1. Preface

Detailed studies of silvo-pastoral systems (SPS), including studies of changes in degradation patterns, as well as the causes and impacts of degradation, for example, on biogeophysical mechanisms, productivity, and resilience, are crucial for building a broad understanding of the overall process of SPS land-use change and to support policymakers in making informed decisions. Timely and accurate data on the main SPS components, such as tree and herbaceous layer cover fraction, and on the spatio-temporal patterns of SPS are therefore critical for such studies. Free access to remote-sensing data sets obtained from spaceborne sensors, along with imagery from airborne sensors and unmanned aerial vehicles (UAVs), supplemented by data from ground-based instruments, allows researchers to ask new scientific questions and address relevant issues, including monitoring and assessing the spatio-temporal trends of SPS land-use changes. Given the presence of a markedly dry season in SPS systems, and also the spatial fuzziness caused by its tree density variability, the spectral characteristics of these landscapes constitute one of the most challenging aspects for remote sensing. Despite the great progress, especially in recent years, the remote sensing of SPS is still evolving due to the growing availability of high-quality remote-sensing-based products able to effectively address the complexity, diversity, and dynamics of these ecosystems. This special issue addresses these and other challenges, as well as focusing on recent advances in remote sensing of SPS. The special issue includes manuscripts originally presented at the ‘World Congress Silvo-Pastoral Systems 2016: Silvo-Pastoral Systems in a changing world: functions, management and people’, held 27–30 September 2016 at Évora, Portugal. The articles can be divided into four groups according to the type of platform used to produce remote and proximal sensing data and derived products, namely spaceborne (satellite), airborne, UAVs, ground instruments, and multi-platform (multi-source applications).

2. Spaceborne (satellite-based) applications

Filizzola et al. (2018 Filizzola, C., R. Corrado, A. Falconieri, M. Faruolo, N. Genzano, M. Lisi, G. Mazzeo, R. Paciello, N. Pergola, and V. Tramutoli. 2018. “On the Use of Temporal Vegetation Indices in Support of Eligibility Controls for EU Aids in Agriculture.” International Journal of Remote Sensing 39: 4572–4598. doi:10.1080/01431161.2017.1395973.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) propose a multi-temporal multispectral algorithm exploiting the Thematic Mapper/Enhanced Thematic Mapper Plus data on Landsat platforms to identify different land covers in the context of the European Union Common Agricultural Policy (CAP). In fact, satellite remote sensing has progressively assumed a key role in identifying and monitoring crops within the framework of the CAP. Nevertheless, the only technique that is still officially accepted in the CAP context for crop identification in the past remains photo-interpretation of high/very high-resolution aerial orthoimages. Where this type of imagery is not available, data from satellite sensors with adequate spatial, spectral, and temporal resolution, together with a reliable data analysis technique, may support, or even substitute for, orthophoto interpretation. In this study, the methodological framework for discriminating arable from non-arable lands showed generally good agreement with standard CAP requirements.

Demisse et al. (2018 Demisse, G. B., T. Tadesse, Y. Bayissa, S. Atnafu, M. Argaw, and D. Nedaw. 2018. “Vegetation Condition Prediction for Drought Monitoring in Pastoralist Areas: A Case Study in Ethiopia.” International Journal of Remote Sensing 39: 4599–4615. doi:10.1080/01431161.2017.1421797.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) developed a remote-sensing-based vegetation condition drought-monitoring approach for pastoralist areas using multi-temporal and spatial resolution satellite (SPOT-VEGETATION and Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI)), climate, and biophysical data sets from Ethiopia. In this study, 24 years of data (1983–2006), with 11 attributes, were extracted and used for developing the prediction models. A classification and regression tree modelling technique (CART) was used to integrate and model the drought parameters. Using the CART models, drought prediction maps were produced for 2016, and the model outputs agreed with what had been reported by government and humanitarian partners of Ethiopia. This methodology can be used for future drought monitoring and early warning for risk-based drought planning.

