Using Multi-Temporal Satellite Data to Analyse Phenological Responses of Rubber (Hevea brasiliensis) to Climatic Variations in South Sumatra, Indonesia

Azizan, Fathin Ayuni; Astuti, Ike Sari; Aditya, Mohammad Irvan; Febbiyanti, Tri Rapani; Williams, Alwyn; Young, Anthony; Aziz, Ammar Abdul

doi:10.3390/rs13152932

Cited by 20 publications

(21 citation statements)

References 92 publications

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“…It was shown that the phenological SOS, EOS, and LOS were closely related to such deviations as extreme precipitation and increased temperature. The increase in the temperature by 1 °C led to an increase in SOS by ~25 days and EOS -by ~14 days (Azizan, 2021).…”

Section: Discussionmentioning

confidence: 97%

Dynamics of climate changes and its effect on the performance of cereals according to satellite data

et al. 2022

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Aim. To study the effect of climate changes on the performance of cereals. Methods. Remote, statistical, and analytic methods. Climate changes were determined by the sum of radiation temperatures of the earth’s surface, calculated using the data of the infrared range (10.3–11.3; 11.4–12.4 μm) of the high precision radiometer AVHRR of the meteorological satellites of the Earth, NOAA. The effect of climate changes on the state of vegetation, including the phenological parameters, such as the beginning, the end, and the length of the vegetation peri- od, was determined by the Normalized Difference Vegetation Index (NDVI) obtained using the data of infrared (0.72–1.1 μm) and red (0.58–0.68 μm) ranges of the abovementioned radiometer. The satellite data are openly accessed on the website of STAR NESDIS NOAA – Satellite Applications and Research of NOAA’s National En- vironmental Satellite Data Information Services of the National Ocean and Atmosphere Research Department of the USA – http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/. The dynamics of СО2 concentration in the air was determined using the satellite data of AIRS/Aqua (https://disc.gsfc.nasa.gov/datasets/AIRS3C2M_005/summary), the precipitation dynamics – using the data of ERA5 ECMWF/Copernicus Climate Change Service (https://devel- opers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY#description). The association be- tween the performance of cereals and satellite data was found by the method of correlation and regression analysis. Results. Using the satellite data, it was found that in 1982–2021, the sum of radiation temperatures of the earth’s surface reliably increased in the vegetation period (April–September), and the amount of precipitation tended to decrease in the zones of Polissia, Forest-Steppe, and Steppe. The highest tempo of warming was observed in the territory of the Steppe. In general, the warming had a positive effect on the state of vegetation, according to NDVI, and on the performance of cereals. However, in the recent decade (2010–2020), the positive effect of the warming on the vegetation state decreased, and according to the NDVI trend, there has been a tendency towards the negative effect of the further increase in the temperature, which is especially remarkable for the Steppe and Forest-Steppe. On the contrary, the warming in the Polissia zone created the conditions for the increase in the performance of agri- cultural cereals. Conclusions. From 1982 till 2021, there was a strong tendency towards the increase in the sum of radiation temperatures of the earth’s surface in the vegetation period and the decrease in the precipitation amount in Polissia, Forest-Steppe, and Steppe. According to the NDVI data, due to the warming, the length of the vegeta- tion period increased by 14–21 days on average, which had a positive effect on the vegetation. Since 2010, the acceleration in the warming tempo has been observed. The average annual increase in the sum of the radiation tem- peratures of the earth’s surface in the vegetation period is 15–16 °С – for Polissia, 14–18 °С for the Forest-Steppe, and 18–20 °С for the Steppe. According to this tendency, if the current regularity in the temperature increase in 1982–2021 is preserved in the nearest future, the temperature of the vegetation period will additionally increase, and the average sum of temperatures of the earth’s surface will reach 4000 °С in Polissia, which was notable for the Forest-Steppe in 1982–2021, 4440–4600 °С – in the Forest-Steppe, and up to 5800–5900 °С in the Steppe. In 1982–2000, there was a strong regularity of the positive effect of the warming during the vegetation period both on the vegetation state according to the NDVI index, and the performance of cereals in all three natural climatic zones. However, after 2000, due to the negative effect of high summer temperatures on the crops, the effect of the warming on vegetation considerably decreased, which was especially notable for the Steppe where the increase in NDVI compared to the previous period was on average 9.41 % in 1992–2001, and in 2012–2021 – 1.78 %.

