[en] Accurate calibration data remains a major constraint in ecological remote sensing, particularly in arid ecosystems where sparse and heterogeneous woody vegetation is challenging to detect. Despite advances in sensor technology and modelling algorithms, optimal calibration sampling design has received limited attention, with current approaches varying unsystematically (35–1,000 plots/1,000 km²) without empirical benchmarks. Here, we develop and evaluate a quantitative framework for optimizing sampling strategies in remote-sensing models of woody cover through systematic comparison of (i) calibration data source (field surveys versus photointerpretation), (ii) spatial configuration (clustered versus dispersed), and (iii) sampling density. This integrated approach enables isolating the relative contributions of each design component to mapping accuracy—a critical gap in current remote sensing methodology.
We applied this framework to Sentinel-1, Sentinel-2, combined Sentinel-1 + 2, and AlphaEarth Foundations across Madagascar's arid southwest, validating predictions through spatial cross-validation using independent field plots. Photointerpretation substantially outperformed field-based calibration under typical arid-zone constraints (R² = 0.88, RMSE = 0.11 versus R² = 0.46–0.66, RMSE = 0.17–0.21). Performance saturated at 20.7–41.4 dispersed calibration plots per 1,000 km² across all predictors, beyond which gains became marginal. Dispersed strategies required half as many samples as clustered designs to achieve comparable accuracy, demonstrating that spatial distribution outweighs sample size. Once adequate sampling density and distribution were achieved, Sentinel-1 + 2 and AlphaEarth performed similarly (R² ≈ 0.85–0.86), indicating that sampling design outweighs predictor complexity.
Our framework provides empirically derived operational thresholds (minimum: 15.5 plots/1,000 km²; optimal: 20.7–41.4 plots/1,000 km²) and a transferable methodology for determining optimal calibration densities in heterogeneous ecosystems where logistical constraints limit field sampling.
Disciplines :
Environmental sciences & ecology
Author, co-author :
Ramalason, Felana Nantenaina ; Université de Liège - ULiège > TERRA Research Centre ; Université d'Antananarivo - Ecole Supérieure des Sciences Agronomiques (ESSA-forêts), Mention Foresterie et Environnement, Ankatso
Rakotondrasoa, Olivia Lovanirina ; Université d'Antananarivo - Ecole Supérieure des Sciences Agronomiques (ESSA-forêts), Mention Foresterie et Environnement, Ankatso ; Ecole Doctorale Gestion des Ressources Naturelles et Développement (ED GRND), Université d'Antananarivo, Ankatso
Vander Linden, Arthur ; Université de Liège - ULiège > Département GxABT > Biodiversité, Ecosystème et Paysage (BEP)
Renard, Guillaume ; Université de Liège - ULiège > TERRA Research Centre
Randriamalala, Josoa R. ; Université d'Antananarivo - Ecole Supérieure des Sciences Agronomiques (ESSA-forêts), Mention Foresterie et Environnement, Ankatso ; Ecole Doctorale Gestion des Ressources Naturelles et Développement (ED GRND), Université d'Antananarivo, Ankatso
Vereecken, Nicolas J. ; Université libre de Bruxelles (ULB)- Agroecology Lab
Razakamiaramanana, Aina; Université d'Antananarivo - Ecole Supérieure des Sciences Agronomiques (ESSA-forêts), Mention Foresterie et Environnement, Ankatso ; Ecole Doctorale Gestion des Ressources Naturelles et Développement (ED GRND), Université d'Antananarivo, Ankatso
Ranaivoharivelo, Mbolatiana F.; Université d'Antananarivo - Ecole Supérieure des Sciences Agronomiques (ESSA-forêts), Mention Foresterie et Environnement, Ankatso ; Ecole Doctorale Gestion des Ressources Naturelles et Développement (ED GRND), Université d'Antananarivo, Ankatso
Raholinarivo, Sismondi; Université d'Antananarivo - Ecole Supérieure des Sciences Agronomiques (ESSA-forêts), Mention Foresterie et Environnement, Ankatso ; Ecole Doctorale Gestion des Ressources Naturelles et Développement (ED GRND), Université d'Antananarivo, Ankatso
Rakoto Ratsimba, Harifidy; Université d'Antananarivo - Ecole Supérieure des Sciences Agronomiques (ESSA-forêts), Mention Foresterie et Environnement, Ankatso
Bogaert, Jan ; Université de Liège - ULiège > Département GxABT > Biodiversité, Ecosystème et Paysage (BEP)
Bastin, Jean-François ; Université de Liège - ULiège > TERRA Research Centre > Biodiversité, Ecosystème et Paysage (BEP)
Language :
English
Title :
The importance of the sampling design in mapping woody cover in arid ecosystems
Abel, C., S., Horion, T., Tagesson, M., Brandt, and R., Fensholt. 2019. “Towards Improved Remote Sensing Based Monitoring of Dryland Ecosystem Functioning Using Sequential Linear Regression Slopes (Sergs).” Remote Sensing of Environment224: 317–332. https://doi.org/10.1016/j.rse.2019.02.010.
