Abstract:Remotely sensed imagery is a type of data that is compatible with the monitoring and mapping of changes in built-up and bare land within urban areas as the impacts of population growth and urbanisation increase. The application of currently available remote sensing indices, however, has some limitations with respect to distinguishing built-up and bare land in urban areas. In this study, a new index for transforming remote sensing data for mapping built-up and bare land areas is proposed. The Enhanced Built-Up and Bareness Index (EBBI) is able to map built-up and bare land areas using a single calculation. The EBBI is the first built-up and bare land index that applies near infrared (NIR), short wave infrared (SWIR), and thermal infrared (TIR) channels simultaneously. This new index was applied to distinguish built-up and bare land areas in Denpasar (Bali, Indonesia) and had a high accuracy level when compared to existing indices. The EBBI was more effective at discriminating built-up and bare land areas and at increasing the accuracy of the built-up density percentage than five other indices.

Forecasting rice yield before harvest time is important to supporting planners and decision makers to predict the amount of rice that should be imported or exported and to enable governments to put in place strategic contingency plans for the redistribution of food during times of famine. This study used the Normalized Difference Vegetation Index (NDVI) of Landsat Enhanced Thematic Mapper plus (ETM+) images of rice plants to estimate rice yield based on field observation. The result showed that the rice yield could be estimated using the exponential equation of y = 0.3419e^4.1587x, where y and x are rice yield and NDVI, respectively. The R² and SE of the estimation were 0.852 and 0.077 ton/ha, respectively. An accuracy assessment of rice yield estimation using Landsat images was performed by comparing the rice yields from the estimation result and the reference data. The results show that the linear relationship with the R² and SE of the estimation were 0.9262 and 0.21 ton/ha, respectively. The R² is greater than or equal to 0.8, which demonstrates a strong agreement between the remotely sensed estimation and the reference data. Thus, the Landsat ETM+ has good potential for application to rice yield estimation.

Remote sensing data of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis for 13 years have been used to observe the spatial patterns relationship of rainfall with El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) over Indonesia. Linear correlation was measured to determine the relationship level by the restriction analysis of seasonal and monthly relationship, while the partial correlation technique was utilized to distinguish the impact of one phenomenon from that of the other. Application of remote sensing data can reveal an interaction of spatial-temporal relationship of rainfall with ENSO and IOD between land and sea. In general, the temporal patterns relationship of rainfall with ENSO confirmed fairly similar temporal patterns between rainfall with IOD, which is high response during JJA (June-July-August) and SON (September-October-November) and unclear response during DJF (December-January-February) and MAM (March-April-May). Spatial patterns relationship of both phenomena with rainfall is high in the southeastern part of Sumatra Island and Java Island during JJA and SON. During the SON season, IOD has a higher relationship level than ENSO in this part. In the spatial-temporal pattern seen, a dynamic movement of the relationship between IOD and ENSO with rainfall in Indonesia is indicated, where the influence of ENSO and IOD started during JJA especially in July in the southwest of Indonesia and ended in the DJF period especially in January in the northeast of Indonesia.

Existing methods for rice field classification have some limitations due to the large variety of land covers attributed to rice fields. This study used temporal variance analysis of daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images to discriminate rice fields from other land uses. The classification result was then compared with the reference data. Regression analysis showed that regency and district comparisons produced coefficients of determination (R 2 ) of 0.97490 and 0.92298, whereas the root mean square errors (RMSEs) were 1570.70 and 551.36 ha, respectively. The overall accuracy of the method in this study was 87.91%, with commission and omission errors of 35.45% and 17.68%, respectively. Kappa analysis showed strong agreement between the results of the analysis of the MODIS data using the method developed in this study and the reference data, with a kappa coefficient value of 0.8371. The results of this study indicated that the algorithm for variance analysis of multitemporal MODIS images could potentially be applied for rice field mapping.

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