH) from previous research. As a result, stand volume at various stand conditions could be estimated precisely regardless of different laser footprint sizes of 0.16–0.47 m when combining low-density LiDAR data with QuickBird panchromatic imagery. The maximum random error and root mean square error (RMSE) in stand volume estimates by data combination were 10% and 39% of the average stand volume, respectively. Thus, this method based on allometry and using low-density LiDAR data and high-resolution optical imagery could be capable of offering precise stand volume estimates in coniferous forests in different localities.

Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic- and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (k-NN) technique in southwestern Japan. Data from the Forest Resources Monitoring Survey—a national plot sampling survey in Japan—was used as in situ data in this study. The estimates obtained from three Landsat Enhanced Thematic Mapper Plus (ETM+) datasets acquired in different seasons with various combinations of additional feature variables were compared. The results showed that although the addition of environmental factors to satellite image data did not always help improve estimation accuracy, the use of summer rainfall (SRF) data had a consistent positive effect on accuracy improvement. Therefore, SRF may be a useful feature variable to consider in stand volume estimation in this study area. Moreover, the use of forest type information is very effective at reducing k-NN estimation errors when using an optimum combination of satellite image data and environmental factors. All of the results indicated that the k-NN technique combined with appropriate feature variables is applicable to nationwide stand volume estimation in Japan.

Stand Parameter Estimation of Artificial Evergreen Conifer Forests Using Airborne Images: An Evaluation of Seasonal Difference on Accuracy and Best Wavelength

The relationship between the stand parameters (top layer height (H1) and volume/ha (Vha)) and digital number (DN) were evaluated for evergreen conifer stands using three airborne images with 4-m spatial resolution, which were taken in June 1995, September 1993, and October 1994 using the Compact Airborne Spectrographic Imager (CASI). Estimation accuracy of the stand parameters, their seasonal changes, and suitable wavelength were analyzed using correlation coefficients and a regression analysis. The minimum DN of stands, which showed the darkness of a canopy shadow, had a higher correlation with H1 than the average and maximum DN while the average DN had a higher correlation with Vha. The green channels gave the highest correlation coefficients with H1 and Vha, which exceeded — 0.9 for the September and October images. However, the red channels had a consistently high correlation with the stand parameters for the three images. The near infrared channels gave poor correlations with H1 and Vha for the June image. Spectral variations among trees may affect the relationship between DN and the stand parameters in the leaf maturation period in June. Consequently, the late growing season was better at giving consistent results for the stand parameter studies. There was a linear relationship between the measured and the estimated stand parameters for the validation plots especially for the H1 case of September with sufficient accuracy. Nadir viewing images, which had high spatial resolution and a wide dynamic range such as the CASI images, were necessary to estimate the stand parameters accurately.

Estimation and validation of biomass of a mountainous agroecosystem by means of sampling, spectral data and QuickBird satellite image

Biomass estimation in agroecosystems (AESs) is important to understand their role in carbon exchange for a sustainable environment. We used field spectra and sampled biomass of an AES including cultivated and abandoned croplands to develop a simple biomass estimation model. The digital number (DN) of a QuickBird (QB) satellite image was converted to a reflectance factor using the dark object subtraction method and the spectral reflectance of asphalt. The relationship between the reflectance factor of field-based spectra and the QB image obtained in early July 2007 was insignificant in the blue (R 2 = 0.15) and green (R 2 = 0.18) bands but was significant (p < 0.05) in the red (R 2 = 0.57) and near-infrared (NIR, R 2 = 0.45) bands in the AES. Better correlations were obtained between field-based and QB-based vegetation indices (VIs). The best correlations were obtained with the normalized difference vegetation index (NDVI) (R 2 = 0.97, p < 0.001) and the ratio vegetation index (RVI) (R 2 = 0.99, p < 0.001). Biomass was significantly correlated with both field-based NDVI and RVI (R 2 = 0.79 and 0.72, respectively, p < 0.001). Although RVI saturated at higher biomass densities (>600 g m−2), NDVI showed a linear relationship. Other field-based VIs showed poorer correlations with biomass. The model was evaluated by incorporating it into high-resolution QB images to obtain the observed biomass. The relationship between field-estimated and QB-observed biomass appeared to be a one-to-one linear relationship (R 2 = 0.79). Thus, models using field spectra and sampled biomass can be applied to QB images for remote estimation of biomass in an AES.

Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data

Airborne light detection and ranging (LiDAR) has been used for forest biomass estimation for the past three decades. The performance of estimation, in particular, has been of great interest. However, the difference in the performance of estimation between stem volume (SV) and total dry biomass (TDB) estimations has been a priority topic. We compared the performances between SV and TDB estimations for evergreen conifer and deciduous broadleaved forests by correlation and regression analyses and by combining height and no-height variables to identify statistically useful variables. Thirty-eight canopy variables, such as average and standard deviation of the canopy height, as well as the mid-canopy height of the stands, were computed using LiDAR point data. For the case of conifer forests, TDB showed greater correlation than SV; however, the opposite was the case for deciduous broadleaved forests. The average- and mid-canopy height showed the greatest correlation with TDB and SV for conifer and deciduous broadleaved forests, respectively. Setting the best variable as the first and no-height variables as the second variable, a stepwise multiple regression analysis was performed. Predictions by selected equations slightly underestimated the field data used for validation, and their correlation was very high, exceeding 0.9 for coniferous forests. The coefficient of determination of the two-variable equations was smaller than that of the one-variable equation for broadleaved forests. It is suggested that canopy structure variables were not effective for broadleaved forests. The SV and TDB maps showed quite different frequency distributions. The ratio of the stem part of the broadleaved forest is smaller than that of the coniferous forest. This suggests that SV was relatively smaller than TDB for the case of broadleaved forests compared with coniferous forests, resulting in a more even spatial distribution of TDB than that of SV.