Acharki, S., P. L., Frison, M., Amharref, H., Khoj, and S., Bernoussi. 2021. “Complémentarité Des Images Optiques SENTINEL-2 Avec Les Images Radar SENTINEL-1 Et ALOS-PALSAR-2 Pour La Cartographie De La Couverture végétale: Application à Une Aire protégée Et Ses Environs Au Nord-Ouest Du Maroc Via Trois Algorithmes D’Apprentissage Automatique.” RFPT223: 143–158. https://doi.org/10.52638/rfpt.2021.599.
Bastin, J.-F., N., Berrahmouni, A., Grainger, D., Maniatis, D., Mollicone, R., Moore, C., Patriarca, N., Picard, B., Sparrow, and E. M., Abraham. 2017. “The Extent of Forest in Dryland Biomes.” Science (New York, N.Y.)356: 635–638. https://doi.org/10.1126/science.aam6527.
Bastin, J.-F., Y., Finegold, C., Garcia, D., Mollicone, M., Rezende, D., Routh, C. M., Zohner, and T. W., Crowther. 2019. “The Global Tree Restoration Potential.” Science (New York, N.Y.)365: 76–79. https://doi.org/10.1126/science.aax0848.
Belgiu, M., and L., Drăguţ. 2016. “Random Forest in Remote Sensing: a Review of Applications and Future Directions.” ISPRS Journal of Photogrammetry and Remote Sensing114: 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011.
Bey, A., A., Sánchez-Paus Díaz, D., Maniatis, G., Marchi, D., Mollicone, S., Ricci, J.-F., Bastin, et al.2016. “Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation.” Remote Sensing8: 807. https://doi.org/10.3390/rs8100807.
Brandt, M., C. J., Tucker, A., Kariryaa, K., Rasmussen, C., Abel, J., Small, J., Chave, et al.2020. “An Unexpectedly Large Count of Trees in the West African Sahara and Sahel.” Nature (London, United Kingdom)587: 78–82. https://doi.org/10.1038/s41586-020-2824-5.
Brandt, M., P., Hiernaux, T., Tagesson, A., Verger, K., Rasmussen, A. A., Diouf, C., Mbow, E., Mougin, and R., Fensholt. 2016. “Woody plant cover estimation in drylands from Earth Observation based seasonal metrics.” Remote Sensing of Environment172: 28–38. https://doi.org/10.1016/j.rse.2015.10.036.
Breiman, L. 2001. “Random Forests.” Machine Learning45: 5–32. https://doi.org/10.1023/A:1010933404324.
Brown, C. F., S. P., Brumby, B., Guzder-Williams, T., Birch, S. B., Hyde, J., Mazzariello, W., Czerwinski, et al.2022. “Dynamic World, Near Real-Time Global 10 M Land Use Land Cover Mapping.” Scientific Data9: 251. https://doi.org/10.1038/s41597-022-01307-4.
Brown, C. F., Kazmierski, M. R., Pasquarella, V. J., Rucklidge, W. J., Samsikova, M., Zhang, C., Shelhamer, E., et al.2025. AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data. https://doi.org/10.48550/ARXIV.2507.22291.
Colditz, R. R., M., Schmidt, C., Conrad, M. C., Hansen, and S., Dech. 2011. “Land Cover Classification with Coarse Spatial Resolution Data to Derive Continuous and Discrete Maps for Complex Regions.” Remote Sensing of Environment115: 3264–3275. https://doi.org/10.1016/j.rse.2011.07.010.
