Abstract
This chapter investigates changes in land use intensity in a crop-livestock farming system on Lemnos Island through the combination of land use/land cover (LULC) types extracted from black and white (B&W) aerial images, statistics, and qualitative data. Combining quantitative and qualitative data, different insights of land use and landscape changes are assessed. The steps are: first, the timeline of historical changes was compiled; then, remote sensing was used to assess land cover changes and conversions that represent changes in intensity, and this information was complemented by participatory mapping with local farmers. Land use trajectories revealed that extensification is the basic trend from 1960 to 2002. Intensification coexists in at the same time, as grasslands convert to crops. There is a distinct pattern between periods: extensification seems to be the main process of change from 1960 to 1980 affecting mainly the hilly uplands as more remote and marginal areas are being converted from crops to grasslands or are abandoned, whereas intensification is the main trend for the next 20 years mainly in the lowlands as modernized agriculture (irrigated fields, land aggregation, machinery use) replaces more extensive land uses and traditional landscape elements such as tree hedges. The role of complementarity is very important. Conclusively, this case study shows that in some farming systems land use intensity changes cannot be represented through simple dichotomist differentiations.
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Davis, K. F., Gephart, J. A., Emery, K. A., et al. (2016). Meeting future food demand with current agricultural resources. Global Environmental Change. https://doi.org/10.1016/j.gloenvcha.2016.05.004
Smith, P., House JI, Bustamante, M., et al. (2016). Global change pressures on soils from land use and management. Global Change Biology. https://doi.org/10.1111/gcb.13068
Foley, J. A. (2011). Can we feed the world & sustain the planet? Scientific American. https://doi.org/10.1038/scientificamerican1111-60
Schröter, D., Cramer, W., Leemans, R., et al. (2005). Ecology: Ecosystem service supply and vulnerability to global change in Europe. Science, 80. https://doi.org/10.1126/science.1115233
Herzog, F., Steiner, B., Bailey, D., et al. (2006). Assessing the intensity of temperate European agriculture at the landscape scale. European Journal of Agronomy. https://doi.org/10.1016/j.eja.2005.07.006
Stoate, C., Báldi, A., Beja, P., et al. (2009). Ecological impacts of early 21st century agricultural change in Europe - a review. Journal of Environmental Management.
Tilman, D., Cassman, K. G., Matson, P. A., et al. (2002). Agricultural sustainability and intensive production practices. Nature.
Kuemmerle, T., Erb, K., Meyfroidt, P., et al. (2013). Challenges and opportunities in mapping land use intensity globally. Current Opinion in Environment Sustainability.
Di Gregorio, A., & Jansen, L. J. M. (2000). Land cover classification system (LCCS): Classification concepts and user manual. FAO. https://doi.org/10.1017/CBO9781107415324.004
Lambin, E. F., Geist, H., & Rindfuss, R. R. (2008). Introduction: Local processes with global impacts. In Land-use and land-cover change.
Fisher, P., & Unwin, D. (2005). Land use and land cover: Contradiction or complement. In Re-presenting GIS.
Bürgi, M., Bieling, C., von Hackwitz, K., et al. (2017). Processes and driving forces in changing cultural landscapes across Europe. Landscape Ecology. https://doi.org/10.1007/s10980-017-0513-z
Erb, K. H., Haberl, H., Jepsen, M. R., et al. (2013). A conceptual framework for analysing and measuring land-use intensity. Current Opinion in Environment Sustainability.
Binswanger, H. P., & Rosenzweig, M. R. (1986). Behavioural and material determinants of production relations in agriculture. Journal of Development Studies. https://doi.org/10.1080/00220388608421994
Matson, P. A. (1997). Agricultural intensification and ecosystem properties. Science, 277(80), 504–509. https://doi.org/10.1126/science.277.5325.504
Beeson, P. C., Daughtry, C. S. T., Hunt, E. R., et al. (2016). Multispectral satellite mapping of crop residue cover and tillage intensity in Iowa. Journal of Soil and Water Conservation. https://doi.org/10.2489/jswc.71.5.385
Estel, S., Kuemmerle, T., Levers, C., et al. (2016). Mapping cropland-use intensity across Europe using MODIS NDVI time series. Environmental Research Letters. https://doi.org/10.1088/1748-9326/11/2/024015
Temme, A. J. A. M., & Verburg, P. H. (2011). Mapping and modelling of changes in agricultural intensity in Europe. Agriculture, Ecosystems and Environment. https://doi.org/10.1016/j.agee.2010.11.010
Doraiswamy, P. C., Moulin, S., Cook, P. W., & Stern, A. (2003). Crop yield assessment from remote sensing. Photogramm. Eng. Remote Sensing.
