Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine

Li, Yuchen; Liu, Chenli; Zhang, Jun; Zhang, Ping; Xue, Yufei

doi:10.1109/jstars.2021.3110763

Cited by 8 publications

(5 citation statements)

References 63 publications

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“…The dropping temperatures and decreasing precipitation during the hot-dry season may subject rubber trees to drier conditions ( Figure 7F ), the optimal temperature range for rubber tree growth falls between 20°C and 22 °C ( Yeang, 2007 ; Liu et al., 2016 ; Liyanage et al., 2019 ). The decrease in precipitation and maintenance of favorable sunlight conditions were advantageous to the photosynthesis of rubber trees and further delayed EOS ( Li et al., 2018 ; Gutiérrez-Vanegas et al., 2020 ; Li et al., 2021 ). Cold stress is identified as a primary factor causing defoliation in rubber trees, with severe defoliation occurring within a relatively short time frame in the Xishuangbanna ( Liu W. et al., 2013 ; Lin et al., 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province

Lai,

Chen,

Yin

et al. 2023

Front. Plant Sci.

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The ongoing global warming trajectory poses extensive challenges to plant ecosystems, with rubber plantations particularly vulnerable due to their influence on not only the longevity of the growth cycle and rubber yield, but also the complex interplay of carbon, water, and energy exchanges between the forest canopy and atmosphere. However, the response mechanism of phenology in rubber plantations to climate change remains unclear. This study concentrates on sub-optimal environment rubber plantations in Yunnan province, Southwest China. Utilizing the Google Earth Engine (GEE) cloud platform, multi-source remote sensing images were synthesized at 8-day intervals with a spatial resolution of 30-meters. The Normalized Difference Vegetation Index (NDVI) time series was reconstructed using the Savitzky-Golay (S-G) filter, coupled with the application of the seasonal amplitude method to extract three crucial phenological indicators, namely the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS). Linear regression method, Pearson correlation coefficient, multiple stepwise regression analysis were used to extract of the phenology trend and find the relationship between SOS, EOS and climate factors. The findings demonstrated that 1) the phenology of rubber plantations has undergone dynamic changes over the past two decades. Specifically, the SOS advanced by 9.4 days per decade (R2 = 0.42, p< 0.01), whereas the EOS was delayed by 3.8 days per decade (R2 = 0.35, p< 0.01). Additionally, the LOS was extended by 13.2 days per decade (R2 = 0.55, p< 0.01); 2) rubber phenology demonstrated a notable sensitivity to temperature fluctuations during the dry season and precipitation patterns during the rainy season. The SOS advanced 2.0 days (r =−0.19, p< 0.01) and the EOS advanced 2.8 days (r =−0.35, p< 0.01) for every 1°C increase in the cool-dry season. Whereas a 100 mm increase in rainy season precipitation caused the SOS to be delayed by 2.0 days (r = 0.24, p< 0.01), a 100 mm increase in hot-dry season precipitation caused the EOS to be advanced by 7.0 days (r =-0.28, p< 0.01); 3) rubber phenology displayed a legacy effect of preseason climate variations. Changes in temperature during the fourth preseason month and precipitation during the fourth and eleventh preseason months are predominantly responsible for the variation in SOS. Meanwhile, temperature changes during the second, fourth, and ninth preseason months are primarily responsible for the variation in EOS. The study aims to enhance our understanding of how rubber plantations respond to climate change in sub-optimal environments and provide valuable insights for sustainable rubber production management in the face of changing environmental conditions.

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Section: Discussionmentioning

confidence: 99%

Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province

Lai,

Chen,

Yin

et al. 2023

Front. Plant Sci.

