MODIFICATION OF INPUT IMAGES FOR IMPROVING THE ACCURACY OF RICE FIELD CLASSIFICATION USING MODIS DATA

The standard image classification method typically uses multispectral imageryon one acquisition date as an input for classification. Rice fields exhibit high variability inland cover states, which influences their reflectance. Using the existing standard method forrice field classification may increase errors of commission and omission, thereby reducingclassification accuracy. This study utilised temporal variance in a vegetation index as amodified input image for rice field classification. The results showed that classification ofrice fields using modified input images provided a better result. Using the modifiedclassification input improved the correspondence between rice field area obtained from theclassification result and reference data (R2 increased from 0.2557 to 0.9656 for regencylevelcomparisons and from 0.5045 to 0.8698 for district-level comparisons). Theclassification accuracy and the estimated Kappa value also increased when using themodified classification input compared to the standard method, from 66.33 to 83.73 andfrom 0.49 to 0.77, respectively. The commission error, omission error, and Kappa variancedecreased from 68.11 to 42.36, 28.48 to 27.97, and 0.00159 to 0.00039, respectively, whenusing modified input images compared to the standard method. The Kappa analysisconcluded that there are significant differences between the procedure developed in thisstudy and the standard method for rice field classification. Consequently, the modifiedclassification method developed here is significant improvement over the standardprocedure

APPLICATION OF VAN HENGEL AND SPITZER ALGORITHM FOR INFORMATION ON BATHYMETRY EXTRACTION USING LANDSAT DATA

Remote sensing technology provides an opportunity for effective and efficient bathymetry mapping, especially in areas which level of depth changes quickly. Bathymetry information is very useful for hydrographic and shipping safety. Landsat medium resolution satellite imagery can be used for the extraction of bathymetry information. This study aims to extract information from the Landsat bathymetry by using Van Hengel and Spitzer rotation algorithm transformation (1991) in the water of Menjangan Island, Bali. This study shows that Van Hengel and Spitzer rotation algorithm transformation (1991) can be used to extract information on the bathymetry of Menjangan Island. Extraction of bathymetric information generated from Landsat TM imagery data in March 19, 1997 had shown the depth interval of (-0.6) m to (-12.3) m and R2 value of 0.671. While Data LANDSAT ETM + dated June 23, 2000 resulted in depth interval of 0 m to (-19.1) m and R2 value of 0.796. Furthermore, data LANDSAT ETM + dated March 12, 2003 resulted in depth interval of 0 m to (-22.5) m and R2 value of 0.931

GIS Based Analysis of Agroclimate Land Suitability for Banana Plants in Bali Province, Indonesia

The need for bananas in Bali far exceeds the production. To obtain optimal production according to their genetic potential, the development of banana cultivation should be preceded by a land suitability evaluation study. This study aims to evaluate the land suitability based on agroecological parameters such as rainfall, altitude, dry month, slope, and considering current land use. The results showed that 257.467 ha or 46.16% of the area of Bali Province has the potential to be planted with bananas. Buleleng Regency has the widest area for the development of banana plants, followed by Karangasem, Tabanan, Jembrana and Bangli. Denpasar town has the smallest suitable area. Based on the observed agroclimate parameters, slope is the most severe limiting factor in banana cultivation, while rainfall, altitude, and dry months are not significant limiting factors. Recommended land use for the development of banana plants is garden, grass, rain-fed rice field, scrub, bare land, and moor

Identification of Banana Plants from Unmanned Aerial Vehicles (UAV) Photos Using Object Based Image Analysis (OBIA) Method (A Case Study in Sayang Village, Jatinangor District, West Java)

  Banana is one of the leading fruit commodities of Indonesia and ranks the sixth position as one of the largest banana producers in the world. There are more than 200 types of banana in Indonesia. The utilization of bananas is influenced by the local culture, where in every 10 horticultural households, 5 of them plant bananas both as garden plants or field plants. This horticultural crop is expectantly being one of the actions to improve economic prosperity especially in rural areas. In maintaining the diversity of the growing bananas in rural areas, a geospatial approach to identify the vegetation is required. Remote sensing technology is one of the solutions to observe and to develop banana plants with one of the methods namely Object Based Image Analysis (OBIA). This method consists of segmentation, classification, and validation. In classification process, the OBIA method distinguishes objects not only based on pixel values but also on the basis of the shape, area, and texture around them. This research has proven that the classification using OBIA method is better than the traditional classification such as maximum likelihood classification method to identify banana plants. OBIA method can quickly identifies the vegetation and non-vegetation, also the regular plants and banana plants

Belajar Sendiri : Menganalasis Data Spasial dengan Arcview GIS 3.3 Untuk Pemula

xi, 376 hlm,; 16 c

Belajar sendiri : menganalisa data spasial dengan arcview GIS 3.3 untuk pemula

xi, 376 hal .; 21 cm

RELATIONSHIPS BETWEEN RICE GROWTH PARAMETERS AND REMOTE SENSING DATA

Rice is an agriculture plants that has the specific characteristic in the life stage due to the growth stage having different proportion of vegetation, water, and soil. Vegetation index is one of the satellite remote sensing parameter that is widely used to monitor the global vegetation cover. The objective of the study is to know the spectral characteristic of rice plant in the life stage and find the relationship between the rice growth parameters and the remote sensing data by the Landsat ETM data using the correlation and regression analysis. The result of study shows that the spectral characteristic of the rice before one month of age is defferent comparing after one month. All of the examined vegetation index has close linear relationship with rice coverage. Difference Vegetation Index (DVI) is the best vegetation index which estimates rice coverage with equation y = 1.762x + 2.558 and R degree value was 0.946. Rice age has a high quadratic relationship with all of evaluated vegetation index. Transformed Vegetation Index (TVI) is the best vegetation to predict the age of the rice. Formula y = 0.013x - 1.625x + 145.8 is the relationship form between the rice age and the TVI with R = 0.939. Peak of the vegetation index of rice is in the rice age of 2 months. This period is the transition of vegetative and generative stages. Keywords: Vegetation index, Rice growth, Spectral characteristic, Landsat ETM