Abid, Bargaoui, and Mannaerts (2018 Abid, N., Z. Bargaoui, and C. M. Mannaerts. 2018. “Remote Sensing Estimation of the Water Stress Coefficient and Comparison with Drought Evidence.” International Journal of Remote Sensing 39: 4616–4639. doi:10.1080/01431161.2018.1430917.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) present a method of spatial and temporal estimation of drought index in Medjerda basin (Tunisia) for the 2010 water year, using satellite data and its validation with in situ investigation of areas showing crop damage realized by the Tunisia Ministry of Agriculture. To estimate drought index, potential evapotranspiration (PET) was calculated using the Penman–Monteith equation and the modified FAO-56 crop coefficient (Kc) approach combined with remote-sensing data, being the actual evapotranspiration derived from the MSG platforms. PET estimations showed good accuracy. Furthermore, water stress coefficient calculated using optical remote-sensing data can be considered as a good indicator of drought for different land-cover types in the Medjerda basin. This study shows that satellite remote-sensing-based approaches can be used successfully to monitor drought in river basins in Northern Africa and Mediterranean region, being able to support decision-making in drought forecasting, monitoring, and management system.

Godinho, Guiomar, and Gil (2018 Godinho, S., N. Guiomar, and A. Gil. 2018. “Estimating Tree Canopy Cover Percentage in a Mediterranean Silvo-Pastoral System Using Sentinel-2A Imagery and the Stochastic Gradient Boosting Algorithm.” International Journal of Remote Sensing 39: 4640–4662. doi:10.1080/01431161.2017.1399480.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) explored the capabilities of Sentinel-2A multispectral data, in combination with a stochastic modelling technique, for mapping montado tree canopy cover percentage (CCP) at the pixel level. The stochastic gradient boosting algorithm was used to predict tree CCP using Sentinel-2A spectral data, vegetation indices, and textural information as predictor variables. The results of the study showed that the combination of multispectral bands with the selected vegetation indices and grey-level co-occurrence matrix texture features performs well. This study demonstrated the usefulness of narrow spectral bands provided by the Sentinel-2A sensor for accurately estimating tree CCP. The modelling procedure used in this study emphasizes the effectiveness of stochastic models for predicting tree canopy cover from a complex semiarid SPS by using Sentinel-2A multispectral data.

Allen et al. (2018 Allen, H., W. Simonson, E. Parham, E. de Basto e Santos, and P. Hotham. 2018. “Satellite Remote Sensing of Land Cover Change in a Mixed Agro-Silvo-Pastoral Landscape in the Alentejo, Portugal.” International Journal of Remote Sensing 39:4663–4683.. doi:10.1080/01431161.2018.1440095.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) demonstrated a novel approach to the investigation of land-cover change over a 25 year period (1984–2009) in an area of importance for the conservation of the Iberian lynx, Black vulture, and other threatened biodiversity in southeast Alentejo, Portugal. They applied a Tasselled Cap Transformation to Landsat imagery from these two years, and then change vector analysis on the transformed data to highlight areas of vegetation gain and loss during the intervening period. Using a 2009 land-cover classification, and a set of rules based on these vegetation changes, they then predicted the land change over the 25 year period focusing on predominant classes of vegetation physiognomy. They finally identified the probable drivers for these changes, as well as the implications for biodiversity and other landscape values.

Cárdenas et al. (2018 Cárdenas, A., A. Moliner, C. Hontoria, and H. Schernthanner. 2018. “Analysis of Land Use/Land Cover Changes in a Livestock Landscape Dominated by Traditional Silvo-Pastoral Systems: A Methodological Approach Based on Free Open Source Software.” International Journal of Remote Sensing 39: 4684–4698. doi:10.1080/01431161.2018.1463116.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) analysed the land-use/land-cover (LULC) changes affecting forest frontiers and traditional silvo-pastoral systems (TSPS) in a representative livestock area of Nicaragua, by using Landsat scenes from 1986 (Landsat TM 5) and 2015 (Landsat 8 OLI). A methodological framework was conceptually developed and implemented based on free open-source software components and by applying the Random Forests algorithm. A conceptual LULC classification scheme representing TSPS was developed. Although the study area’s imagery showed a heterogeneous surface cover and mixed pixels, it was possible to achieve good overall accuracies above 85% for both data sets. The classifications show that from 1986 to 2015 (29 years) the intervened secondary forest increased 2.6 times while the degraded pastures decreased by 34.5%. The livestock landscape in Matiguás is in a state of constant transformation, but the main changes head towards the positive direction of tree cover recovery and an increased number of areas of natural regeneration.