i-LOVE: ISS-JEM lidar for observation of vegetation environment

It is very important to watch the spatial distribution of vegetation biomass and changes in biomass over time, representing invaluable information to improve present assessments and future projections of the terrestrial carbon cycle. A space lidar is well known as a powerful remote sensing technology for measuring the canopy height accurately. This paper describes the ISS(International Space Station)-JEM(Japanese Experimental Module)-EF(Exposed Facility) borne vegetation lidar using a two dimensional array detector in order to reduce the root mean square error (RMSE) of tree height due to sloped surface.

Estimation of Leaf Area Index in a Mountain Forest of Central Japan with a 30-m Spatial Resolution Based on Landsat Operational Land Imager Imagery: An Application of a Simple Model for Seasonal Monitoring

An accurate estimation of the leaf area index (LAI) by satellite remote sensing is essential for studying the spatial variation of ecosystem structure. The goal of this study was to estimate the spatial variation of LAI over a forested catchment in a mountainous landscape (ca. 60 km2) in central Japan. We used a simple model to estimate LAI using spectral reflectance by adapting the Monsi-Saeki light attenuation theory for satellite remote sensing. First, we applied the model to Landsat Operational Land Imager (OLI) imagery to estimate the spatial variation of LAI in spring and summer. Second, we validated the model’s performance with in situ LAI estimates at four study plots that included deciduous broadleaf, deciduous coniferous, and evergreen coniferous forest types. Pre-processing of the Landsat OLI imagery, including atmospheric correction by elevation-dependent dark object subtraction and Minnaert topographic correction, together with application of the simple model, enabled a satisfactory 30-m spatial resolution estimation of forest LAI with a maximum of 5.5 ± 0.2 for deciduous broadleaf and 5.3 ± 0.2―for evergreen coniferous forest areas. The LAI variation in May (spring) suggested an altitudinal gradient in the degree of leaf expansion, whereas the LAI variation in August (mid-summer) suggested an altitudinal gradient of yearly maximum forest foliage density. This study demonstrated the importance of an accurate estimation of fine-resolution spatial LAI variations for ecological studies in mountainous landscapes, which are characterized by complex terrain and high vegetative heterogeneity.

Chapter 15 Terrestrial Net Primary Production (NPP) Estimation Using NOAA Satellite Imagery: Inter-annual Changes between 1982 and 1999

Abstract The light use efficiency (LUE) approach using the normalized difference vegetation index (NDVI) is the simplest method to estimate terrestrial net primary production (NPP). A simple LUE-based model, which used LUEs of plants, solar radiation, NDVI and stress functions for soil water and air temperature, was applied to estimate global terrestrial NPP between 1982 and 1999. NDVI images were computed using channels 1 and 2 of the Advanced Very High Resolution Radiometer (AVHRR) produced by the Pathfinder AVHRR LAND (PAL) datasets, after a simple radiometric correction. Anomalies in time series of global average monthly NDVI of the original PAL data, which were probably caused by volcanic ashes exposed in the atmosphere by huge eruptions such as Pinatubo, were successfully reduced in the computed NDVI. The solar radiation, air temperature and soil water of NCEP/NCAR Reanalysis data were also used for the NPP estimation. LUEs were determined based on the maximum photosynthetic rate in the literature for 17 land cover types of the IGBP-DIS classification. NPP was also computed using a constant LUE for the global vegetation to understand the effects of multiple LUE-settings. As a result, global NPP was estimated at between 58.5 and 62.6 petagram (Pg) carbon in 1983 and 1998, respectively, for the multiple LUE-settings and between 51.0 and 54.8 Pg carbon in 1983 and 1991, respectively, for the constant LUE-setting. NPP increased about 0.1 Pg carbon annually ( P 2 concentration. The rate of change in carbon absorption observed in that study is similar to that of NPP by our estimation. Inter-annual changes in NPP varied from region to region. Most clear long-term increasing or decreasing trends of NPP appeared in semiarid areas. The inter-annual changes in NDVI seemed to relate most clearly to the inter-annual NPP changes in these areas among parameters used for the NPP estimation, which were NDVI, average monthly air temperature, volumetric soil water content and solar radiation.

Chapter 16 Global Mapping of Net Primary Production

Abstract It is a well-known fact that atmospheric greenhouse gases are rapidly increasing within these 100 years, however, the sinks and sources of these gases are not necessarily clarified. Especially, sinks of carbon dioxide, which have the largest effects on global warming, are not evident. Generation of global map of net primary production (NPP) using earth-observing satellite data was performed in the research project named “International joint researches on global mapping of carbon cycle and its advancement” sponsored by the Ministry of Education, Culture, Sports, Science and Technology (MEXT). The results of this mapping project are briefly described in this chapter. The first global NPP maps using satellite data, which cover both ocean and continental ecosystems, have been obtained in this project. These global NPP maps have sufficient accuracy for a primary approximation. However, many problems remain, and various efforts are required to increase the accuracy of the global NPP data.