Appel, M., Lahn, F., Buytaert, W. & Pebesma, E. Open and scalable analytics of large earth observation datasets: From scenes to multidimensional arrays using SCIDB and GDAL. ISPRS J. Photogramm. Remote Sens. 138, 47–56 (2018).
Google Scholar
Audebert, N., Saux, B. L. & Lefvre, S. Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks. ISPRS J. Photogramm. Remote Sens. 140, 20–32 (2018).
Google Scholar
Ball J. E., Anderson D. T., & Chan C. S. Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community. J. Appl. Remote Sens. https://doi.org/10.1117/1.JRS.11.042609 (2017).
Proceedings of the Royal Society B: Biological Sciences. Vol. 282. 20141657 (2015).
Velázquez, E., Paine, C. T., May, F. & Wiegand, T. Linking trait similarity to interspecific spatial associations in a moist tropical forest. J. Veg. Sci. 26, 1068–1079 (2015).
Google Scholar
Ben-Said, M. Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review. Ecol. Process. 10, 1–23 (2021).
Google Scholar
Watt, A. S. Pattern and process in the plant community. J. Ecol. 35, 1–22 (1947).
Google Scholar
Pielou, E.C. Mathematical Ecology; Number 574.50151 P613 1977. (Wiley, 1977).
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Google Scholar
Brown, C., Law, R., Illian, J. B. & Burslem, D. F. Linking ecological processes with spatial and non-spatial patterns in plant communities. J. Ecol. 99, 1402–1414 (2011).
Google Scholar
Detto, M. & Muller-Landau, H. C. Fitting ecological process models to spatial patterns using scalewise variances and moment equations. Am. Nat. 181, E68–E82 (2013).
Google Scholar
May, F., Huth, A., & Wiegand, T. Moving beyond abundance distributions: neutral theory and spatial patterns in a tropical forest. Proceedings. Biological sciences 282(1802), 20141657. https://doi.org/10.1098/rspb.2014.1657 (2015).
Google Scholar
Kerr, J. T. & Ostrovsky, M. From space to species: Ecological applications for remote sensing. Trends Ecol. Evol. 18, 299–305 (2003).
Google Scholar
Gillespie, T. W., Foody, G. M., Rocchini, D., Giorgi, A. P. & Saatchi, S. Measuring and modelling biodiversity from space. Prog. Phys. Geogr. 32, 203–221 (2008).
Google Scholar
He, J., Zhang, L., Wang, Q. & Li, Z. Using diffusion geometric coordinates for hyperspectral imagery representation. IEEE Geosci. Remote Sens. Lett. 6(4), 767–771 (2009).
Google Scholar
Lechner, A.M., Foody, G.M., & Boyd, D.S. Applications in remote sensing to forest ecology and management. One Earth 2.5, 405–412 (2020).
Arévalo, P., Olofsson, P. & Woodcock, C. E. Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting. Remote Sens. Environ. 238, 111051 (2020).
Google Scholar
Gillespie, T.W. et al. Measuring and modelling biodiversity from space. Prog. Phys. Geogr. 32.2, 203–221 (2008).
Lausch, A., Erasmi, S., King, D. J., Magdon, P. & Heurich, M. Understanding forest health with remote sensing-part II—A review of approaches and data models. Remote Sens. 9(2), 129 (2017).
Google Scholar
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., et al. Monitoring vegetation systems in the Great Plains with ERTS. in NASA Special Publication. Vol. 351. 309 (1974).
Chen, J. M. & Black, T. Defining leaf area index for non-flat leaves. Plant Cell Environ. 15, 421–429 (1992).
Google Scholar
Zha, Y., Gao, J. & Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24, 583–594 (2003).
Google Scholar
Zhao, S. et al. Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient. Int. J. Appl. Earth Obs. Geoinf. 12, 351–358 (2010).
Google Scholar
Gao, B. C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266 (1996).
Google Scholar
Wan, Z. & Dozier, J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens. 34, 892–905 (1996).
Google Scholar
Xu, H., Wang, Y., Guan, H., Shi, T. & Hu, X. Detecting ecological changes with a remote sensing based ecological index (RSEI) produced time series and change vector analysis. Remote Sensing 11, 2345 (2019).
