Deforestation by human activities is a common issue in Amazonian countries. This occurs at different spatial and temporal scales causing primary forest loss and land fragmentation issues. During the deforestation process as the forest loses connectivity, the deforested patches create new intricate connections, which in turn create complex networks. In this study, we analyzed the local connected fractal dimension (LCFD) of the deforestation process in the Sumaco Biosphere Reserve (SBR) with two segmentation methods, —CA-wavelet and K-means—to categorize the complexity of deforested patches’ connections and then relate these with the spatial processes. The results showed an agreement with both methods, in which LCFD values below 1 corresponded to isolated patches with simple shapes and those above 1 signified more complex and connected patches. From CA-wavelet a threshold of 1.57 was detected allowing us to identify and discern low and high land transformation, while the threshold for K-means was 1.61. Both values represent the region from which deforestation performs local aggressive expansion networks. The thresholds were used to map the LCFD in which all spatial processes were visually detected. However, the threshold of 1.6 ± 0.03 was more effective in discerning high land transformation. such as shrinkage and attrition, in the deforestation process in the SBR.

The conventional methods of land use and land cover (LULC) analysis are frequently based on crosstabulation matrices. However, this analysis becomes complex when including sites with multiple management zones and categories at different time points. This is challenging regarding data processing and the presentation of numerous results. We transformed the graphical representation of conventional Intensity Analysis to assess and compare the intensity and magnitude of LULC changes in six Continental Ecuadorian Biosphere Reserves at three levels: interval, category, and transition, and at three time intervals. A dimension reduction strategy was used to convert multiple bar charts into three composite heat maps. The results confirm the global conservation effectiveness in the core zones among the biosphere reserves with less than 10% change, but worrisome dynamics were detected in buffers and transitions with a change of up to 10% for all periods. Deforestation and agriculture were detected as the most relevant land processes. This work highlights the dimension reduction by summarizing 17, 51, and 312 bar charts from conventional Intensity Analysis in three composite heat maps, one for each level of analysis. Systematic suspicious transitions occurred in the water body category because of its dynamics and misclassification in LULC maps.

Agricultural remote sensing provides valuable information on various characteristics of vegetation states and processes that cannot be simultaneously surveyed over large areas of the territory or with high frequency. Grasslands are recognized worldwide as strategic vegetation in ecosystems and especially in southern Ecuador in South America, due to their extension and importance in the configuration of the social fabric. This area presents an interesting area for studying grassland dynamics since there is a complex mosaic of natural, semi-natural and managed grasslands in which ranchers, indigenous and farmers share a bounding and fragmented landscape. This work aims to validate the use of specific remote sensing tools for monitoring grassland dynamics and in the improvement in identifying the general management rules under fragmented landscape features. To do this, indices and metrics of historical series of satellite images are used, this facilitates the development of biomass production evaluation procedures with greater spatiotemporal precision. The vegetation indices coupled with advanced window timing record sensitivity points allow correlating a set of interested plots reducing the uncertainty in similar biogeographic conditions such as soil properties, slope and vegetation management. The preliminary results show that a reduced number of sensitive monitoring points is suitable for stakeholders in monitoring local and regional areas for estimates of grassland impacts in terms of high/low production, drought, and excess rains. Thus, this work supports the advancement towards optimizations in monitoring vegetation dynamics.  Besides, it develops a common methodological framework that can be used as a reference for the monitoring of pastures in mountainous fragmented landscapes and is useful for parameterising/calibrating vegetation and hydrological models. AcknowledgementsThe authors acknowledge the support of Master in Climate Change, Agriculture and Sustainable Rural Development (MACCARD), co-funded by the Erasmus + Programme of the European Union. The authors also acknowledge support from European Union NextGenerationEU and RD 289/2021 and the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades de España. References David Rivas-Tabares, Ana M. Tarquis, Ángel de Miguel, Anne Gobin, Bárbara Willaarts. Enhancing LULC scenarios impact assessment in hydrological dynamics using participatory mapping protocols in semiarid regions. Sci. Total Environ., 803, 149906, 2022. https://doi.org/10.1016/j.scitotenv.2021.149906 Rivas-Tabares, A. de Miguel, B. Willarts and A.M. Tarquis. Self-organising map of soil properties in the context of hydrological modeling. Applied Mathematical Modelling, 88,175-189, 2020. https://doi.org/10.1016/j.apm.2020.06.044 Rivas-Tabares, D. A., Saa-Requejo, A., Martín-Sotoca, J. J., & Tarquis, A. M. (2021). Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain. Remote Sensing, 13(4), 568. Urgilez‐Clavijo, A., de la Riva, J., Rivas‐Tabares, D. A., & Tarquis, A. M. (2021). Linking deforestation patterns to soil types: A multifractal approach. European Journal of Soil Science, 72(2), 635-655.

