Application of the MODIS MOD 17 Net Primary Production product in grassland carrying capacity assessment

Leeuw, Jan de; Rizayeva, Afag; Namazov, Elmaddin; Bayramov, Emil; Marshall, Michael; Etzold, Jonathan; Neudert, Regina

doi:10.1016/j.jag.2018.09.014

Cited by 41 publications

(28 citation statements)

References 37 publications

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“…NPP has been used previously to study desertification linked to climate change 65 , 66 . Here, we use MODIS monthly NPP data (2001–2015) following the procedure outlined by 67 .…”

Section: Methodsmentioning

confidence: 99%

Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices

Dameneh

Gholami

Telfer

et al. 2021

Sci Rep

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Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Here, we identify and analyze desertification trends in Iran for the period 2001–2015 via a combination of three indices for vegetation (NPP—net primary production, NDVI—normalized difference vegetation index, LAI—leaf area index) and two climate indices (LST—land surface temperature, P—precipitation). We combine these indices to identify and map areas of Iran that are susceptible to land degradation. We then apply a simple linear regression method, the Mann–Kendall non-parametric test, and the Theil–Sen estimator to identify long-term temporal and spatial trends within the data. Based on desertification map, we find that 68% of Iran shows a high to very high susceptibility to desertification, representing an area of 1.1 million km2 (excluding 0.42 million km2 classified as unvegetated). Our results highlight the importance of scale in assessments of desertification, and the value of high-resolution data, in particular. Annually, no significant change is evident within any of the five indices, but significant changes (some positive, some negative) become apparent on a seasonal basis. Some observations follow expectations; for instance, NDVI is strongly associated with cooler, wet spring and summer seasons, and milder winters. Others require more explanation; for instance, vegetation appears decoupled from climatic forcing during autumn. Spatially, too, there is much local and regional variation, which is lost when the data are considered only at the largest nationwide scale. We identify a northwest–southeast belt spanning central Iran, which has experienced significant vegetation decline (2001–2015). We tentatively link this belt of land degradation with intensified agriculture in the hinterlands of Iran’s major cities. The spatial and temporal trends identified with the three vegetation and two climate indices afford a cost-effective framework for the prediction and management of future environmental trends in developing regions at risk of desertification.

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“…NPP has been used previously to study desertification linked to climate change 65 , 66 . Here, we use MODIS monthly NPP data (2001–2015) following the procedure outlined by 67 .…”

Section: Methodsmentioning

confidence: 99%

Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices

Dameneh

Gholami

Telfer

et al. 2021

Sci Rep

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“…Description Download site Land use MODIS Land Cover Type Yearly L3 Global 500 m SIN Grid product-MCD12Q1 [2] https://lpdaac.usgs. [3] https://ecocast.arc.nasa.g ov/data/pub/gimms/3g.v1/ https://lpdaac.usgs.gov/pr oducts/mod13a3v006/ NPP MODIS NPP product [4] https://lpdaac.usgs.gov/pr oducts/mod17a3v055/ GRACE GRACE Monthly Mass Grids -Land [5] https://grace.jpl.nasa.gov/ data/get-data/monthlymass-grids-land/…”

Section: Data Typementioning

confidence: 99%

Spatial-temporal Variations in Vegetation and their Driving Factors in the Amur River Basin

Zhou¹,

Zhang²

2019

dteees

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“…To better cope with the complex characteristics of grassland ecosystem dynamics and uncertainty, Yu et al [79] used slope, distance from water sources, and soil erosion intensity as limiting factors to formulate grassland use strategies and adjust the theoretical livestock carrying capacity. De Leeuw et al [80] introduced a slope factor to further limit the amount of available grass yield on a slope greater than 10% when developing a grassland use strategy, thereby preventing the occurrence of soil erosion on a steep slope. The existing grassland use strategy focuses on the maximum available grassland yield and less on determining the sustainable available grassland yield from the current state of the grassland ecosystem.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Grassland Carrying Capacity by Applying High Spatiotemporal Remote Sensing Techniques in Zhenglan Banner, Inner Mongolia, China

Qin

Sun

et al. 2021

Sustainability

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Overgrazing directly leads to grassland degradation, which is a serious constraint to the sustainable development of animal husbandry. In drylands, grassland biomass is highly heterogeneous in space and time. It is difficult to achieve sustainable utilization of grassland resources by focusing only on the average annual carrying capacity assessment obtained from grass yield. Here, we proposed a novel approach for assessing grassland carrying capacity, taking Zhenglan Banner (County) in Inner Mongolia as the study area. First, monthly grass yield at 30 m spatial resolution was estimated, derived from Carnegie–Ames–Stanford Approach (CASA) model and spatial and temporal adaptive reflectance fusion model (STARFM). Then, based on the degree of sand mobility and degradation condition of typical steppe, the utilization patterns for sandy land and typical steppe in different grazing seasons were developed separately to obtain available grass yield. Finally, the carrying capacity at the Gacha (Village)-scale was estimated and the current livestock carrying status was evaluated to facilitate the grassland refined management. In Zhenglan Banner, the carrying capacity was 237.46 thousand cattle-units in summer. The grassland resources are being overgrazed, with an overload rate of 19.32%. At Gacha-scale, the maximum reasonable stock density was ranged from 0.06 cattle-unit/ha to 0.42 cattle-unit/ha. Fifty-one Gachas exhibited livestock overload. This study is expected to provide technical support and scientific reference data for ecological conservation and grassland management in the study area, as well as in dryland pastoral areas of northern China.

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Application of the MODIS MOD 17 Net Primary Production product in grassland carrying capacity assessment

Cited by 41 publications

References 37 publications

Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices

Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices

Spatial-temporal Variations in Vegetation and their Driving Factors in the Amur River Basin

Estimation of Grassland Carrying Capacity by Applying High Spatiotemporal Remote Sensing Techniques in Zhenglan Banner, Inner Mongolia, China

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