Ms. Yoanna Ristya Profile

Yoanna Ristya

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Proceedings Article | 24 December 2019 Paper

Estimation of aboveground biomass in urban forest area using SPOT 7 imageries in Tangerang City

Alifa Muthia, . Taqyuddin, Iqbal P. A. Shidiq, R. Saraswati, Y. Ristya

Proceedings Volume 11372, 1137215 (2019) https://doi.org/10.1117/12.2541749

KEYWORDS: Vegetation, Carbon, Analytical research, Ecosystems, Remote sensing, Data processing

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The national law in Indonesia stated that cities are required to have 30% of green open space from the total area. Tangerang City is one of the capital's buffer cities that has been continuously grown since the 1990s. The development of the city is quite rapid, and the emergence of various activities such as household activities, transportation, and industry are encouraging changes in green open space areas. Data from the regional statistic shows that open green space in Tangerang City is only 12.56% (2,319.21 hectares) of the total area. The less open green space area might degrade its main function as an absorber of emissions or pollutants, especially for carbon dioxide gas (CO₂). This study aims to estimate the biomass, total CO₂ stored by vegetation. This study uses direct measurements and vegetation index to formulate the ideal biomass formula. The pre-field activities begin with the extraction of NDVI (Normalized Differential Vegetation Index) and EVI (Enhanced Vegetation Index) from SPOT 7 imageries. The biomass allometric formulas used in this study are the Brown and Lugo equation, to find biomass values from tree stand parameters such as diameter breast height (DBH) and tree height. Quantitative and spatial analysis used in this study is a regression analysis of biomass and vegetation index value. The results show EVI has a better regression value and total biomass of around 26 million kilograms.

Proceedings Article | 24 December 2019 Paper

Detection of rice varieties based on spectral value data using UAV-based images

Fida Afdhalia, S. Supriatna, Iqbal Putut Ash Shidiq, Masita Dwi Mandini Manessa, Yoanna Ristya

Proceedings Volume 11372, 1137222 (2019) https://doi.org/10.1117/12.2541473

KEYWORDS: Vegetation, RGB color model, Remote sensing, Unmanned aerial vehicles, Agriculture

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Paddy is a strategic commodity in Indonesia. Paddy crop divided into hundreds of varieties with diverse characteristics. Therefore, information about the characteristics of each rice variety is needed. Also, several studies on the spectral characteristics of rice varieties have been carried out. These studies applied the vegetation indices approach to plant canopies. The aim of this study is detecting the spectral characteristics of rice varieties based on vegetation indices. Several vegetation indices, derived from Red, Green, Blue (RGB) bands, namely Excess Green Vegetation Index (ExG), Normalized Green Red Difference Index (NGRDI), and Visible Atmospherically Resistant Index (VARI). Paddy field image derived from Unmanned Aerial Vehicle (UAV) was carried out to analyzed three rice varieties namely Ciherang, IR 64, and IR 42. The result showed that three rice varieties in Bekasi Regency have diverse spectral characteristics. It evident from the spectral minimum-maximum value of each variety, especially using the NGRDI. Ciherang has the highest spectral value (at the beginning of growth) and IR 42 has the highest spectral value (at the middle and end of growth).

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