Fang, H., Liang, S., & Hoogenboom, G. (2011). Integration of MODIS LAI and vegetation index products with the CSM-CERES-maize model for corn yield estimation. International Journal of Remote Sensing. https://doi.org/10.1080/01431160903505310
Prasad, A. K., Chai, L., Singh, R. P., & Kafatos, M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2005.06.002
Bubová, T., Vrabec, V., Kulma, M., & Nowicki, P. (2015). Land management impacts on European butterflies of conservation concern: A review. Journal of Insect Conservation.
De Palma, A., Kuhlmann, M., Roberts, S. P. M., et al. (2015). Ecological traits affect the sensitivity of bees to land-use pressures in European agricultural landscapes. Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.12524
Frenzel, M., Everaars, J., & Schweiger, O. (2016). Bird communities in agricultural landscapes: What are the current drivers of temporal trends? Ecological Indicators. https://doi.org/10.1016/j.ecolind.2015.11.020
Liu, Y., Rothenwöhrer, C., Scherber, C., et al. (2014). Functional beetle diversity in managed grasslands: Effects of region, landscape context and land use intensity. Landscape Ecology, 29, 529–540. https://doi.org/10.1007/s10980-014-9987-0
de Groot, R. S., Alkemade, R., Braat, L., et al. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological Complexity. https://doi.org/10.1016/j.ecocom.2009.10.006
Rega, C., & Spaziante, A. (2013). Linking ecosystem services to Agri-environmental schemes through SEA: A case study from northern Italy. Environmental Impact Assessment Review. https://doi.org/10.1016/j.eiar.2012.09.002
Chen, N., Li, H., & Wang, L. (2009). A GIS-based approach for mapping direct use value of ecosystem services at a county scale: Management implications. Ecological Economics. https://doi.org/10.1016/j.ecolecon.2008.12.001
Maes, J., Egoh, B., Willemen, L., et al. (2012). Mapping ecosystem services for policy support and decision making in the European Union. Ecosystem Services, 1, 31–39. https://doi.org/10.1016/j.ecoser.2012.06.004
Mouchet, M. A., Paracchini, M. L., Schulp, C. J. E., et al. (2017). Bundles of ecosystem (dis)services and multifunctionality across European landscapes. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2016.09.026
Plieninger, T., Dijks, S., Oteros-Rozas, E., & Bieling, C. (2013). Assessing, mapping, and quantifying cultural ecosystem services at community level. Land Use Policy. https://doi.org/10.1016/j.landusepol.2012.12.013
Sherrouse, B. C., Clement, J. M., & Semmens, D. J. (2011). A GIS application for assessing, mapping, and quantifying the social values of ecosystem services. Applied Geography. https://doi.org/10.1016/j.apgeog.2010.08.002
Alcantara, C., Kuemmerle, T., Prishchepov, A. V., & Radeloff, V. C. (2012). Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sensing of Environment, 124, 334–347. https://doi.org/10.1016/j.rse.2012.05.019
Redo, D. J., & Millington, A. C. (2011). A hybrid approach to mapping land-use modification and land-cover transition from MODIS time-series data: A case study from the Bolivian seasonal tropics. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2010.09.007
Godinot, O., Leterme, P., Vertès, F., & Carof, M. (2016). Indicators to evaluate agricultural nitrogen efficiency of the 27 member states of the European Union. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2016.02.007
Kerr, J. T., & Cihlar, J. (2003). Land use and cover with intensity of agriculture for Canada from satellite and census data. Global Ecology and Biogeography. https://doi.org/10.1046/j.1466-822X.2003.00017.x
Dixon, J., Gulliver, A., & Gibbon, D. (2001). Farming systems and poverty: Improving farmers’ livelihoods in a changing world.
Bell, L. W., & Moore, A. D. (2012). Integrated crop–livestock systems in Australian agriculture: Trends, drivers and implications. Agricultural Systems, 111, 1–12. https://doi.org/10.1016/j.agsy.2012.04.003
Moraine, M., Duru, M., Nicholas, P., et al. (2014). Farming system design for innovative crop-livestock integration in Europe. Animal. https://doi.org/10.1017/S1751731114001189
Bégué, A., Arvor, D., Bellon, B., et al. (2018). Remote sensing and cropping practices: A review. Remote Sensing.
