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21 January 2025 |
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Article overview
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Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin | | Leikun Yin
; Rahul Ghosh
; Chenxi Lin
; David Hale
; Christoph Weigl
; James Obarowski
; Junxiong Zhou
; Jessica Till
; Xiaowei Jia
; Troy Mao
; Vipin Kumar
; Zhenong Jin
; | | Date: |
1 Jan 2023 | | Abstract: | Cashews are grown by over 3 million smallholders in more than 40 countries
worldwide as a principal source of income. As the third largest cashew producer
in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15%
of the country’s national export earnings. However, a lack of information on
where and how cashew trees grow across the country hinders decision-making that
could support increased cashew production and poverty alleviation. By
leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep
learning algorithms, and large-scale ground truth datasets, we successfully
produced the first national map of cashew in Benin and characterized the
expansion of cashew plantations between 2015 and 2021. In particular, we
developed a SpatioTemporal Classification with Attention (STCA) model to map
the distribution of cashew plantations, which can fully capture texture
information from discriminative time steps during a growing season. We further
developed a Clustering Augmented Self-supervised Temporal Classification
(CASTC) model to distinguish high-density versus low-density cashew plantations
by automatic feature extraction and optimized clustering. Results show that the
STCA model has an overall accuracy of 80% and the CASTC model achieved an
overall accuracy of 77.9%. We found that the cashew area in Benin has doubled
from 2015 to 2021 with 60% of new plantation development coming from cropland
or fallow land, while encroachment of cashew plantations into protected areas
has increased by 70%. Only half of cashew plantations were high-density in
2021, suggesting high potential for intensification. Our study illustrates the
power of combining high-resolution remote sensing imagery and state-of-the-art
deep learning algorithms to better understand tree crops in the heterogeneous
smallholder landscape. | | Source: | arXiv, 2301.00363 | | Services: | Forum | Review | PDF | Favorites | |
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