Unmanned aerial systems accurately map rangeland condition indicators in a dryland savannah

Amputu, Vistorina; Braun, Andreas; Heshmati, Sara; Retzlaff, Rebecca; Röder, Achim; Knox, Nichola; Tielbӧrger, Katja

doi:10.1016/j.ecoinf.2023.102007

Cited by 4 publications

(10 citation statements)

References 46 publications

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“…The reflectance was radiometrically corrected with the aid of an integrated light sensor and a calibrated panel that was captured before and after each flight. The quadcopter was flown at 80 m above the ground to provide data with a ground sampling distance of 5.6 cm/pixel, a suitable resolution for estimating the sparsely distributed and relatively small-sized herbaceous plants in the study system [7,31]. Ground control point (GCP) targets were placed in each of the four corners of all plots during drone flights for later identification to directly overlay the drone and field information.…”

Section: Data Collectionmentioning

confidence: 99%

“…After conducting the drone flights, we collected field data from the nine plots along the transects (Table 1) with the perspective of the drone (i.e., considering only the top of the canopy). Within each plot (100 m 2 ), we estimated the percentage cover of herbaceous plants, woody plants, and bare ground, hereafter collectively termed rangeland functional attributes (RFA) as described in [7]. Additionally, we harvested herbaceous biomass in 1 m × 5 m subplots adjacent to 100 m 2 .…”

Section: Data Collectionmentioning

confidence: 99%

“…These data served as the ground-truth information to provide a reliable reference for the predicted estimates derived from the drone imagery. A detailed account of the data acquisition can be found in [7].…”

Section: Data Collectionmentioning

confidence: 99%

“…It provides reliable and timely estimates of key forage resources such as aboveground biomass [2][3][4] and vegetation cover [5,6]. However, calibrating drone-based models with field measurements, required for model fitting to ensure unbiased predictions, is often costly and time-consuming [5,7]. Rather, it would be desirable to know whether drone-based models calibrated with field data collected in a particular area, and at a certain time point (hereafter, also referred to as a scene), can be transferred to other areas or points in time [8].…”

Section: Introductionmentioning

confidence: 99%

“…While the concept of generality is commonly investigated for ecological models [8,9,11], its relevance should also be recognized in the context of models developed from remote sensing data. Despite the increasing popularity of drone technology for rangeland applications over the past two decades [1,7,12,13], testing the transferability of derived models remains underexplored. Conducting such assessments holds immense potential for optimizing landscape-scale monitoring and, thus, minimizing the degradation of these valuable resources [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Spatio-Temporal Transferability of Drone-Based Models to Predict Forage Supply in Drier Rangelands

Amputu,

Männer,

Tielbörger

et al. 2024

Remote Sensing

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Unmanned aerial systems offer a cost-effective and reproducible method for monitoring natural resources in expansive areas. But the transferability of developed models, which are often based on single snapshots, is rarely tested. This is particularly relevant in rangelands where forage resources are inherently patchy in space and time, which may limit model transfer. Here, we investigated the accuracy of drone-based models in estimating key proxies of forage provision across two land tenure systems and between two periods of the growing season in semi-arid rangelands. We tested case-specific models and a landscape model, with the expectation that the landscape model performs better than the case-specific models as it captures the highest variability expected in the rangeland system. The landscape model did achieve the lowest error when predicting herbaceous biomass and predicted land cover with better or similar accuracy to the case-specific models. This reinforces the importance of incorporating the widest variation of conditions in predictive models. This study contributes to understanding model transferability in drier rangeland systems characterized by spatial and temporal heterogeneity. By advancing the integration of drone technology for accurate monitoring of such dynamic ecosystems, this research contributes to sustainable rangeland management practices.

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Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spatio-Temporal Transferability of Drone-Based Models to Predict Forage Supply in Drier Rangelands

Amputu,

Männer,

Tielbörger

et al. 2024

Remote Sensing

Self Cite

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Connecting the Dots: How Ecohydrological Connectivity Can Support Remote Sensing and Modeling to Inform Management of Woody Plant Encroachment

Schmidt,

Osorio Leyton,

Popescu

et al. 2024

Rangeland Ecology & Management

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Spectral imaging of grass species in arid ecosystems of Namibia

Bantelmann,

Wyss,

Pius

et al. 2024

Front. Remote Sens.

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Grasslands across the African continent are under pressure from climate change and human activities, particularly in arid ecosystems. From a remote sensing perspective, these ecosystems have not received much scientific attention, especially in Namibia. To address this knowledge gap, various remote sensing methods were implemented using new generation spaceborne imaging spectrometers amongst others. Therefore, this research provides a first methodological approach aimed at mapping and evaluating the distribution of grasslands within two private nature reserves, namely, the NamibRand Nature Reserve (NRNR) and ProNamib Nature Reserve (PNNR) with surrounding farmlands on the edge of Namib Sand Sea. The multi-sensor approach utilizes Mixture Tuned Matched Filtering (MTMF) and incorporated spectral information collected in the field to analyze grasslands. The research involves a sensor comparison of multispectral Sentinel-2 and PlanetScope data, hyperspectral data from Environmental Mapping and Analysis Programme (EnMAP) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) and an additional data fusion product derived from Sentinel-2 and EnMAP imagery based on a Smoothing Filter-based Intensity Modulation Hypersharpening method (SFIM-HS). Additionally, a unique spectral library of collected field spectra was established and inter-species spectral separability and intra-species spectral homogeneity was analyzed. This library presents newly published spectra of individual species. Due to dry initial conditions, the calculated spectral separability of individual grasses is limited, making only a mean endmember feasible for partial unmixing. The validation results of satellite comparison show that data fusion products (R2 = 0.51 with Normalized Difference Vegetation Index (NDVI); R2 = 0.66 with Soil Adjusted Vegetation Index (SAVI)) are more suitable for mapping arid grasslands than multispectral or hyperspectral data (all R2 < 0.35). More research is required and potential methodological adjustments are discussed to further investigate the spatio-temporal dynamics of arid grasslands and to aid conservation efforts in the Greater Sossusvlei-Namib Landscape in line with the United Nations Decade of Restoration.

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Unmanned aerial systems accurately map rangeland condition indicators in a dryland savannah

Cited by 4 publications

References 46 publications

Spatio-Temporal Transferability of Drone-Based Models to Predict Forage Supply in Drier Rangelands

Spatio-Temporal Transferability of Drone-Based Models to Predict Forage Supply in Drier Rangelands

Connecting the Dots: How Ecohydrological Connectivity Can Support Remote Sensing and Modeling to Inform Management of Woody Plant Encroachment

Spectral imaging of grass species in arid ecosystems of Namibia

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