The objective of this study was to obtain the fundamental results for forest tree height estimation at stand level by using visible and near infra red (NIR) spectra analysis in the 400-1000 nm region and tree height data. This study was carried out at the Takakuma Experimental Forest located on the Ohsumi island of the Kagoshima prefecture. The original absorbance spectra and their second derivative spectra in the 400-1000 nm region were obtained; bands around 492, 581, and 673 nm that may be assigned to chlorophyll absorption were observed in the 400-700 nm region, and a small peak around 947 nm may be assigned to the second overtone stretching mode of the intra-molecular hydrogen-bonded OH group. The intensities of the inflection point of bands at 492 and 673 nm observed by the second derivative spectra showed a linear increase with a high correlation corresponding to the increase of tree height. The score of the first component (PC1) obtained by Principal Component Analysis (PCA) also showed a linear increase with tree height change. All of the expected absorptions were observed by the loadings of PC1. For tree height estimation, a simple linear regression, ratio regression, and Partial Least-Squares (PLS) regression using feature bands identified by second derivative were calculated. In the case of a simple linear regression using a band at 673 nm and PLS regression using a band in the 644-699 nm region, a coefficient of determination (R²) of more than 0.8 and a root mean square error (RMSE) of less than 5.1 were obtained, respectively. The results obtained in the present study have demonstrated that the visible and NIR spectral characteristics, especially the chlorophyll absorption band, combined with spectral analysis are powerful tools for the estimation of tree height.

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The aim of the present study is to use data from relevant government agencies to understand the change in rice production and land use since 2000 in North Sumatra, Indonesia, and to examine the combination of climatic and socio-economic factors affecting the change. Interviews and focused group meetings were also held to overcome the limitation of data availability. This study finds that the consistent increase in rice productivity has been offset by the reduction in rice harvest area, resulting in almost no growth in rice production over the last decade in North Sumatra. In contrast, oil palm plantations, particularly smallholder estates, have been expanding in the province. Four factors are identified as those affecting the change in land use: (1) climate conditions, (2) economic environment, (3) rice planting index, and (4) distance from palm oil enterprise estates. At the time of substantial reduction in rice harvest area, unusual climate conditions were observed across different regencies in North Sumatra, as was the case in 2006, when Medan, the provincial capital, recorded the highest annual rainfall during the last 20 years. The responses of the farmers to the rice planting index and the proximity to palm oil enterprise estates, both of which vary across the province, are differentiated into three types: (1) land use conversion from rice production to oil palm plantation, (2) conversion to other cash crops, and (3) staying with rice production. As oil palm is more resilient to rainfall variability, land use conversion from rice to oil palm can be considered a good adaptation from the farmers perspective. The large scale of the conversions, however, may be a threat to the food security of the society as a whole. This indicates that the countermeasures face trade-offs between different social groups, as well as between adaptation actions and other development priorities.

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Globe temperature is one element of the heat stress index, the Wet Bulb Globe Temperature, which is used to evaluate how radiation adds to thermal discomfort in the workplace. As the measurement of globe temperature is not standardized, empirical equations were introduced to estimate the globe temperature from weather factors, including air temperature, solar radiation and wind speed. As it was not known whether these equations were applicable in an urban park environment with vegetation, we observed the globe temperature using a set of instruments in three parks. The observation along with the heat balance analysis of the globe revealed that the globe temperature depended curvilinearly on solar radiation and that wind speed influenced this dependence. We compared two previously proposed empirical equations to the observed globe temperature and found both equations had systematic estimation errors. Although the errors were reduced by fitting the equations to the observed data and reevaluating their numerical constants, the equations still had shortcomings, as one did not consider wind speed and the other included a discontinuity. We therefore derived a new equation based on the heat balance equations of the globe with its numerical constants experimentally determined. This equation was able to predict the curvilinear dependence of the globe temperature on global solar radiation without any discontinuity, and it also showed the globe temperature response to wind speed.

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In the present study, we calculated seven vegetation indices (simple ratio [SR], normalized difference vegetation index [NDVI], enhanced vegetation index [EVI], soil-adjusted vegetation index [SAVI], optimized soil-adjusted vegetation index [OSAVI], land surface water index [LSWI], and greenery ratio [GR]) based on MODIS surface reflectance data and analyzed the relationships between these indices and the aboveground biomass in semiarid grasslands in Mongolia. The measured values of the biomass for the study area ranged from 7.5 to 137.0 g dry weight (dw) m^-2. The results revealed that NDVI, SR, and OSAVI were strongly correlated with the biomass. Among these, NDVI showed the best performance as a proxy of the plant biomass of Mongolian grasslands. The relationships of NDVI and OSAVI with the temporal variation and spatial pattern of biomass were statistically similar, suggesting that a single linear equation was appropriate to derive aboveground biomass values from NDVI or OSAVI.

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