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

Evaluation of the Surface Water Distribution in North-Central Namibia Based on MODIS and AMSR Series

Semi-arid North-central Namibia has high potential for rice cultivation because large seasonal wetlands (oshana) form during the rainy season. Evaluating the distribution of surface water would reveal the area potentially suitable for rice cultivation. In this study, we detected the distribution of surface water with high spatial and temporal resolution by using two types of complementary satellite data: MODIS (MODerate-resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer–Earth Observing System), using AMSR2 after AMSR-E became unavailable. We combined the modified normalized-difference water index (MNDWI) from the MODIS data with the normalized-difference polarization index (NDPI) from the AMSR-E and AMSR2 data to determine the area of surface water. We developed a simple gap-filling method (“database unmixing”) with the two indices, thereby providing daily 500-m-resolution MNDWI maps of north-central Namibia regardless of whether the sky was clear. Moreover, through receiver-operator characteristics (ROC) analysis, we determined the threshold MNDWI (−0.316) for wetlands. Using ROC analysis, MNDWI had moderate performance (the area under the ROC curve was 0.747), and the recognition error for seasonal wetlands and dry land was 21.2%. The threshold MNDWI let us calculate probability of water presence (PWP) maps for the rainy season and the whole year. The PWP maps revealed the total area potentially suitable for rice cultivation: 1255 km2 (1.6% of the study area)