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Section: Discussionmentioning

confidence: 97%

Dynamics of climate changes and its effect on the performance of cereals according to satellite data

et al. 2022

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“…As a result, to reduce the impact of weather fluctuations on the analyses, all images were captured during the dry season. It is recommended that images be from the same recording period to reduce variations from image to image caused by sun angle, soil moisture, atmospheric conditions, and differences in vegetation phenology (Azizan et al, 2021;Verstegen et al, 2019). (Gashaw et al, 2017;Ruben et al, 2020).…”

Section: Datamentioning

confidence: 99%

Historical and future dynamics of land use land cover and its drivers in Ajora‐Woybo watershed, Omo‐Gibe basin, Ethiopia

Toma

Belete

Ulsido

2022

Natural Resource Modeling

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Land use land cover (LULC) dynamics have long been recognized as a significant driver of natural resource change. As a result, understanding the spatial and temporal variation of LULC in the watershed is essential for effective natural resource management and long‐term development. This study attempts to analyze the dynamics and change drivers from 1990 to 2020 and predict the situation for 2035 and 2050 in the Ajora‐Woybo watershed. ArcGIS 10.3 and ERDAS 2015 were used to analyze quantitative data from Landsat imagery. For supervised image classification, a Maximum‐Likelihood classification algorithm was used. To identify driver variables, focus groups and key informants' interviews were done. TerrSet 18.31 software was used to predict LULC utilizing the Multi‐Layer Perceptron Neural Network and Cellular Automata‐Markov Chain models incorporated in Land Change Modeler. Six LULC classes were discovered: cultivated land, built‐up, shrub land, forest land, bare land, and water body. Cultivated land, built‐up area, and bare land have increased at the expense of shrub land and forest land over the last three decades. Trends in water bodies show both decreasing and increasing trends. According to the predicted outcomes, cultivated land, built‐up and bare land has increased, while shrub land and forest land have declined. Finally, agricultural expansion, population growth, wood extraction, resettlement, urbanization, and lack of environmental consideration were identified as the major drivers of LULC change. The study demonstrated that there have been significant changes in the watershed LULC. As a result, reversing the predicted conditions is critical to ensuring the watershed long‐term viability.

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“…Most of the studies identified canopy density based on the vegetation index on multispectral satellite images. In Azizan's research [10], a phenology analysis of canopy density was performed using NDVI on Sentinel-2 imagery and MODIS temporally. Meanwhile, Razak's research [11] used several vegetation indices such as NDVI, EVI, LAI, and RGRI on Landsat ETM imagery to detect canopy density as a basis for time seriesphenology analysis.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach to assess rubber plant health through canopy density mapping using very high-resolution aerial photographs

Ayu,

Manessa,

Stevanus

et al. 2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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The health of Indonesian rubber plantations has recently been compromised by rubber leaf fall disease, prompting a need for effective monitoring techniques. This study explores the use of high-resolution aerial photographs to assess rubber plant health through canopy density analysis. We employed the Random Forest machine learning algorithm for this purpose, focusing on two classification systems: [low, medium, high] and [low, high] canopy densities. Our findings reveal contrasting levels of accuracy between the two classification systems. The three-tier classification ([low, medium, high]) resulted in moderate accuracy (Overall Accuracy: 0.50, Kappa Value: 0.24), suggesting that this approach might be too detailed for the task. In contrast, the binary classification ([low, high]) demonstrated significantly better performance, with satisfactory accuracy (Overall Accuracy: 0.76, Kappa Value: 0.33). This improvement indicates that a simpler classification system with fewer categories is more effective for identifying the health of rubber plants using aerial photographs and machine learning techniques. This study underscores the importance of selecting an appropriate level of classification detail in machine learning models for agricultural monitoring. The results suggest that less complex models, with fewer canopy density categories, are more suitable for accurately assessing the health of rubber plants in situations like the rubber leaf fall disease outbreak in Indonesia.

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Using Multi-Temporal Satellite Data to Analyse Phenological Responses of Rubber (Hevea brasiliensis) to Climatic Variations in South Sumatra, Indonesia

Cited by 20 publications

References 92 publications

Dynamics of climate changes and its effect on the performance of cereals according to satellite data

Dynamics of climate changes and its effect on the performance of cereals according to satellite data

Historical and future dynamics of land use land cover and its drivers in Ajora‐Woybo watershed, Omo‐Gibe basin, Ethiopia

Machine learning approach to assess rubber plant health through canopy density mapping using very high-resolution aerial photographs

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