Integrating satellite images and topographic data for mapping seasonal grazing management units in pastoral landscapes of eastern Africa

Shibia, M.; Röder, Achim; Fava, Francesco; Stellmes, Marion; Hill, Joachim

doi:10.1016/j.jaridenv.2021.104661

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…E1), which aligned with fPAR changes shown in SI: Appendix F. The fPAR values are often used to represent vegetation greenness (Forkel et al, 2014;Twine & Kucharik, 2008). In this context, rangeland greenness can be influenced both by environmental conditions and management practices (Browning et al, 2019;Long et al, 2019;Shibia et al, 2022).…”

Section: Factors Driving Regional Rangeland C Dynamicsmentioning

confidence: 68%

Coupling Remote Sensing with a Process Model for the Simulation of Rangeland Carbon Dynamics

Xia,

Sanderman,

Watts

et al. 2024

Preprint

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Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics, as well as limited data availability. We developed a Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable datasets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux datasets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern U.S. rangelands, to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE < 390 g C m-2) than net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE < 180 g C m-2), and captured the spatial variability of surface SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Our RCTM simulations indicated slightly enhanced SOC stocks during the past decade, which is mainly driven by an increase in precipitation. Regression analysis identified slope, soil texture, and climate factors as the main controls on model-predicted C sequestration rate. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of rangeland vegetation biomass, C fluxes, and SOC stocks.

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Section: Factors Driving Regional Rangeland C Dynamicsmentioning

confidence: 68%

Coupling Remote Sensing with a Process Model for the Simulation of Rangeland Carbon Dynamics

Xia,

Sanderman,

Watts

et al. 2024

Preprint

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“…Satellite observations and the Google Earth Engine (GEE) platform provide an opportunity to map the spatiotemporal patterns of GI at local, regional, and global scales using long-term multi-source remote sensing archive data and state-of-the-art cloud-computing capability 10 . Moreover, it is promising to monitor the GI considering the consequences on grasslands by satellite techniques based on the characteristics of vegetation dynamics 11 . In grassland ecosystems, the feeding behaviors of herbivores directly influence the vegetation states 12 , 13 .…”

Section: Background and Summarymentioning

confidence: 99%

A 10-m annual grazing intensity dataset in 2015–2021 for the largest temperate meadow steppe in China

Chang,

Wang,

Zhao

et al. 2024

Sci Data

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Mapping grazing intensity (GI) using satellites is crucial for developing adaptive utilization strategies according to grassland conditions. Here we developed a monitoring framework based on a paired sampling strategy and the classification probability of random forest algorithm to produce annual grazing probability (GP) and GI maps at 10-m spatial resolution from 2015 to 2021 for the largest temperate meadow in China (Hulun Buir grasslands), by harmonized Landsat 7/8 and Sentinel-2 images. The GP maps used values of 0–1 to present detailed grazing gradient information. To match widely used grazing gradients, annual GI maps with ungrazed, moderately grazed, and heavily grazed levels were generated from the GP dataset with a decision tree. The GI maps for 2015–2021 had an overall accuracy of more than 0.97 having significant correlations with the statistical data at city (r = 0.51) and county (r = 0.75) scales. They also effectively captured the GI gradients at site scale (r = 0.94). Our study proposed a monitoring approach and presented annual 10-m grazing information maps for sustainable grassland management.

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“…For a detailed description of the theoretic and mathematical constructs of the area under class accuracy assessment method, readers are referred to Olofsson et al [69] and Card [70]. This method is widely accepted and has been used by several other studies [29,71,72]. A class-wise accuracy metric was subsequently calculated using F1-score, which leverages the importance of both the precision (UA) and recall (PA) in a single fused accuracy measure that ranges between 0 and 100% [73].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango (Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe

et al. 2022

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Generating tree-specific crop maps within heterogeneous landscapes requires imagery of fine spatial and temporal resolutions to discriminate among the rapid transitions in tree phenological and spectral features. The availability of freely accessible satellite data of relatively high spatial and temporal resolutions offers an unprecedented opportunity for wide-area land use and land cover (LULC) mapping, including tree crop (e.g., mango; Mangifera indica L.) detection. We evaluated the utility of combining Sentinel-1 (S1) and Sentinel-2 (S2) derived variables (n = 81) for mapping mango orchard occurrence in Zimbabwe using machine learning classifiers, i.e., support vector machine and random forest. Field data were collected on mango orchards and other LULC classes. Fewer variables were selected from ‘All’ combined S1 and S2 variables using three commonly utilized variable selection methods, i.e., relief filter, guided regularized random forest, and variance inflation factor. Several classification experiments (n = 8) were conducted using 60% of field datasets and combinations of ‘All’ and fewer selected variables and were compared using the remaining 40% of the field dataset and the area underclass approach. The results showed that a combination of random forest and relief filter selected variables outperformed (F1 score > 70%) all other variable combination experiments. Notwithstanding, the differences among the mapping results were not significant (p ≤ 0.05). Specifically, the mapping accuracy of the mango orchards was more than 80% for each of the eight classification experiments. Results revealed that mango orchards occupied approximately 18% of the spatial extent of the study area. The S1 variables were constantly selected compared with the S2-derived variables across the three variable selection approaches used in this study. It is concluded that the use of multi-modal satellite imagery and robust machine learning classifiers can accurately detect mango orchards and other LULC classes in semi-arid environments. The results can be used for guiding and upscaling biological control options for managing mango insect pests such as the devastating invasive fruit fly Bactrocera dorsalis (Hendel) (Diptera: Tephritidae).

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Integrating satellite images and topographic data for mapping seasonal grazing management units in pastoral landscapes of eastern Africa

Cited by 5 publications

References 29 publications

Coupling Remote Sensing with a Process Model for the Simulation of Rangeland Carbon Dynamics

Coupling Remote Sensing with a Process Model for the Simulation of Rangeland Carbon Dynamics

A 10-m annual grazing intensity dataset in 2015–2021 for the largest temperate meadow steppe in China

Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango (Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe

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