Gridded livestock density database and spatial trends for Kazakhstan

Kolluru, Venkatesh; John, Ranjeet; Saraf, Sakshi; Chen, Jiquan; Hankerson, Brett; Robinson, Sarah; Kussainova, Maira; Jain, Khushboo

doi:10.1038/s41597-023-02736-5

Cited by 10 publications

(5 citation statements)

References 98 publications

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“…Out of the 40 feature variables, 16 features have importance rankings of 10% or higher in the classification. Of course, like Kolluru V and others, we can also demonstrate how each variable helps predict the garlic distribution by plotting response curves [19]. Please refer to Supplementary Figure S1 for the variable importance for other years.…”

Section: Feature Selection Analysismentioning

confidence: 60%

Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine

Li,

Pan,

Peng

et al. 2024

Agronomy

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Garlic (Allium sativum) is an important economic crop in China. In terms of using remote sensing technology to identify it, there is still room for improvement, and the high-precision classification of garlic has become an important issue. However, to the best of our knowledge, few studies have focused on garlic area mapping. Here, we propose a method for identifying garlic crops using samples and a multi-feature dataset under limited conditions. The results indicate the following: (1) In the land-use classification of the Erhai Lake Basin, the importance ranking of the characteristic bands, from high to low, is as follows: spectral features, vegetation features, texture features, and terrain features. (2) The random forest method based on feature selection demonstrates high accuracy in land-use classification within the Erhai Lake Basin in Yunnan Province. The overall classification accuracy reached 95.79%, with a Kappa coefficient of 0.95. (3) From 1999 to 2023, the expansion of garlic cultivation in the Erhai Lake Basin showed a trend of initially strengthening from north to south, which was followed by weakening. The vertical development of garlic cultivation reached saturation, showing a slow trend toward horizontal expansion between 2005 and 2018. The planting distributions in various townships in the Erhai Lake Basin gradually shifted from relatively uniform distributions to upstream development. This study utilized the Google Earth Engine (GEE) cloud computing platform and machine learning algorithms to compensate for the lack of statistical data on garlic cultivation in the Erhai Lake Basin. Moreover, it accurately, rapidly, and efficiently extracted planting information, demonstrating significant potential for practical applications.

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Section: Feature Selection Analysismentioning

confidence: 60%

Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine

Li,

Pan,

Peng

et al. 2024

Agronomy

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“…The simulation results for livestock distribution in this study are consistent with the trend reported by Saizen 9 and roughly depict the areas where sheep & goats, cattle, and horses congregate in the basin, highlighting the areas with favorable conditions for livestock survival. The principal livestock of the Selenge River Basin including sheep & goats, cattle, and horses, exhibit varying distributions across certain provinces, showing a spatial clustering state, demonstrably representing genuine spatial dependence patterns inherent in livestock distribution 21 . They were mainly distributed in Khuvsgul, eastern Darkhan, and the middle-lower parts of the Khangai region upstream of the basin.…”

Section: Discussionmentioning

confidence: 99%

“…Slope and aspect were chosen because the natural environment drives the distribution of livestock mainly through the topography, and livestock tend to be distributed in plains and lower terrain areas 13 . To some extent, NPP and NDVI reflect the availability of feed biomass, which has geospatial heterogeneity and is also an important indicator of the geographical spatial distribution of livestock 21 . Precipitation and land surface temperature can reflect climatic differences in different regions.…”

Section: Methodsmentioning

confidence: 99%

“…Li et al established the relationship between various environmental factors (such as land, terrain, climate, and socioeconomic factors) and livestock density using the RF, and constructed a spatial distribution map of cattle and sheep in western China at a 1 km resolution 20 . Some scholars have realized the acquisition of livestock density gridded datasets in Qinghai-Tibet Plateau and Kazakhstan based on the RF, which can bridge the gap in the dynamic monitoring of the spatial distribution of livestock density in the region over a long period 21 , 22 . Cheng et al used RF to downgrade reared pigs from the administrative level to 30 × 30 arcsec (about 131 km) resolution 23 .…”