Defourny, P., Bontemps, S., Bellemans, N., et al. (2019). Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2018.11.007
Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111402
Yin, H., Prishchepov, A. V., Kuemmerle, T., et al. (2018). Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2018.02.050
Kolecka, N. (2018). Height of successional vegetation indicates moment of agricultural land abandonment. Remote Sensing. https://doi.org/10.3390/rs10101568
Prishchepov, A. V., Radeloff, V. C., Dubinin, M., & Alcantara, C. (2012). The effect of Landsat ETM/ETM+ image acquisition dates on the detection of agricultural land abandonment in Eastern Europe. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2012.08.017
Dubovyk, O., Menz, G., Conrad, C., et al. (2013). Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-012-2904-6
Liu, X., & Bo, Y. (2015). Object-based crop species classification based on the combination of airborne hyperspectral images and LiDAR data. Remote Sensing, 7, 922–950. https://doi.org/10.3390/rs70100922
Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, 1335–1343. https://doi.org/10.1109/TGRS.2004.827257
Phiri, D., & Morgenroth, J. (2017). Developments in landsat land cover classification methods: A review. Remote Sensing, 9, 967. https://doi.org/10.3390/rs9090967
Sohn, Y., & Rebello, N. S. (2002). Supervised and unsupervised spectral angle classifiers. Photogrammetric Engineering & Remote Sensing, 68, 1271–1280.
Strahler, A. H. (1980). The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment, 10, 135–163. https://doi.org/10.1016/0034-4257(80)90011-5
Vasilakos, C., Kavroudakis, D., & Georganta, A. (2020). Machine learning classification ensemble of multitemporal Sentinel-2 images: The case of a mixed mediterranean ecosystem. Remote Sensing. https://doi.org/10.3390/rs12122005
Walter, V. (2004). Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 225–238. https://doi.org/10.1016/j.isprsjprs.2003.09.007
Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing.
Li, Q., Wang, C., Zhang, B., & Lu, L. (2015). Object-based crop classification with landsat-MODIS enhanced time-series data. Remote Sensing, 7, 16091–16107. https://doi.org/10.3390/rs71215820
Goodin, D. G., Anibas, K. L., & Bezymennyi, M. (2015). Mapping land cover and land use from object-based classification: An example from a complex agricultural landscape. International Journal of Remote Sensing, 36, 4702–4723. https://doi.org/10.1080/01431161.2015.1088674
Csillik, O., Belgiu, M., Asner, G. P., & Kelly, M. (2019). Object-based time-constrained dynamic time warping classification of crops using Sentinel-2. Remote Sensing. https://doi.org/10.3390/rs11101257
Watkins, B., & Van Niekerk, A. (2019). Automating field boundary delineation with multi-temporal Sentinel-2 imagery. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2019.105078
Crespin-Boucaud, A., Lebourgeois, V., Lo Seen, D., et al. (2020). Agriculturally consistent mapping of smallholder farming systems using remote sensing and spatial modelling. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci, XLII-3(W11), 35–42. https://doi.org/10.5194/isprs-archives-XLII-3-W11-35-2020
Morgan, J. L., Gergel, S. E., & Coops, N. C. (2010). Aerial photography: A rapidly evolving tool for ecological management. Bioscience. https://doi.org/10.1525/bio.2010.60.1.9
Vogels, M. F. A., de Jong, S. M., Sterk, G., & Addink, E. A. (2017). Agricultural cropland mapping using black-and-white aerial photography, object-based image analysis and random forests. International Journal of Applied Earth Observation and Geoinformation, 54, 114–123. https://doi.org/10.1016/j.jag.2016.09.003
Panitsa, M., Snogerup, B., Snogerup, S., & Tzanoudakis, D. (2003). Floristic investigation of Lemnos island (NE Aegean area, Greece). Willdenowia, 33, 79–105. https://doi.org/10.3372/wi.33.33108
ELSTAT. (2019). Hellenic Statistical Authority. https://www.statistics.gr/en/home/. Accessed 29 Mar 2019.
