Classifying grass-dominated habitats from remotely sensed data: The influence of spectral resolution, acquisition time and the vegetation classification system on accuracy and thematic resolution

Bradter, Ute; O’Connell, Jerome; Kunin, William E.; Boffey, Caroline W.H.; Ellis, Richard J.; Benton, Tim G.

doi:10.1016/j.scitotenv.2019.134584

Cited by 28 publications

(27 citation statements)

References 68 publications

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“…Managing land better to maintain a high level of biodiversity will be most efficient when planning takes into account the effect of complex landscapes on ecological systems and on species distribution. Detailed maps of vegetation can facilitate environmentally and biodiversity friendly planning (Bradter et al, 2020). Current thinking in ecology and nature conservation is that land management fails to accommodate the consequences of changing environmental conditions and the fusion of species and ecological communities of the postindustrial novel ecosystem.…”

Section: Discussionmentioning

confidence: 99%

“…The applicability of the discontinuity approach is the reason why a majority of studies have focused on vegetation classification by using RS tools. Among the nonforest vegetation studies, the discontinuous approach—vegetation classification by using RS tools—has been applied for for example, grasslands (Zlinszky et al, 2014), mountain grasslands (Schweiger et al, 2017), wetlands (Schmidtlein et al, 2012), and grass‐dominated habitats (Bradter et al, 2020); vegetation plots dominated by a single species have been classified for rangeland and grasslands (Mansour, Mutanga, Adam, & Abdel‐Rahman, 2016). Broader habitat types have also been mapped (Feilhauer et al, 2014; Harris, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Use of remote sensing to track postindustrial vegetation development

et al. 2020

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The effects of natural processes on deposited mineral material of postindustrial sites is underestimated. Natural vegetation development on mineral material substratum is an unappreciated way of site management. Due to the classification‐based approach to assembly of plant community diversity, remote sensing methods have limited application. We aimed to assess whether remotely sensed data allow for building predictive models, able to recognise vegetation variability along the main gradients of species composition. We assessed vegetation in 321 study plots on four coal‐mine spoil heaps in Silesia (S Poland). We determined the main gradients of species composition using detrended correspondence analysis (DCA), and we identified how DCA scores describe vegetation variability. DCA axes explained 38.5%, 35.4%, 31.4%, and 20.1% of species composition variability. We built machine learning models of DCA scores using multispectral satellite images and airborne laser scanning data as predictors. We obtained good predictive power of models for the first two DCA axes (R2 = 0.393 and 0.443, root mean square errors, RMSE = 0.571 and 0.526) and low power for the third and fourth DCA axes (R2 = 0.216 and 0.064, RMSE = 0.513 and 0.361). These scores allowed us to prepare a vegetation map based on DCA scores, and distinguish meadow‐like from forest‐edge‐like vegetation, and to identify thermophilous and highly productive vegetation patches. Our approach allowed us to account for species composition gradients, which improved remote sensing‐based vegetation surveys. This method may be used for planning future management.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of remote sensing to track postindustrial vegetation development

et al. 2020

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“…Kebutuhan pengukuran dan pemetaan bidang tanah di Indonesia masih sangat tinggi dimana masih banyak bidang-bidang tanah yang belum terpetakan (Africa et al, 2020;Bradter et al, 2019;Brenner et al, 2012). Sejak tahun 2016 pemerintah melaksanakan percepatan pendaftaran tanah sistematik lengkap hingga pada tahun 2025 seluruh bidang tanah di Indonesia telah terdaftar.…”

Section: Pemetaan Bidang Tanahunclassified

“…Sedangkan pemetaan adalah proses, cara, perbuatan membuat peta. Pemetaan bidang tanah merupakan bagian dari pendaftaran tanah secara sistematis lengkap (Bradter et al, 2019;Brenner et al, 2012;Brown et al, 2013).…”

Section: Pemetaan Bidang Tanahunclassified

Pemanfaatan Drone dalam Pemetaan Kontur Tanah

Andrew¹

2020

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Sejak tahun 2016 pemerintah melaksanakan percepatan pendaftaran tanah sistematik lengkap hingga pada tahun 2025 seluruh bidang tanah di Indonesia telah terdaftar. Pada tahun 2018 saja pemerintah menargetkan 7 juta bidang, dan tahun 2019 menargetkan 9 juta. Untuk mencapai target tersebut diperlukan teknologi yang bisa mengatasi hal tersebut. Drone, yang lebih dikenal Unmanned Aerial Vehicle (UAV) atau teknologi pesawat tanpa awak sebagai solusi untuk pemetaan tanah dengan target yang banyak, feksibilitas waktu dan areal pemotretan yang diinginkan, dan hasil pemotretan resolusi spasial yang detail serta biaya yang relatif lebih murah dibandingkan harga perekaman dengan satelit. Penggunaan drone untuk percepatan pemetaan tanah adalah pilihan yang tepat karena hasil pemotretan drone punya resolusi spasial yang tinggi sehingga sesuai dengan aturan pemetaan bidang tanah dan harganya murah.

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“…Percentage of ground covered by each vascular plant species recorded in sample areas of 4 m 2 are supplied for vegetation categories recorded with the ASD Field Spec Pro (Table 22) and the SVC HR-1024i (Table 23). The data were used to study the spectral separability of vegetation categories depending on the month in which the spectra were recorded [4].…”

Section: Datamentioning

confidence: 99%

Field spectroscopy data from non-arable, grass-dominated objects in an intensively used agricultural landscape in East Anglia, UK

et al. 2020

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Remote sensing of vegetation provides important information for ecological applications and environmental assessments. The association between vegetation composition and structure with its spectral signal can most fully be assessed with hyperspectral data. Particularly field spectroscopy data can improve such understanding as the spectral data can be linked with the vegetation under consideration without the geographic registration uncertainties of aerial or satellite imagery. The data provided in this article contain field spectroscopy measurements from non-arable, grass-dominated objects on four farms in an intensively used agricultural landscape in the South-East of the UK. Detailed data on the plant species composition of the objects are also supplied with this article to support further analysis. Reuse potential includes linking the vegetation data with the spectral response using spectral unmixing techniques to map certain plant species or including the field spectroscopy data in a larger study with data from a wider area. This data article is related to the paper ‘Classifying grass-dominated habitats from remotely sensed data: the influence of spectral resolution, acquisition time and the vegetation classification system on accuracy and thematic resolution’ (Bradter et al., 2019) in which the ability to classify the recorded vegetation from the field spectroscopy data was analysed.

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Classifying grass-dominated habitats from remotely sensed data: The influence of spectral resolution, acquisition time and the vegetation classification system on accuracy and thematic resolution

Cited by 28 publications

References 68 publications

Use of remote sensing to track postindustrial vegetation development

Use of remote sensing to track postindustrial vegetation development

Pemanfaatan Drone dalam Pemetaan Kontur Tanah

Field spectroscopy data from non-arable, grass-dominated objects in an intensively used agricultural landscape in East Anglia, UK

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