The expansion of the agricultural frontier is a process that has been affecting natural ecosystems, driving landscape fragmentation, and promoting habitat loss from 1990 in the Ecuadorian Amazon. Characterizing spatial patterns of the expansion of agricultural frontier can provide valuable data to take forward trade-offs in areas with exacerbated expansion rates and habitat loss (Urgilez-Clavijo et al., 2020). The aim of this work is to identify and characterize the spatial patterns of the expansion of the agricultural frontier in Ecuador and provide an alternative to setting the priority areas.With this purpose, an image analysis approach was applied to identify process patterns using classified images from 1990 to 2020. A statistical analysis of the agricultural expansion dynamics is performed in the Amazon region accumulating the land use information. Complementary to this, we used a soil map to detect a correlation of the process to soil types. Then the Intensity Analysis (IA) was implemented to characterize and visualize the spatio-temporal rates of the expansion process. This method allows identifying areas in which the process is faster and active.The results show distinct patterns of agricultural expansion in the Amazon region, especially from Andean hill slopes to the primary forest. These processes are in part explained by soil type suitability, transportation network development, and urban expansion. The spatial priorities of the expansion of the agricultural frontier are identified from two sources, i) from intensity analysis graphs and ii) from regional maps. The spatial characteristics and identification of spatial priorities of the expansion of the agricultural frontier will bring valuable information to policymakers to achieve SDG 15th of the 2030 Agenda in Ecuador.Keywords: expansion of agricultural frontier, Intensity Analysis, priority areas, image analysis, patternsReferenceUrgilez-Clavijo, A., J. de la Riva, D. Rivas-Tabares and A.M. Tarquis. Linking deforestation patterns to soil types: A multifractal approach. European Journal of Soil Science, https://doi.org/10.1111/ejss.13032AcknowledgementsThe authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330

The agricultural expansion frontier in Ecuador is mainly attributed to the deforestation process. Exacerbated rates of forest loss have been motivated more significant impacts in the territory, even negatively affecting the new uses of land, agriculture, and livestock. Climatic conditions have been changing in larger deforested areas, increasing landslides, floods, water level rise, and drought. This work aims to go deeper in understanding the deforestation process patterns by analyzing the structure of the expansion at the patch level.We used the concept of patch skeletons and the Local connected fractal analysis (LCFA) through its temporal dynamics to identify complex hotspots inside the new agricultural areas. The K-means algorithm was used to perform LCFA segmentation and colouring to identify the complex intensity of the deforestation structure. This may indicate active expansion areas associated with high risked areas to perform agriculture and livestock systems because of high ecosystem dynamics recovery.Hot spotting derived from the fractal analysis and k-means clustering not only serves for reforestation but will also lead to decision-makers for monitoring other associated environmental impacts. Most of the deforested areas in Ecuador after 5 to 7 years in agriculture were abandoned because of the nutrient loss and agricultural failure activities because of feeble farming systems infrastructure. LCFA and colouring communicate in a straightforward spatially explicit visualization strategy the hot spot method to geographically allocate the complex points of the deforested structure of the patches.AcknowledgementsThe authors acknowledge the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020.ReferencesAndrea Urgilez-Clavijo, J. de la Riva, D. Rivas-Tabares and A.M. Tarquis. Linking deforestation patterns to soil types: A multifractal approach. European Journal of Soil Science, 2021, 72(2), 635-655. https://doi.org/10.1111/ejss.13032Andrea Urgilez-Clavijo, D. Rivas-Tabares, J.J. Martín-Sotoca and Ana M. Tarquis. Local fractal connections to characterize the spatial processes of deforestation in Ecuadorian Amazon. Entropy, 23(6), 748, 2021, https://doi.org/10.3390/e23060748