Crowther, T. W., H. B., Glick, K. R., Covey, C., Bettigole, D. S., Maynard, S. M., Thomas, J. R., Smith, et al.2015. “Mapping Tree Density at a Global Scale.” Nature (London, United Kingdom)525: 201–205. https://doi.org/10.1038/nature14967.
D’Odorico, P., A., Bhattachan, K. F., Davis, S., Ravi, and C. W., Runyan. 2013. “Global Desertification: Drivers and Feedbacks.” Advances in Water Resources51: 326–344. https://doi.org/10.1016/j.advwatres.2012.01.013.
De Marzo, T., D., Pflugmacher, M., Baumann, E. F., Lambin, I., Gasparri, and T., Kuemmerle. 2021. “Characterizing Forest Disturbances Across the Argentine Dry Chaco Based on Landsat Time Series.” International Journal of Applied Earth Observation and Geoinformation98: 102310. https://doi.org/10.1016/j.jag.2021.102310.
Dostálová, A., M., Doubková, D., Sabel, B., Bauer-Marschallinger, and W., Wagner. 2014. “Seven Years of Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) of Surface Soil Moisture over Africa.” Remote Sensing6: 7683–7707. https://doi.org/10.3390/rs6087683.
Drusch, M., U., Del Bello, S., Carlier, O., Colin, V., Fernandez, F., Gascon, B., Hoersch, et al.2012. “Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services.” Remote Sensing of Environment120: 25–36. https://doi.org/10.1016/j.rse.2011.11.026.
Eldridge, D. J., M. A., Bowker, F. T., Maestre, E., Roger, J. F., Reynolds, and W. G., Whitford. 2011. “Impacts of Shrub Encroachment on Ecosystem Structure and Functioning: Towards a Global Synthesis.” Ecology letters14: 709–722. https://doi.org/10.1111/j.1461-0248.2011.01630.x.
Fassnacht, F. E., F., Hartig, H., Latifi, C., Berger, J., Hernández, P., Corvalán, and B., Koch. 2014. “Importance of Sample Size, Data Type and Prediction Method for Remote Sensing-Based Estimations of Aboveground Forest Biomass.” Remote Sensing of Environment154: 102–114. https://doi.org/10.1016/j.rse.2014.07.028.
Fassnacht, F. E., H., Latifi, K., Stereńczak, A., Modzelewska, M., Lefsky, L. T., Waser, C., Straub, and A., Ghosh. 2016. “Review of Studies on Tree Species Classification from Remotely Sensed Data.” Remote Sensing of Environment186: 64–87. https://doi.org/10.1016/j.rse.2016.08.013.
Feldman, A. F., S., Reed, C., Amaral, A., Babst‐Kostecka, F., Babst, J., Biederman, and C., Devine, et al.2024. “Adaptation and Response in Drylands (ARID): Community Insights for Scoping a NASA Terrestrial Ecology Field Campaign in Drylands.” Earth’s Future12(9): e2024EF004811.https://doi.org/10.1029/2024ef004811.
Feng, S., and Q., Fu. 2013. “Expansion of Global Drylands under a Warming Climate.” Atmospheric Chemistry and Physics13: 10081–10094. https://doi.org/10.5194/acp-13-10081-2013.
Foody, G. M. 2009. “Classification Accuracy Comparison: Hypothesis Tests and the Use of Confidence Intervals in Evaluations of Difference, Equivalence and Non-Inferiority.” Remote Sensing of Environment113: 1658–1663. https://doi.org/10.1016/j.rse.2009.03.014.
Francis, R. J., R. T., Kingsford, K., Moseby, J., Read, R., Pedler, A., Fisher, J., McCann, and R., West. 2023. “Tracking Landscape Scale Vegetation Change in the Arid Zone By Integrating Ground, Drone and Satellite Data.” Remote Sensing in Ecology and Conservation10: 374–387. https://doi.org/10.1002/rse2.375.
Frison, P.-L., L., Jarlan, E., Mougin, P., Hiernaux, A., Dezetter, A., Alhassane, and J.-P., Rudant. 2018. “Forest Biomass Estimation in Tanzanian Miombo Woodlands Using Sentinel-1 and Sentinel-2 Data.” Remote Sensing10: 1439. https://doi.org/10.3390/rs10091439.
Funk, C., P., Peterson, M., Landsfeld, D., Pedreros, J., Verdin, S., Shukla, G., Husak, et al.2015. “The Climate Hazards Infrared Precipitation with stations—a New Environmental Record for Monitoring Extremes.” Scientific Data2: 150066. https://doi.org/10.1038/sdata.2015.66.
