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Landslides are disasters that cause damage to anthropic activities, innumerable loss of human life, and affect the natural ecosystem and its services globally. The landslide risk evaluated by integrating susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools to address these disasters, starting with their prevention, with free spatial data and appropriate models. However, to correctly understand their interrelationships and social affection, studying the different ecosystem services that relate to them is necessary. This study is the first that has been attempted in which an integrated application methodology of ecosystem services is used to know in a systematic way if the information that ecosystem services provide is useful for landslide risk assessment. For the integration of ecosystem services into the landslide risk evaluation, (1) eight ecosystem services were chosen and mapped to improve understanding of the spatial relationships between these services in the Guerrero State (México), and (2) areas of synergies and trade-offs were identified through a principal component analysis, to understand their influence on risk analysis better. These are extracted from the models of the ARIES platform, artificial intelligence, and big data platform. Finally, (3) the similarity between the risk characteristics (susceptibility and vulnerability, already mapped by the authors) and the ecosystem services assessment was analysed. The results showed that the ecosystem services that most affect the synergy are organic carbon mass and the potential value of outdoor recreation; meanwhile, the possible removed soil mass was the most important trade-off. Furthermore, the lowest similarity value was found between landslide vulnerability and ecosystem services synergy, indicating the importance of including these ecosystem services as a source of valuable information in the risk analysis methodologies, especially with respect to risk vulnerability. [ABSTRACT FROM AUTHOR]

Land cover is crucial for ecosystems and human activities. Therefore, monitoring land cover changes has become relevant in recent years. This study proposes an alternative method based on conventional change detection techniques combined with maximum likelihood (MaxLike) supervised classification of satellite images to generate consistent Land Use/Land Cover (LULC) maps. The novelty of this method is that the supervised classification is applied in an earlier stage of change detection exclusively to identified dynamics zones. The LULC categories of the stable zones are acquired from an initial date's previously elaborated base map. The methodology comprised the use of Landsat images from 2011 and 2016, applying the Sun Canopy Sensor (SCS + C) topographic correction model enhanced through the classification of slopes, using derived topographic corrected images with NDVI, and employing Tasseled Cap (TC) Brightness-Greenness-Wetness indices and Principal Components (PCs). The study incorporated a comparative analysis of the consistency of the LULC mapping, which is generated based on control areas. The results show that the proposed method, although slightly laborious, is viable and fully automatable. The generated LULC map is accurate and robust and achieves a Kappa concordance index of 87.53. Furthermore, the boundary consistency was visually superior to the conventional classified map. [ABSTRACT FROM AUTHOR]

Empirical verification of multiple states in drylands is scarce, impeding the design of indicators to anticipate the onset of desertification. Remote sensing‐derived indicators of ecosystem states are gaining new ground due to the possibilities they bring to be applied inexpensively over large areas. Remotely sensed albedo has been often used to monitor drylands due to its close relationship with ecosystem status and climate. Here, we used a space‐for‐time‐substitution approach to evaluate whether albedo (averaged from 2000 to 2016) can identify multiple ecosystem states in African drylands spanning from the Saharan desert to tropical Africa. By using latent class analysis, we found that albedo showed two states (low and high; the cut‐off level was 0.22 at the shortwave band). Potential analysis revealed that albedo exhibited an abrupt and discontinuous increase with increased aridity (1 − [precipitation/potential evapotranspiration]). The two albedo states co‐occurred along aridity values ranging from 0.72 to 0.78, during which vegetation cover exhibited a rapid, continuous decrease from ~90% to ~50%. At aridity values of 0.75, the low albedo state started to exhibit less attraction than the high albedo state. Low albedo areas beyond this aridity value were considered as vulnerable regions where abrupt shifts in albedo may occur if aridity increases, as forecasted by current climate change models. Our findings indicate that remotely sensed albedo can identify two ecosystem states in African drylands. They support the suitability of albedo indices to inform us about discontinuous responses to aridity experienced by drylands, which can be linked to the onset of land degradation. [ABSTRACT FROM AUTHOR]