Lourenço et al. (2018 Lourenço, P., D. Alcaraz-Segura, A. Reyes-Díez, J. Requena-Mullor, and J. Cabello. 2018. “Trends in Vegetation Greenness Dynamics in Protected Areas across Borders: What are the Environmental Controls?” International Journal of Remote Sensing 39:4699–4713. doi:10.1080/01431161.2018.1466080.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) characterized the inter-annual trends of the enhanced vegetation index (EVI) seasonal dynamics in the protected areas (PAs) of Southern Spain and Northern Morocco. They used a time series (from January 2001 to December 2012) derived from the Moderate Resolution Imaging Spectroradiometer Terra sensor satellite imagery. EVI inter-annual trends were grouped into four patterns of trends. Patterns of trends in the EVI seasonal dynamics might be climate-driven in the PAs of both countries. Nevertheless, these dynamics are changing in both countries’ PAs in the short term but not in the same way. Therefore, socioecological processes might be an additional underlying cause of these patterns of trends. The use of remote sensing to monitor temporal trends in ecosystem functioning should be considered to understand the ecological responses to drivers of change in Mediterranean ecosystems.

Soares et al. (2018 Soares, C., A. Príncipe, M. Köbel, A. Nunes, C. Branquinho, and P. Pinho. 2018. “Tracking Tree Canopy Cover Changes in Space and Time in High Nature Value Farmland to Prioritize Reforestation Efforts.” International Journal of Remote Sensing 39: 4714–4726. doi:10.1080/01431161.2018.1475777[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) used aerial photography and Landsat satellite imagery to quantify spatial and temporal changes in montado’s tree cover. This was done relating Landsat-derived normalized difference vegetation index (NDVI) to aerial photography photointerpretation, over a precipitation gradient in a Mediterranean dryland area. Dry season NDVI was positively related with tree canopy cover changes, both over space and time. The spatio-temporal models developed in their study were then validated with independent data and applied over a large area to create regional maps of changes in holm oak canopy cover over space and time. They concluded that NDVI–based data can be used in large-scale assessments of holm oak canopy cover. Furthermore, their findings provide an important tool to improve forest management strategies, e.g. by enabling to map and quantify tree cover decline, and to prioritize areas for reforestation, thus improving ecosystem services delivery in holm oak woodlands, such as improving farmland productivity and resilience to climate change.

3. Airborne applications

López-Sánchez, Dirzo, and Roig (2018 López-Sánchez, A., R. Dirzo, and S. Roig. 2018. “Changes in Livestock Footprint and Tree Layer Coverage in Mediterranean Dehesas: A Six-Decade Study Based on Remote Sensing.” International Journal of Remote Sensing 39: 4727–4743. doi:10.1080/01431161.2017.1365391.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) have used multidate panchromatic and RGB aerial photography from 1956, 1984, 1998, and 2008 to quantify the changes in dehesa landscape status within a geographic area covering the main Spanish dehesa range over the last 60 years by focusing on changes of two important dehesa elements: tree density and live stocking rates. Their multi-scaled analysis explains the impact of social, political, and economic factors on the conservation status of the main dehesa territory within Spain, concluding that it is necessary to ensure the effectiveness of these systems through sustainable activities to avoid negative consequences of poor management practices that threaten dehesa perpetuation.

