DETECTING AND COUNTING COCONUT TREES IN PLEIADES SATELLITE IMAGERY USING HISTOGRAM OF ORIENTED GRADIENTS AND SUPPORT VECTOR MACHINE

This paper describes the detection of coconut trees using very-high-resolution optical satellite imagery. The satellite imagery used in this study was a panchromatic band of Pleiades imagery with a spatial resolution of 0.5 metres. The authors proposed the use of a histogram of oriented gradients (HOG) algorithm as the feature extractor and a support vector machine (SVM) as the classifier for this detection. The main objective of this study is to find out the parameter combination for the HOG algorithm that could provide the best performance for coconut-tree detection. The study shows that the best parameter combination for the HOG algorithm is a configuration of 3 x 3 blocks, 9 orientation bins, and L2-norm block normalization. These parameters provide overall accuracy, precision and recall of approximately 80%, 73% and 87%, respectively

Simple Algorithm for Estimation of Photosynthetically Active Radiation (PAR) Using Satellite Data

A simple algorithm for estimation of Photosynthetically Active Radiation (PAR) with satellite data is developed. The algorithm, which is based on a simple radiative transfer scheme, requires only one spectral channel (red) of a satellite. The algorithm was tested with satellite data taken by Terra/MODIS and Aqua/MODIS. The relative error of daily total PAR estimated by this algorithm was 27%. It can provide PAR maps with a resolution as fine as 250 m, with which the topographic influence on the PAR distribution (due to local clouds) is clearly seen

Monitoring of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors

In this study, a novel data fusion approach was used to monitor the water-body extent in a tropical wetland (Lake Sentarum, Indonesia). Monitoring is required in the region to support the conservation of water resources and biodiversity. The developed approach, random forest database unmixing (RFDBUX), makes use of pixel-based random forest regression to overcome the limitations of the existing lookup-table-based approach (DBUX). The RFDBUX approach with passive microwave data (AMSR2) and active microwave data (PALSAR-2) was used from 2012 to 2017 in order to obtain PALSAR-2-like images with a 100 m spatial resolution and three-day temporal resolution. In addition, a thresholding approach for the obtained PALSAR-2-like backscatter coefficient images provided water body extent maps. The validation revealed that the spatial patterns of the images predicted by RFDBUX are consistent with the original PALSAR-2 backscatter coefficient images (r = 0.94, RMSE = 1.04 in average), and that the temporal pattern of the predicted water body extent can track the wetland dynamics. The PALSAR-2-like images should be a useful basis for further investigation of the hydrological/climatological features of the site, and the proposed approach appears to have the potential for application in other tropical regions worldwide

Field experiments to test the use of the normalized-difference vegetation index for phenology detection

Some previous studies have detected the timing of leaf expansion and defoliation using the normalized-difference vegetation index (NDVI), but to examine tree phenology using satellite data, NDVI results should be confirmed using ground-truthing. We examined the relationship between NDVI and tree phenology during leaf expansion and defoliation by simultaneously observing the spectral reflectance of the canopy surface and canopy surface images in a cool-temperate deciduous broad-leaved forest. To define the timing of leaf expansion and defoliation using NDVI, the index should meet three criteria: (1) NDVI should exhibit a monotonous increase or decrease (monotonicity). (2) The relationship between NDVI and the forest canopy\u27s status should be unique (uniqueness). (3) The method is robust against the systematic noise (bias) (robustness). In the spring, NDVI values of 0.2–0.3 (relative values: 0.15–0.28) and 0.6–0.7 (relative values: 0.65–0.78) satisfied all three criteria. NDVI values of 0.6–0.7 can serve as potential criteria for detecting the timing of leaf expansion. In autumn, no NDVI values satisfied all three criteria. Thus, NDVI does not appear to be useful for detecting the timing of defoliation. For an area where evergreen vegetation or snow covers the forest floor in winter, our results suggest that previous uses of NDVI to identify the timing of leaf expansion and defoliation on the basis of the date of the maximum rate of growth or reduction of NDVI and the date with a value midway between the year\u27s maximum and minimum values are misleading

In-situ observations on a moderate resolution scale for validation of the Global Change Observation Mission-Climate ecological products: The uncertainty quantification in ecological reference data

We report the ground validation activity for the terrestrial ecology products (leaf area index, above-ground biomass, and fraction of absorbed photosynthetically active radiation) of the Second-generation Global Imager (SGLI) on JAXA’s satellite named “Global Change Observation Mission-Climate.” We gave special attention to quantifying the uncertainty propagating from errors in the ecological reference data (ERD) obtained by the field work. Specifically, for optimal design and practical implementation of the field work with small uncertainty and small cost, we proposed: 1) a practical target which defined the accuracy threshold of ERD as a quarter of the satellite accuracy threshold, and 2) a calculation method of the uncertainty quantification of ERD by accounting for the uncertainty propagating from the empirical regression equations (such as allometry equations) and the statistical distribution of the population. As a result, we obtained ERD for GCOM-C/SGLI in various plant functional types (a deciduous needle-leaved forest, a deciduous broad-leaved forest, an evergreen needle-leaved forest, and dry and wet grassland) with sufficient quality, especially with a coverage area of 500 m × 500 m which can include a footprint of the sensor (250 m × 250 m) in any situation. We demonstrated: 1) the accuracy target was the key decision to make the practical calibration/validation work, 2) the regression uncertainty had a large impact, although little literature provided sufficient ancillary data about the regression equations necessary for quantification of the uncertainty, and 3) the optimal protocols of ERD observation can change depending on situations (plant functional types, phenology stages, type of products, accuracy targets, resources, and development of observation instruments and techniques); hence the choice should be made on the basis of quantification of the uncertainties