Goldblatt, R., A., Rivera Ballesteros, and J., Burney. 2017. “High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sert∼ Ao.” Remote Sensing9: 1336. https://doi.org/10.3390/rs9121336.
Guida-Johnson, B., P. E., Villagra, L. M., Alvarez, F., Rojas, and J. A., Alvarez. 2021. “Finding Woodlands in Drylands: Bases for the Monitoring of Xeric Open Forests in a Cloud Computing Platform.” Remote Sensing Applications: Society and Environment22: 100528. https://doi.org/10.1016/j.rsase.2021.100528.
Hänke, H., J., Barkmann, C., Coral, E., Enfors Kaustky, and R., Marggraf. 2017. “Social-Ecological Traps Hinder Rural Development in Southwestern Madagascar.” E&S22: art42. https://doi.org/10.5751/ES-09130-220142.
Hawkins, D. M. 2004. “The Problem of Overfitting.” Journal of Chemical Information and Computer Sciences44(1): 1–12. https://doi.org/10.1021/ci0342472.
Hersbach, H., B., Bell, P., Berrisford, S., Hirahara, A., Horányi, J., Muñoz-Sabater, J., Nicolas, et al.2020. “The ERA5 Global Reanalysis.” Quarterly Journal of the Royal Meteorological Society146: 1999–2049. https://doi.org/10.1002/qj.3803.
Karakizi, C., O., Gounari, E., Sofikiti, G., Begkos, K., Karantzalos, and E., Symeonakis. 2023. Assessing the Contribution of Optical and Sar Data for Fractional Savannah Woody Vegetation Mapping. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. 3118–3121, Pasadena, CA, USA: IEEE International Geoscience and Remote Sensing Symposium, IEEE, Presented at the IGARSS 2023.https://doi.org/10.1109/IGARSS52108.2023.10282969.
Karl, J. W., S. E., McCord, and B. C., Hadley. 2017. “A Comparison of Cover Calculation Techniques for Relating Point-Intercept Vegetation Sampling to Remote Sensing Imagery.” Ecological Indicators73: 156–165. https://doi.org/10.1016/j.ecolind.2016.09.034.
IPCCLee, H., and J., Romero, eds. 2023. Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team]. Geneva, Switzerland: IPCC. Intergovernmental Panel on Climate Change (IPCC). https://doi.org/10.59327/IPCC/AR6-9789291691647.
McRoberts, R. E., Q., Chen, D. D., Gormanson, and B. F., Walters. 2018. “Probability-And Model-Based Approaches to Inference for Proportion Forest Using Satellite Imagery as Auxiliary Data.” Remote sensing of environment204: 642–649. https://doi.org/10.1016/j.rse.2017.09.032.
Mellor, A., S., Boukir, A., Haywood, and S., Jones. 2015. “Exploring Issues of Training Data Imbalance and Mislabelling on Random Forest Performance for Large Area Land Cover Classification Using the Ensemble Margin.” ISPRS Journal of Photogrammetry and Remote Sensing105: 155–168. https://doi.org/10.1016/j.isprsjprs.2015.03.014.
Meyer, H., Ludwig, M, Milà, C., Linnenbrink, J., and Schumacher, F., 2024. The CAST package for training and assessment of spatial prediction models in R [WWW Document]. arXiv.org. URL (accessed 10.28.25). https://doi.org/10.48550/arXiv.2404.06978.
Nuzzo, R. L., 2019. “Histograms: a Useful Data Analysis Visualization.” PM&R11: 309–312. https://doi.org/10.1002/pmrj.12145.
Olofsson, P., G. M., Foody, M., Herold, S. V., Stehman, C. E., Woodcock, and M. A., Wulder. 2014. “Good Practices for Estimating Area and Assessing Accuracy of Land Change.” Remote Sensing of Environment148: 42–57. https://doi.org/10.1016/j.rse.2014.02.015.
Pengra, B. W., S. V., Stehman, J. A., Horton, D. J., Dockter, T. A., Schroeder, Z., Yang, W. B., Cohen, S. P., Healey, and T. R., Loveland. 2007. “Quality Control and Assessment of Interpreter Consistency of Annual Land Cover Reference Data in an Operational National Monitoring Program.” ISPRS journal of photogrammetry and remote sensing128: 299–306. https://doi.org/10.1016/j.isprsjprs.2017.04.010.
