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Land Suitability Evaluation and Drought Stress Assessment of Tea Estates Using Satellite Remote Sensing-based Multi-Criteria Decision Support System

ANIMESH, CHANDRA DAS 筑波大学 DOI:10.15068/0002002140

2021.12.03

概要

Land suitability evaluation aims at finding suitable lands in order to optimize the land resource planning and management and drought stress assessment provides guidance to minimize the climate change effects for increasing tea production. Bangladesh is considered one of the major tea producing countries in the world. However, land under tea cultivation in Bangladesh has not been increased significantly to increase production. In addition, adverse global climate change effects, especially, the reduction in rainfall that results in drought have been challenging the optimal tea production in this country. Therefore, it is crucial to identify the suitability of the existing cultivable lands and assessing the drought stress in order to foster better land use decisions and climate change-focused tea production strategies in Bangladesh. However, adequate information is unavailable in the existing literature regarding land suitability evaluation and drought stress assessment for tea production in Bangladesh. Therefore, this study focused on developing a land suitability model for increasing tea production using multi-criteria decision support systems and assessing the drought severity including drought classification for tea estates in Bangladesh.

To meet the research objectives, the land suitability evaluation followed by subsequent validation of suitability classes using time-series yield information was performed to classify lands as well as to select the suitable lands for sustainable tea production in the north-eastern part of Bangladesh. According to the review of literature and field survey, twelve criteria were selected for the land suitability evaluation. The AHP was used as a MCA technique to incorporate expert’s opinion for prioritizing the criteria. The study area was classified into four categories- “Highly suitable”, “Moderately suitable”, “Marginally suitable” and “Not suitable”. This research utilized the phenological datasets of remote sensing, geospatial datasets of soil-plant bio-physical properties, and expert’s opinion. The Sentinel-2 satellite images were processed to obtain the layers for the Land Use and Land Cover (LULC) and normalized difference vegetation index (NDVI). Data of Shuttle Radar Topography Mission (SRTM) were used to generate the elevation layer. The vector layers of edaphic, climatic, topographic, and accessibility parameters were processed in the ArcGIS 10.7.1® software for respective raster layers. Finally, suitability classes were determined using spatial analysis of the reclassified raster layers along with the consideration of result of multi-criteria analysis. Results of this study showed that 41,460 hectares lands (3.37 % of the total lands) were under highly suitable class. The proportion of moderately suitable, marginally suitable and not-suitable classes of lands for tea cultivation in the study area were 9.01 %, 49.87 %, and 37.75%, respectively. Thirty-one tea estates were in highly suitable areas, 79 were in moderately suitable, 24 in marginally suitable, and only one in a not suitable area. There are two important vegetative parameters for yield estimation in tea viz. NDVI and LAI were selected for the validation of suitability classes. The results of yield estimation using time-series yield information shows the NDVI (R2 = 0.69, 0.66, and 0.67), and LAI (R2 = 0.68, 0.65, and 0.63) for 2017, 2018, and 2019, respectively.

The aim of the study on the assessment of drought stress for tea estates was to measure the drought severity in tea plantation areas using optical and thermal remote sensing technology with the Standardized Precipitation Index (SPI). To calculate the SPI, rainfall data for the Sylhet and Sreemangal station was gathered from the Bangladesh Meteorological Department (BMD). Landsat 8 OLI/TIRS images were processed to develop the maps for Land Surface Temperature (LST), and Soil Moisture Index (SMI). The Normalized Difference Moisture Index (NDMI) maps were developed from the Sentinel 2 satellite images. The drought frequency was calculated from the classification of droughts utilizing SPI. The results of the study demonstrated that the drought frequencies for the Sylhet station was 38.46% for near normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station showed the frequencies: 28.21%, 41.02%, and 30.77%, for near normal, normal, and moderately dry months, respectively. The correlation coefficient between the SMI and NDMI were observed as 0.84, 0.77, and 0.79 for the drought period of 2018-2019, 2019-2020 and 2020-2021, respectively, indicates a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought incidence in tea estates demonstrated that 61%, 60%, and 60% of estates in the study area provides lower yield than the observed yield during drought, which accounted for 7.72%, 11.92% and 12.52% yield loss in 2018, 2019, and 2020, respectively.

In conclusion, geospatial datasets have been proved to be a reliable source for land suitability evaluation with validation of suitability classes as well as drought stress assessment for tea plantation. This study suggests that the remote sensing technology with the multi-criteria decision support system could be used by scientists, land policy makers, and agricultural land use planners to select suitable lands as well as to measure the magnitude of drought stress in tea estates for increasing tea production

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