Airborne imaging spectroscopy for assessing land-use effect on soil quality in drylands

Levi, Nathan; Karnieli, Arnon; Paz‐Kagan, Tarin

doi:10.1016/j.isprsjprs.2022.01.018

Cited by 13 publications

(1 citation statement)

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“…These impacts have been exacerbated by the dwindling availability of arable land, which is vital for feeding the rapidly growing global population (Levi et al 2022) and also the unprecedented conversion of natural land covers into various land uses (Aneseyee et al 2020). Therefore, it is imperative to prioritize soil conservation as the cornerstone of our land management strategies to ensure the resilience and proper functioning of terrestrial ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Indexing and segment-level mapping of soil quality in a spatially complex watershed in northern Iran

Aghalari,

Chavoshi,

Borujeni

2023

Preprint

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Soil quality (SQ) modeling and mapping is a leading research field aiming to provide reproducible and cost-effective yet accurate SQ predictions at the landscape level. This endeavor was conducted in a complex watershed in northern Iran. We classified the region into spectrally and topographically homogenous land units (average area of 48 ± 23 ha) using object-based segmentation analysis. Following the physicochemical analysis of soil samples from 98 stations, the Nemoro soil quality index (SQIn) was produced using the minimum dataset procedure and a non-linear sigmoid scoring function. SQIn values averaged 0.21 ± 0.06 and differed statistically between major land uses. To predict and map SQIn for each land unit, the best-performing regression model (F(3, 84) = 45.57, p = 0.00, R2 = 0.617) was built based on the positive contribution of the mean Landsat 8-OLI band-5, and negative influence of land surface temperature retrieved from Landsat 8-OLI band 10 and surface slope (T-test p-values < 0.01). Results showed that dense-canopy woodlands located in low-slope land units exhibit higher SQIn while regions characterized by either low-vegetation or steep-sloped land units had SQ deficits. This study provides insights into SQ prediction and mapping across spatially complex large-scale landscapes.

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Section: Introductionmentioning

confidence: 99%