Navarro et al. (2018 Navarro, J. A., A. Fernández-Landa, J. Tomé, M. Guillén-Climent, and J. Ojeda. 2018. “Testing the Quality of Forest Variable Estimation Using Dense Image Matching: A Comparison with Airborne Laser Scanning in a Mediterranean Pine Forest.” International Journal of Remote Sensing. 39: 4744–4760. doi:10.1080/01431161.2018.1471551.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) explored and assessed the potential of dense image matching (DIM) point clouds for forest mapping, monitoring, and management as an alternative to airborne laser scanning (ALS). Area-based approach estimations from ALS and DIM-based point clouds in a Pinus pinaster Ait. forest located in Madrid region (Spain) were compared. Heights from image matching were normalized by an ALS-derived digital elevation model (DEM). A total of 50 sampling plots were used to fit non-parametric models for the estimation of forest structure variables. This study demonstrates the usefulness of the combination of DIM with ALS-derived DEM to develop forest metrics and high-quality inventories in Mediterranean pine forests. This study has also demonstrated that, despite the differences between ALS and DIM point clouds, DIM provides results close to ALS in Mediterranean pine forests when a high-resolution DEM is available. Accuracy of DIM-based models depends on the availability of a precise DEM due to DIM lack of penetration capacity below the canopy. However, with an accurate DEM, DIM presents an opportunity to develop forest metrics and high-quality inventories in a cost-effective manner. In addition, the cycle of image acquisition is shorter for aerial imagery than for ALS, so DIM may be used in forest mapping when ALS is not updated.

4. UAV-based applications

Mayr et al. (2018 Mayr, M. J., S. Malß, E. Ofner, and C. Samimi. 2018. “Disturbance Feedbacks on the Height of Woody Vegetation in a Savannah: A Multi-Plot Assessment Using an Unmanned Aerial Vehicle (UAV).” International Journal of Remote Sensing 39: 4761–4785. doi:10.1080/01431161.2017.1362132.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) assessed the plot-scale (i.e. 0.5–2 ha) height metrics of woody vegetation in a Namibian savannah using an image-based point cloud acquired with an UAV. They tested whether a low-cost approach that solely relies on the data acquired by the UAV can reliably reproduce the heights of plots consisting of trees and shrubs in an experimental set-up. The results indicate good agreement between the UAV-derived and in situ-measured height metrics on the plot scale. They concluded that the approach applied in their study is able to reproduce the plot-scale heights of woody vegetation with acceptable accuracy.

Surový, Almeida Ribeiro, and Panagiotidis (2018 Surový, P., N. Almeida Ribeiro, and D. Panagiotidis. 2018. “Estimation of Positions and Heights from UAV-sensed Imagery in Tree Plantations in Agrosilvopastoral Systems.” International Journal of Remote Sensing 39: 4786–4800. doi:10.1080/01431161.2018.1434329.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) presented a method to estimate heights and positions of individual trees, from remotely sensed imagery obtained using a low-flying UAV with an integrated RGB sensor. In the summer of 2015, a 5 ha forest stand located in Évora, Portugal, was photographed with a low-flying (40 m) hexacopter. A 3D point cloud and orthophoto were created from the images. The point cloud was used to identify local maxima as candidates for tree positions and height estimates. Their results showed that the height measured with the UAV was reliable on pines, whereas the reliability for oaks was dependent on the size of the trees: smaller trees were especially problematic as they tended to have an irregular crown shape, resulting in larger errors. However, the error showed a strong trend, and adequate models could be produced to improve the estimates.

5. Ground-based applications

Serrano et al. (2018 Serrano, J., J. Shakib Shahidian, M. Da Silva, E. Sales-Baptista, I. Ferraz DeOliveira, J. Lopes DeCastro, A. Pereira, M. Cancela De Abreu, E. Machado, and M. De Carvalho. 2018. “Tree Influence on Soil and Pasture: Contribution of Proximal Sensing to Pasture Productivity and Quality Estimation in Montado Ecosystems.” International Journal of Remote Sensing 39: 4801–4829. doi:10.1080/01431161.2017.1404166.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) evaluate tree influence on soil and pasture parameters in a montado ecosystem located in Southern Portugal. They also evaluate the use of proximal sensing techniques that might have the potential for monitoring aspects related to the spatial and temporal variability of pasture productivity and quality in montado ecosystems. The evolution of the pasture was measured in 24 sampling points at five monitoring dates, from October 2015 to June 2016, using several pasture parameters. They determined that soil under tree canopy had significantly higher levels of organic matter, N, P, K, and Mg, and better pasture quality, while the pasture productivity was higher outside tree canopy. They concluded that the use of fast and efficient tools associated with geo-referenced systems can greatly simplify the pasture monitoring process, which is the basis for estimating feed availability in the field.

