Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Evaluation of Geographical and Annual Changes in Rice Planting Patterns with Satellite Images in Flood-Prone Area of Pampanga River Basin, the Philippines

Kohei Hosonuma
,
Kentaro AIDA
ORCID logo ,
Vicente Jr Ballaran
,
Naoko Nagumo
,
Patricia Ann J Sanchez
,
Tsuyoshi SUMITA
,
Koki Homma
* ORCID logo
Version 1 : Received: 31 October 2023 / Approved: 31 October 2023 / Online: 31 October 2023 (08:16:03 CET)

A peer-reviewed article of this Preprint also exists.

Hosonuma, K.; Aida, K.; Ballaran, V., Jr.; Nagumo, N.; Sanchez, P.A.J.; Sumita, T.; Homma, K. Evaluation of Geographical and Annual Changes in Rice Planting Patterns Using Satellite Images in the Flood-Prone Area of the Pampanga River Basin, the Philippines. Remote Sens. 2024, 16, 499. Hosonuma, K.; Aida, K.; Ballaran, V., Jr.; Nagumo, N.; Sanchez, P.A.J.; Sumita, T.; Homma, K. Evaluation of Geographical and Annual Changes in Rice Planting Patterns Using Satellite Images in the Flood-Prone Area of the Pampanga River Basin, the Philippines. Remote Sens. 2024, 16, 499.

Abstract

Floods are some of the most devastating crop disasters in Southeast Asia. The Pampanga River Basin in the Philippines is a representative flood-prone area, where cultivation patterns are varied according to the flood risk. However, quantitative analyses on the effects of flooding on cultivation patterns remain quite limited. Accordingly, this study analyzes MODIS LAI data (MCD15A2H) from 2007 to 2022 to evaluate annual and geographical differences in cultivation patterns in the Candaba municipality of the basin. The analysis consists of two stages of hierarchical clustering: a first stage for area classification and a second stage for the classification of annual LAI dynamics. As a result, Candaba is divided into four areas, which are found to be partly consistent with the observed flood risk. Subsequently, the annual LAI dynamics in each area are divided into two or three clusters. The obvious differences among the clusters are caused by flooding in the late rainy season, delaying the start of planting in the dry season. The clusters also indicate that the cultivation patterns slightly changed over the 16 years of the study period. The results of this study suggest that the two-stage clustering approach provides an effective tool for the analysis of MODIS LAI data when considering cultivation patterns characterized by annual and geographical differences.

Keywords

Philippines; flood-prone area; MODIS; time-series data; remote sensing; rice

Subject

Biology and Life Sciences, Agricultural Science and Agronomy

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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