Pérez-Luque, A. J., M. E., Ramos-Font, M. J., Tognetti Barbieri, C., Tarragona Pérez, G., Calvo Renta, and A. B., Robles Cruz. 2022. “Vegetation Cover Estimation in Semi-Arid Shrublands after Prescribed Burning: Field-Ground and Drone Image Comparison.” Drones6: 370. https://doi.org/10.3390/drones6110370.
Pouliot, D., R., Latifovic, J., Pasher, and J., Duffe. 2018. “Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training.” Remote Sensing10: 394. https://doi.org/10.3390/rs10030394.
Probst, P., M. N., Wright, and A., Boulesteix. 2019. “Hyperparameters and Tuning Strategies for Random Forest.” WIREs Data Min & Knowl9(3): e1301. https://doi.org/10.1002/widm.1301.
Provencher-Nolet, L., M., Bernier, and E., Lévesque. 2014. Short Term Change Detection in Tundra Vegetation Near Umiujaq, Subarctic Quebec, Canada In 2014 IEEE Geoscience and Remote Sensing Symposium4668–4670. IEEE Geoscience and Remote Sensing Symposium, Presented at the 2014.https://doi.org/10.1109/IGARSS.2014.6947534.
Qu, L., H., Lu, Z., Tian, J. M., Schoorl, B., Huang, Y., Liang, D., Qiu, and Y., Liang. 2024. “Spatial Prediction of Soil Sand Content at Various Sampling Density Based on Geostatistical and Machine Learning Algorithms in Plain Areas.” CATENA234: 107572. https://doi.org/10.1016/j.catena.2023.107572.
Ramalason, F. N., O. L., Rakotondrasoa, and J.-F., Bastin. 2025a. Recension Des Travaux antérieurs Sur Les méthodes utilisées Dans La Cartographie Des écosystèmes Forestiers Des Zones Arides: Cas Du Sud-Ouest De Madagascar. In LES CAHIERS DE L’ED GRND. Presented at the Colloque de l’ED GRND 2023, ED, GRND, Antananarivo, Madagascar.
Ramalason, F. N., O. L., Rakotondrasoa, A. V., Linden, G., Renard, H. R., Ratsimba, J., Bogaert, and J.-F., Bastin. 2025b. “Potential of Global Vegetation Maps in Capturing Xerophytic Vegetation Cover: Insights from Madagascar’S Arid Ecosystems.” Journal of Arid Environments229: 105406. https://doi.org/10.1016/j.jaridenv.2025.105406.
Randriamalala, J. R., H. O., Radosy, M., Ramanakoto, H., Razafindrahanta, J.-M., Ravoninjatovo, R. S., Haingomanantsoa, and T., Ramananantoandro. 2022. “Allometric Models to Predict the Individual Aboveground Biomass of Shrubs of Malagasy Xerophytic Thickets.” Journal of Arid Environments202: 104751. https://doi.org/10.1016/j.jaridenv.2022.104751.
Reiche, J., R., Verhoeven, J., Verbesselt, E., Hamunyela, N., Wielaard, and M., Herold. 2018. “Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts.” Remote Sensing10: 777. https://doi.org/10.3390/rs10050777.
Reynolds, J. F., D. M., Stafford Smith, E. F., Lambin, B. L., Turner, M., Mortimore, S. P., Batterbury, T. E., Downing, H., Dowlatabadi, R. J., Fernández, and J. E., Herrick. 2007. “Global Desertification: Building a Science for Dryland Development.” Science (New York, N.Y.)316: 847–851. https://doi.org/10.1126/science.1131634.
Saah, D., G., Johnson, B., Ashmall, G., Tondapu, K., Tenneson, M., Patterson, A., Poortinga, et al.2019. “Collect Earth: an Online Tool for Systematic Reference Data Collection in Land Cover and Use Applications.” Environmental Modelling & Software118: 166–171. https://doi.org/10.1016/j.envsoft.2019.05.004.
Sengani, D., A., Ramoelo, and E., Archer. 2023. “A Review of Fusion Framework Using Optical Sensors and Synthetic Aperture Radar Imagery to Detect and Map Land Degradation and Sustainable Land Management in the Semi-Arid Regions.” Geocarto International38: 2278325. https://doi.org/10.1080/10106049.2023.2278325.