6. Multi-source applications

Fernández-Landa et al. (2018 Fernández-Landa, A., J. Fernández-Moya, J. L. Tomé, N. Algeet-Abarquero, M. L. Guillén-Climent, R. Vallejo, V. Sandoval, and M. Marchamalo. 2018. “High Resolution Forest Inventory of Pure and Mixed Stands at Regional Level Combining National Forest Inventory Field Plots, Landsat, and Low Density Lidar.” International Journal of Remote Sensing 39: 4830–4844. doi:10.1080/01431161.2018.1430406.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) developed a remote-sensing-based multi-source approach integrating available databases such as nationwide lidar flights, Landsat imagery, and permanent field plots from the Spanish National Forest Inventory, with good results in the generation of wall-to-wall forest inventories. These available databases offer great opportunities to reduce drastically high-resolution forest inventory costs as this mapping methodological framework allows the generation of valid products both for planning and stand-level management. Pixel predictions can be aggregated to produce unbiased stand estimates, which are valid when designing silvicultural interventions, and to quantify future harvested wood or biomass. Wall-to-wall species stratification is crucial for obtaining accurate estimations of forest variables and Landsat classification may become a highly usable tool for plot-level lidar inventories in temperate forests when data sources have a similar spatial resolution. It might be particularly suitable in areas with several different intermingled species, such as conifer and broadleaf mixed stands.

7. Concluding thoughts

This special issue has contributed in providing new insights in methodological and operational issues associated with SPS mapping, assessment, and monitoring by using remote-sensing and ground-based approaches. It has also demonstrated the wide range of applications able to be explored using different remote-sensing platforms, proximal sensors, and derived products, which include

• spaceborne/satellite-based applications (Abid, Bargaoui, and Mannaerts 2018 Abid, N., Z. Bargaoui, and C. M. Mannaerts. 2018. “Remote Sensing Estimation of the Water Stress Coefficient and Comparison with Drought Evidence.” International Journal of Remote Sensing 39: 4616–4639. doi:10.1080/01431161.2018.1430917.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Allen et al. 2018 Allen, H., W. Simonson, E. Parham, E. de Basto e Santos, and P. Hotham. 2018. “Satellite Remote Sensing of Land Cover Change in a Mixed Agro-Silvo-Pastoral Landscape in the Alentejo, Portugal.” International Journal of Remote Sensing 39:4663–4683.. doi:10.1080/01431161.2018.1440095.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Cárdenas et al., 2018 Cárdenas, A., A. Moliner, C. Hontoria, and H. Schernthanner. 2018. “Analysis of Land Use/Land Cover Changes in a Livestock Landscape Dominated by Traditional Silvo-Pastoral Systems: A Methodological Approach Based on Free Open Source Software.” International Journal of Remote Sensing 39: 4684–4698. doi:10.1080/01431161.2018.1463116.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Demisse et al. 2018 Demisse, G. B., T. Tadesse, Y. Bayissa, S. Atnafu, M. Argaw, and D. Nedaw. 2018. “Vegetation Condition Prediction for Drought Monitoring in Pastoralist Areas: A Case Study in Ethiopia.” International Journal of Remote Sensing 39: 4599–4615. doi:10.1080/01431161.2017.1421797.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Lourenço et al. 2018 Lourenço, P., D. Alcaraz-Segura, A. Reyes-Díez, J. Requena-Mullor, and J. Cabello. 2018. “Trends in Vegetation Greenness Dynamics in Protected Areas across Borders: What are the Environmental Controls?” International Journal of Remote Sensing 39:4699–4713. doi:10.1080/01431161.2018.1466080.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Soares et al. 2018 Soares, C., A. Príncipe, M. Köbel, A. Nunes, C. Branquinho, and P. Pinho. 2018. “Tracking Tree Canopy Cover Changes in Space and Time in High Nature Value Farmland to Prioritize Reforestation Efforts.” International Journal of Remote Sensing 39: 4714–4726. doi:10.1080/01431161.2018.1475777[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Filizzola et al., 2018 Filizzola, C., R. Corrado, A. Falconieri, M. Faruolo, N. Genzano, M. Lisi, G. Mazzeo, R. Paciello, N. Pergola, and V. Tramutoli. 2018. “On the Use of Temporal Vegetation Indices in Support of Eligibility Controls for EU Aids in Agriculture.” International Journal of Remote Sensing 39: 4572–4598. doi:10.1080/01431161.2017.1395973.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Godinho, Guiomar, and Gil 2018 Godinho, S., N. Guiomar, and A. Gil. 2018. “Estimating Tree Canopy Cover Percentage in a Mediterranean Silvo-Pastoral System Using Sentinel-2A Imagery and the Stochastic Gradient Boosting Algorithm.” International Journal of Remote Sensing 39: 4640–4662. doi:10.1080/01431161.2017.1399480.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]);