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“…Southeast Asia boasts expansive plantations and serves as a significant producer of tropical cash crops, including rubber, oil palm, coconut, and banana fiber. It stands as the world's largest production region, encompassing 85% of the world's rubber plantations and 90% of rubber production [33]. Additionally, Southeast Asia is home to over 80% of oil palm plantations and contributes to more than 90% of global palm oil production [28].…”

Section: Southeast Asiamentioning

confidence: 99%

A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data

Liu,

Yang,

Wang

et al. 2023

Remote Sensing

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Currently, many globally accessible forest mapping products can be utilized to monitor and assess the status of and changes in forests. However, substantial disparities exist among these products due to variations in forest definitions, classification methods, and remote sensing data sources. This becomes particularly conspicuous in regions characterized by significant deforestation, like Southeast Asia, where forest mapping uncertainty is more pronounced, presenting users with challenges in selecting appropriate datasets across diverse regions. Moreover, this situation impedes the further enhancement of accuracy for forest mapping products. The aim of this research is to assess the consistency and accuracy of six recently produced forest mapping products in Southeast Asia. These products include three 10 m land cover products (Finer Resolution Observation and Monitoring Global LC (FROM-GLC10), ESA WorldCover 10 m 2020 (ESA2020), and ESRI 2020 Land Cover (ESRI2020)) and three forest thematic mapping products (Global PALSAR-2 Forest/Non-Forest map (JAXA FNF2020), global 30 m spatial distribution of forest cover in 2020 (GFC30_2020), and Generated_Hansen2020, which was synthesized based on Hansen TreeCover2010 (Hansen2010) and Hansen Global Forest Change (Hansen GFC) for the year 2020). Firstly, the research compared the area and spatial consistency. Next, accuracy was assessed using field validation points and manual densification points. Finally, the research analyzed the geographical environmental and biophysical factors influencing consistency. The results show that ESRI2020 had the highest overall accuracy for forest, followed by ESA2020, FROM-GLC10, and Generated_Hansen2020. Regions with elevations ranging from 200 to 3000 m and slopes below 15° or above 25° showed high spatial consistency, whereas other regions showed low consistency. Inconsistent regions showed complex landscapes heavily influenced by human activities; these regions are prone to being confused with shrubs and cropland and are also impacted by rubber and oil palm plantations, significantly affecting the accuracy of forest mapping. Based on the research findings, ESRI2020 is recommended for mountainous areas and abundant forest regions. However, in areas significantly affected by human activities, such as forest and non-forest edges and mixed areas of plantations and natural forests, caution should be taken with product selection. The research has identified areas of forest inconsistency that require attention in future forest mapping. To enhance our understanding of forest mapping and generate high-precision forest cover maps, it is recommended to incorporate multi-source data, subdivide forest types, and increase the number of sample points.

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“…With sustained and stable economic growth, the importance of rubber in a regional economy continues to increase (Rule et al, 2022). As a result of the expansion of rubber growing areas, many areas of the tropics, such as South East Asia, have turned large natural forests into monoculture rubber plantations (Li et al, 2021). For example, from 1987 to 2018, forest cover in Xishuangbanna decreased from 71% to 52%, while the total area of rubber cultivation expanded nearly six times (Xiao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Rubber-based agroforestry systems modify the soil fungal composition and function in Southwest China

Hong,

Li,

et al. 2024

Soil Ecol. Lett.

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Rubber-based agroforestry systems increased the complexity of fungal networks.• Fungal community structure was strongly correlated with soil pH and SOC.• Rubber-based agroforestry systems reduced the presence of certain pathogens. Rubber-based agroforestry systems have been recognized as a practical and sustainable solution to promote the development of agriculture and the environment. However, interactions between fungal communities and these systems are still not sufficiently investigated. In this study, we compared the abundance, diversity, and community composition of soil fungi in four treatments, including rubber monoculture and three rubber-based agroforestry treatments involving intercropping with Camellia sinensis, Coffea liberica, and Theobroma cacao. The results revealed that the community composition exhibited significant variation between the four different treatments, while the overall soil α-diversity was relatively stable across all treatments. Soil pH and soil organic carbon were significantly related to the structure of the fungal community. In particular, the complexity of the functional fungal network increased in response to agroforestry treatments, promoting beneficial fungi and suppressing certain plant pathogens. These results suggest that rubber-based agroforestry systems can promote the health of soil microbial community composition, and therefore provide an effective approach to enhancing soil quality.

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Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine

Cited by 8 publications

References 63 publications

Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province

Dry season temperature and rainy season precipitation significantly affect the spatio-temporal pattern of rubber plantation phenology in Yunnan province

A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data

Rubber-based agroforestry systems modify the soil fungal composition and function in Southwest China

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