Stehman, S. V., and G. M., Foody. 2019. “Key Issues in Rigorous Accuracy Assessment of Land Cover Products.” Remote Sensing of Environment231: 111199. https://doi.org/10.1016/j.rse.2019.05.018.
Stock, A. 2025. “Choosing Blocks for Spatial Cross-Validation: Lessons from a Marine Remote Sensing Case Study.” Frontiers in Remote Sensing6: 1531097. https://doi.org/10.3389/frsen.2025.1531097.
Symeonakis, E., A., Korkofigkas, T., Higginbottom, J., Boyd, E., Arnau-Rosalen, G., Stamou, and K., Karantzalos. 2022. Towards a Deep Learning Fractional Woody Vegetation Cover Monitoring Framework. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium5905–5908, Kuala Lumpur, Malaysia: IEEE, Presented at the IGARSS 2022.https://doi.org/10.1109/IGARSS46834.2022.9883347.
Torres, R., P., Snoeij, D., Geudtner, D., Bibby, M., Davidson, E., Attema, P., Potin, et al.2012. “GMES Sentinel-1 Mission.” Remote Sensing of Environment120: 9–24. https://doi.org/10.1016/j.rse.2011.05.028.
Trabucco, A., and R., Zomer. 2019. Global Aridity Index and Potential Evapotranspiration (ET0) Climate Databasev2. https://doi.org/10.6084/M9.FIGSHARE.7504448.V3.
Tsendbazar, N., M., Herold, L., Li, A., Tarko, S., de Bruin, D., Masiliunas, M., Lesiv, et al.2021. “Towards Operational Validation of Annual Global Land Cover Maps.” Remote Sensing of Environment266: 112686. https://doi.org/10.1016/j.rse.2021.112686.
UNCCD.2024. The Global Threat of Drying Lands: Regional and global aridity trends and future projections. A Report of the Science-Policy Interface.
Valavi, R., J., Elith, J., Lahoz-Monfort, and G., Guillera-Arroita. 2018. “blockCV: an R Package for Generating Spatially or Environmentally Separated Folds for k-fold Cross-Validation of Species Distribution Models.” Methods in Ecology and Evolution10: 225–232. https://doi.org/10.1111/2041-210X.13107.
Valjarević, A., A., Šiljeg, S., Šiljeg, F., Vujović, and A., Sahay. 2025. “GIS-based Water Stress Analysis in North African Drylands.” Journal of Arid Environments230: 105427. https://doi.org/10.1016/j.jaridenv.2025.105427.
Valjarević, A., T., Djekić, V., Stevanović, R., Ivanović, and B., Jandziković. 2018. “GIS Numerical and Remote Sensing Analyses of Forest Changes in the Toplica Region for the Period of 1953-2013.” Applied Geography92: 131–139. https://doi.org/10.1016/j.apgeog.2018.01.016.
Van Passel, J., W., De Keersmaecker, and B., Somers. 2020. “Monitoring Woody Cover Dynamics in Tropical Dry Forest Ecosystems Using sentinel-2 Satellite Imagery.” Remote Sensing12: 1276. https://doi.org/10.3390/rs12081276.
Wang, G., G., Gertner, and A. B., Anderson. 2005. “Sampling Design and Uncertainty Based on Spatial Variability of Spectral Variables for Mapping Vegetation Cover.” International Journal of Remote Sensing26: 3255–3274. https://doi.org/10.1080/01431160500114748.
White, A., B., Sparrow, E., Leitch, J., Foulkes, R., Flitton, A. J., Lowe, and S., Caddy-Retalic. 2012. AusPlots Rangelands survey protocols manual. University of Adelaide Press.
Yamazaki, D., D., Ikeshima, J., Sosa, P. D., Bates, G. H., Allen, and T. M., Pavelsky. 2019. “MERIT Hydro: a High-Resolution Global Hydrography Map Based on Latest Topography Dataset.” Water Resources Research55: 5053–5073. https://doi.org/10.1029/2019WR024873.
Zhang, X., Q., Pan, Y., Zhao, and Q, Huang. 2005. Research on spatial deviation analysis model of land-use change. In Presented at the MIPPR 2005 Image Analysis TechniquesD., Li and H., Ma, eds. 60440C. Wuhan, China. https://doi.org/10.1117/12.652320.