Google Scholar
List of Top 10 Sources of Free Remote Sensing Data (2017).
USGS Earth Explorer: Download Free Landsat Imagery (2021).
Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote. Sens. 65, 2–16 (2010).
Google Scholar
Li, M., Zang, S., Zhang, B., Li, S. & Wu, C. A review of remote sensing image classification techniques: The role of spatio-contextual information. Eur. J. Remote Sens. 47, 389–411 (2014).
Google Scholar
Gómez-Chova, L., Tuia, D., Moser, G. & Camps-Valls, G. Multimodal classification of remote sensing images: A review and future directions. Proc. IEEE 103, 1560–1584 (2015).
Google Scholar
Alajlan, N., Bazi, Y., Melgani, F. & Yager, R. R. Fusion of supervised and unsupervised learning for improved classification of hyperspectral images. Inf. Sci. 217, 39–55 (2012).
Google Scholar
Csillik, O. Fast segmentation and classification of very high resolution remote sensing data using SLIC superpixels. Remote Sens. 9, 243 (2017).
Google Scholar
Thanh Noi, P. & Kappas, M. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors 18, 18 (2018).
Google Scholar
Jiang, S., Zhao, H., Wu, W., & Tan, Q. A novel framework for remote sensing image scene classification. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2018, 42 (2018).
Baddeley, A. Spatial Point Process Modelling and Its Applications. Vol. 20. (Publicacions de la Universitat Jaume I, 2004).
Vasudevan, K., Eckel, S., Fleischer, F., Schmidt, V. & Cook, F. Statistical analysis of spatial point patterns on deep seismic reflection data: A preliminary test. Geophys. J. Int. 171, 823–840 (2007).
Google Scholar
Cheng, Y. & Luo, J. Statistical analysis of metastable pitting events on carbon steel. Br. Corros. J. 35, 125–130 (2000).
Google Scholar
Velázquez, E., Martínez, I., Getzin, S., Moloney, K. A. & Wiegand, T. An evaluation of the state of spatial point pattern analysis in ecology. Ecography 39, 1042–1055 (2016).
Google Scholar
Clark, P. J. & Evans, F. C. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35, 445–453 (1954).
Google Scholar
Stoyan, D., & Penttinen, A. Recent applications of point process methods in forestry statistics. Stat. Sci. 2000, 61–78 (2000).
Illian, J., Penttinen, A., Stoyan, H., & Stoyan, D. Statistical Analysis and Modelling of Spatial Point Patterns. Vol. 70. (Wiley, 2008).
Brodrick, P. G., Davies, A. B. & Asner, G. P. Uncovering ecological patterns with convolutional neural networks. Trends Ecol. Evol. 34, 734–745 (2019).
Google Scholar
Liu, S., Luo, H., Tu, Y., He, Z., & Li, J. Wide contextual residual network with active learning for remote sensing image classification. in IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium. 7145–7148 (IEEE, 2018).
Lee, H. & Kwon, H. Going deeper with contextual CNN for hyperspectral image classification. IEEE Trans. Image Process. 26, 4843–4855 (2017).
Google Scholar
Cheng, G., Xie, X., Han, J., Guo, L. & Xia, G. S. Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 3735–3756 (2020).
Google Scholar
Lewy, D., & Mandziuk, J. An overview of mixing augmentation methods and augmentation strategies. arXiv preprint arXiv:2107.09887 (2021).
Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q.V. Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018).
Naveed, H. Survey: Image mixing and deleting for data augmentation. arXiv preprint arXiv:2106.07085 (2021).
Freeman, I., Roese-Koerner, L. & Kummert, A. Effnet: An efficient structure for convolutional neural networks. 25th IEEE international conference on image processing (ICIP). IEEE 2018, 6–10 (2018).
LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989).
Google Scholar
Raeisi, M., Bonneu, F. & Gabriel, E. A spatio-temporal multi-scale model for Geyer saturation point process: Application to forest fire occurrences. Spatial Stat. 41, 100492 (2021).
Google Scholar
Baddeley, A. Analysing spatial point patterns in R. in Workshop Notes Version. Vol. 3 (2008).
Source: Ecology - nature.com