The multiangular Rahman--Pinty--Verstraete modified (MRPV) semiempirical model uses three parameters (ρ0, Θ, and k) for describing the anisotropy of an arbitrary target. They have been usefully proved to characterize some forest attributes and land covers. However, there is no enough evaluation of the consistency of this product, and the possible affection from different factors in the reliability of them. Here, we explored the consistency of the MRPV parameters provided in the MISR L2 Land Surface (MIL2ASLS) product, with data from Mainland Spain, grouping MISR images into close time pairs. Thus, it was studied the three MRPV parameters through retrieving Spearman's rank correlation coefficient (ρ) and mean relative differences related to every pair of images. The results showed the ρ0 parameter presented higher consistency than the others, with ρ over 0.85 and meant relative differences around 15%. The k parameter showed ρ over 0.65 and average relative disagreements over 8%. Finally, the Θ parameter reached ρ around 0.60. The Θ mean differences were over 25% unless the combination of the blue band which was especially bad and its values were up to 50%. So, it is crucial having into account when the parameters of this product are used to look into the band and the own parameter. [ABSTRACT FROM AUTHOR]

In this study, we showed that the multi-angle satellite remote sensing product, MISR L2 Land Surface (MIL2ASLS), which has a scale of 1.1 km, could be suitable for improving land-cover studies. Using seven images from this product, captured by the multi-angle imaging spectroradiometer sensor (MISR), we explored the values reached by the three parameters (ρ0, Θ, and k) of the Rahman-Pinty-Verstraete model, which was modified by Martonchick (MRPV). Thereafter, we compared the values and behaviors shown in seven Co-ordination of Information on the Environment (CORINE) land cover categories, in the red and near infrared (NIR) bands, over the seven MISR orbits captured in 2006 for Mainland Spain. Furthermore, we used Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) ancillary data and the illumination angles from the same pixels, which made up the images. These ancillary data were also provided by the MISR products. An inferential statistic test was performed to evaluate the relationship between each parameter-band combination, and the land cover in every MISR orbit used. The results suggested that the ρ0 parameters of this product seemed to be the most related to photosynthetic activity, and it should be comparable with the widely-used NDVI. On the other hand, the k and Θ parameter values were not related, or at least not entirely related, to the phenology of land coverage. These seemed to be more influenced by the anisotropy behavior of the studied land cover pixels. Additionally, we observed, by constructing analysis of variance, how the mean of each MRPV parameter-band differed statistically (p < 0.01) by land covers and orbits. This study suggested that the MISR MRPV model parameter data product has great potential to be used to improve land cover applications. [ABSTRACT FROM AUTHOR]

The aim of the topographic normalization of remotely sensed imagery is to reduce reflectance variability caused by steep terrain and thus improve further processing of images. A process of topographic correction was applied to Landsat imagery in a mountainous forest area in the south of Mexico. The method used was the Sun Canopy Sensor + C correction (SCS + C) where the C parameter was differently determined according to a classification of the topographic slopes of the studied area in nine classes for each band, instead of using a single C parameter for each band. A comparative, visual, and numerical analysis of the normalized reflectance was performed based on the corrected images. The results showed that the correction by slope classification improves the elimination of the effect of shadows and relief, especially in steep slope areas, modifying the normalized reflectance values according to the combination of slope, aspect, and solar geometry, obtaining reflectance values more suitable than the correction by non-slope classification. The application of the proposed method can be generalized, improving its performance in forest mountainous areas. [ABSTRACT FROM AUTHOR]