Dimopoulos, T., Dimitropoulos, G., & Georgiadis, N. (2018). The land use systems of Lemnos island. Terra Lemnia project: Recording of land use systems & practices. Strategy 1.1, action 1.1.1. https://terra-lemnia.net/wp-content/uploads/2019/04/Terra-Lemnia-1.1.1-Land-Use-Systems-of-Lemnos-Dec-2018.pdf. Accessed 18 Apr 2020.
Clapuyt, F., Vanacker, V., & Van Oost, K. (2016). Reproducibility of UAV-based earth topography reconstructions based on structure-from-motion algorithms. Geomorphology, 260, 4–15. https://doi.org/10.1016/j.geomorph.2015.05.011
Westoby, M. J., Brasington, J., Glasser, N. F., et al. (2012). ‘Structure-from-motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300–314. https://doi.org/10.1016/j.geomorph.2012.08.021
Agisoft LLC (2016) Agisoft PhotoScan User Manual: Professional Edition, Version 1.2. In: User Manuals.
Blaschke, T., Lang, S., & Hay, G. J. (2008). Object-based image analysis: Spatial concepts for knowledge-driven remote sensing applications. LibTuDelftNet.
Su, T., & Zhang, S. (2017). Local and global evaluation for remote sensing image segmentation. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2017.06.003
Liu, D., & Xia, F. (2010). Assessing object-based classification: Advantages and limitations. Remote Sensing Letters. https://doi.org/10.1080/01431161003743173
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 610–621. https://doi.org/10.1109/TSMC.1973.4309314
Caridade, C. M. R., Marçal, A. R. S., & Mendonça, T. (2008). The use of texture for image classification of black; white air photographs. International Journal of Remote Sensing, 29, 593–607. https://doi.org/10.1080/01431160701281015
Halounová, L. (2005). Automatic classification of B&W aerial orthophotos. In M. Oluic (Ed.), New strategies for European remote sensing (p. 768).
Hossain, M. D., & Chen, D. (2019). Segmentation for object-based image analysis (OBIA): A review of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.009
Benz, U. C., Hofmann, P., Willhauck, G., et al. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2003.10.002
Rahman, M. R., & Saha, S. K. (2008). Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-008-0020-4
Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing. https://doi.org/10.1080/0143116031000139863
Khorram, S. K., Biging, G. S., Chrisman, N. R., et al. (1999). Accuracy assessment of remote sensing-derived change detection.
Darwish A, Leukert K, Reinhardt W (2003) IMage segmentation for the purpose of object-based classification. In: IGARSS 2003. 2003 IEEE international geoscience and remote sensing symposium. Proceedings (IEEE cat. No.03CH37477). IEEE, pp. 2039–2041.
Ryschawy, J., Choisis, N., Choisis, J. P., et al. (2012). Mixed crop-livestock systems: An economic and environmental-friendly way of farming? Animal. https://doi.org/10.1017/S1751731112000675
Stark, F., González-García, E., Navegantes, L., et al. (2018). Crop-livestock integration determines the agroecological performance of mixed farming systems in Latino-Caribbean farms. Agronomy for Sustainable Development, 38, 4. https://doi.org/10.1007/s13593-017-0479-x
Garrett, R. D., Ryschawy, J., Bell, L. W., et al. (2020). Drivers of decoupling and recoupling of crop and livestock systems at farm and territorial scales. Ecology and Society. https://doi.org/10.5751/ES-11412-250124
Vasilakos, C., Chatzistamatis, S., Roussou, O., & Soulakellis, N. (2019). Comparison of terrestrial photogrammetry and terrestrial laser scanning for earthquake response management. In Lecture notes in geoinformation and cartography (pp. 33–57).
Dimopoulos, T., & Kizos, T. (2020). Mapping change in the agricultural landscape of Lemnos. Landscape and Urban Planning, 203, 103894. https://doi.org/10.1016/j.landurbplan.2020.103894
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This paper is a result of the project “Translation of OAP activities into acknowledged landscape approaches (M6) - (17071)” funded by the MAVA Foundation for Nature.
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Dimopoulos, T., Vasilakos, C., Kizos, T. (2022). Integrating Spatial with Qualitative Data to Monitor Land Use Intensity: Evidence from Arable Land – Animal Husbandry Systems. In: Bochtis, D.D., Moshou, D.E., Vasileiadis, G., Balafoutis, A., Pardalos, P.M. (eds) Information and Communication Technologies for Agriculture—Theme II: Data. Springer Optimization and Its Applications, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-030-84148-5_7
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