• airborne applications (Navarro et al. 2018 Navarro, J. A., A. Fernández-Landa, J. Tomé, M. Guillén-Climent, and J. Ojeda. 2018. “Testing the Quality of Forest Variable Estimation Using Dense Image Matching: A Comparison with Airborne Laser Scanning in a Mediterranean Pine Forest.” International Journal of Remote Sensing. 39: 4744–4760. doi:10.1080/01431161.2018.1471551.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; López-Sánchez, Dirzo, and Roig 2018 López-Sánchez, A., R. Dirzo, and S. Roig. 2018. “Changes in Livestock Footprint and Tree Layer Coverage in Mediterranean Dehesas: A Six-Decade Study Based on Remote Sensing.” International Journal of Remote Sensing 39: 4727–4743. doi:10.1080/01431161.2017.1365391.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]);

• UAV-based applications (Surovy et al., 2018 Surový, P., N. Almeida Ribeiro, and D. Panagiotidis. 2018. “Estimation of Positions and Heights from UAV-sensed Imagery in Tree Plantations in Agrosilvopastoral Systems.” International Journal of Remote Sensing 39: 4786–4800. doi:10.1080/01431161.2018.1434329.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Mayr et al. 2018 Mayr, M. J., S. Malß, E. Ofner, and C. Samimi. 2018. “Disturbance Feedbacks on the Height of Woody Vegetation in a Savannah: A Multi-Plot Assessment Using an Unmanned Aerial Vehicle (UAV).” International Journal of Remote Sensing 39: 4761–4785. doi:10.1080/01431161.2017.1362132.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]);

• ground-based applications (Serrano et al. 2018 Serrano, J., J. Shakib Shahidian, M. Da Silva, E. Sales-Baptista, I. Ferraz DeOliveira, J. Lopes DeCastro, A. Pereira, M. Cancela De Abreu, E. Machado, and M. De Carvalho. 2018. “Tree Influence on Soil and Pasture: Contribution of Proximal Sensing to Pasture Productivity and Quality Estimation in Montado Ecosystems.” International Journal of Remote Sensing 39: 4801–4829. doi:10.1080/01431161.2017.1404166.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]);

• multi-source applications (Fernández-Landa et al. 2018 Fernández-Landa, A., J. Fernández-Moya, J. L. Tomé, N. Algeet-Abarquero, M. L. Guillén-Climent, R. Vallejo, V. Sandoval, and M. Marchamalo. 2018. “High Resolution Forest Inventory of Pure and Mixed Stands at Regional Level Combining National Forest Inventory Field Plots, Landsat, and Low Density Lidar.” International Journal of Remote Sensing 39: 4830–4844. doi:10.1080/01431161.2018.1430406.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]).

Despite the great technological and methodological progress in this domain clearly reflected in this special issue, the remote sensing of SPS will remain a key area of future research due to the growing availability of better and more reliable technology and the resulting high-quality remote-sensing-based products able to effectively address the high complexity, diversity, and dynamics of these ecosystems.