Section: Introductionmentioning

confidence: 99%

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Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Liu,

Wang,

Yang

et al. 2024

Sci Rep

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Mapping dynamically distributed livestock in the vast steppe area based on statistical data collected by administrative units is very difficult as it is limited by the quality of statistical data and local geographical environment factors. While, spatial mapping of livestock gridded data is critical and necessary for animal husbandry management, which can be easily integrated and analyzed with other natural environment data. Facing this challenge, this study introduces a spatialization method using random forest (RF) in the Selenge River Basin, which is the main animal husbandry region in Mongolia. A spatialized model was constructed based on the RF to obtain high-resolution gridded distribution data of total livestock, sheep & goats, cattle, and horses. The contribution of factors influencing the spatial distribution of livestock was quantitatively analyzed. The predicted results showed that (1) it has high livestock densities in the southwestern regions and low in the northern regions of the Selenge River Basin; (2) the sheep & goats density was mainly concentrated in 0–125 sheep/km2, and the high-density area was mainly distributed in Khuvsgul, Arkhangai, Bulgan and part soums of Orkhon; (3) horses and cattle density were concentrated in 0–25 head/km2, mainly distributed in the southwest and central parts of the basin, with few high-density areas. This indicates that the RF simulation results effectively depict the characteristics of Selenge River Basin. Further study supported by Geodetector showed human activity was the main driver of livestock distribution in the basin. This study is expected to provide fundamental support for the precise regulation of animal husbandry in the Mongolian Plateau or other large steppe regions worldwide.

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“…Spectral features, phenological characteristics, polarization features, and texture features were chosen for classification. Google Earth Engine (GEE) provides an "interpretability" feature for the random forest classification algorithm, enabling the evaluation of importance scores for the feature variables involved in classification [53]. In this study, feature selection was conducted to utilize only the 30 top-performing features each year for classification (Figure 4a-e).…”

Section: Generate Training Samplesmentioning

confidence: 99%

Mapping Cropland Abandonment in the Cloudy Hilly Regions Surrounding the Southwest Basin of China

Wei,

Wen,

Zhou

et al. 2024

Land

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Cropland is a vital resource intricately connected to food security. Currently, the issue of cropland abandonment poses a serious threat to food production and supply, presenting a significant challenge to rural economies and the stability of the food supply chain. The hilly and cloudy regions of southwest China are particularly affected by cropland abandonment, presenting significant challenges in accurately mapping the distribution of abandoned cropland due to fragmentation and heavy cloud pollution. Therefore, this study focuses on Mingshan County, located in Ya’an City, Sichuan Province, China, as the study area. Utilizing Google Earth Engine (GEE) and a random forest algorithm, a method integrating multi-source data from Landsat 8, Sentinel-2, and Sentinel-1 is proposed to extract abandoned cropland spanning from 2018 to 2022. This study analyzes spatial and temporal characteristics, employing the Geodetector with optimal parameters to explore the underlying mechanisms. The findings reveal the following: (1) The method achieves an overall accuracy of land use classification surpassing 88.67%, with a Kappa coefficient exceeding 0.87. Specifically, the accuracy for identifying abandoned cropland reaches 87.00%. (2) From 2018 to 2022, the abandonment rate in Mingshan County fluctuated between 4.58% and 5.77%, averaging 5.03%. The lowest abandonment rate occurred in 2019–2020, while the highest was observed in 2020–2021. (3) Cropland abandonment is influenced by both natural and social factors. Elevation and slope are the main driving factors, alongside factors such as distance to road, town, and residential settlement that all significantly contribute to abandonment trends. These five factors exhibit positive correlation with the abandonment rate, with distance to the river showing relatively weaker explanatory power.

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Gridded livestock density database and spatial trends for Kazakhstan

Cited by 10 publications

References 98 publications

Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine

Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Mapping Cropland Abandonment in the Cloudy Hilly Regions Surrounding the Southwest Basin of China

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