Landslides are recognized as high-impact natural hazards in different regions around the world; therefore, they are extensively researched by experts. Landslide inventories are essential to identify areas that are likely to be affected in the future, thereby enabling interventions to prevent loss of life. Today, through combined approaches, such as remote sensing and machine learning techniques, it is possible to apply algorithms that use data derived from satellite images to produce landslide inventories. This work presents the performance of five machine learning methods—k-nearest neighbor (KNN), stochastic gradient descendent (SGD), support vector machine radial basis function (SVM RBF Kernel), support vector machine (SVM linear kernel), and AdaBoost—in landslide detection in a zone of the state of Guerrero in southern Mexico, using continuous change maps and primary landslide factors, such as slope angle, terrain orientation (aspect), and lithology, as inputs. The models were trained with 2/3 of ground truth samples of 671 slidden/non-slidden polygons. The obtained inventory maps were evaluated with the remaining 1/3 of ground truth samples by generating a confusion matrix and applying the Kappa concordance coefficient, accuracy, precision, recall, and F1 score as evaluation metrics, as well as omission and commission errors. According to the results, the AdaBoost classifier reached greater spatial and statistical coherence than the other implemented methods. The best input layer combination for detection was the continuous change maps obtained by the linear regression and image differencing detection methods, together with the slope angle, aspect, and lithology conditioning factors. [ABSTRACT FROM AUTHOR]

The temporal evolution of vegetation is one of the best indicators of climate change, and many earth system models are dependent on an accurate understanding of this process. However, the effect of climate change is expected to vary from one land-cover type to another, due to the change in vegetation and environmental conditions. Therefore, it is pertinent to understand the effect of climate change by land-cover type to understand the regions that are most vulnerable to climate change. Hence, in this study we analyzed the temporal statistical trends (2001–2016) of the MODIS13Q1 normalized difference vegetation index (NDVI) to explore whether there are differences, by land-cover class and phytoclimatic type, in mainland Spain and the Balearic Islands. We found 7.6% significant negative NDVI trends and 11.8% significant positive NDVI trends. Spatial patterns showed a non-random distribution. The Atlantic biogeographical region showed an unexpected 21% significant negative NDVI trends, and the Alpine region showed only 3.1% significant negative NDVI trends. We also found statistical differences between NDVI trends by land cover and phytoclimatic type. Variance explained by these variables was up to 35%. Positive trends were explained, above all, by land occupations, and negative trends were explained by phytoclimates. Warmer phytoclimatic classes of every general type and forest, as well as some agriculture land covers, showed negative trends. [ABSTRACT FROM AUTHOR]

Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work. [ABSTRACT FROM AUTHOR]

Currently, there exists growing evidence that warming is amplified with elevation resulting in rapid changes in temperature, humidity and water in mountainous areas. The latter might result in considerable damage to forest and agricultural land cover, affecting all the ecosystem services and the socio-economic development that these mountain areas provide. The Mediterranean mountains, moreover, which host a high diversity of natural species, are more vulnerable to global change than other European ecosystems. The protected areas of the mountain ranges of peninsular Spain could help preserve natural resources and landscapes, as well as promote scientific research and the sustainable development of local populations. The temporal statistical trends (2001–2016) of the MODIS13Q1 Normalized Difference Vegetation Index (NDVI) interannual dynamics are analyzed to explore whether the NDVI trends are found uniformly within the mountain ranges of mainland Spain (altitude > 1000 m), as well as in the protected or non-protected mountain areas. Second, to determine if there exists a statistical association between finding an NDVI trend and the specific mountain ranges, protected or unprotected areas are studied. Third, a possible association between cover types in pure pixels using CORINE (Co-ordination of Information on the Environment) land cover cartography is studied and land cover changes between 2000 and 2006 and between 2006 and 2012 are calculated for each mountainous area. Higher areas are observed to have more positive NDVI trends than negative in mountain areas located in mainland Spain during the 2001–2016 period. The growing of vegetation, therefore, was greater than its decrease in the study area. Moreover, differences in the size of the area between growth and depletion of vegetation patterns along the different mountains are found. Notably, more negatives than expected are found, and fewer positives are found than anticipated in the mountains, such as the Cordillera Cantábrica (C.Cant.) or Montes de Murcia y Alicante (M.M.A). Quite the reverse happened in Pirineos (Pir.) and Montes de Cádiz y Málaga (M.C.M.), among others. The statistical association between the trends found and the land cover types is also observed. The differences observed can be explained since the mountain ranges in this study are defined by climate, land cover, human usage and, to a small degree, by land cover changes, but further detailed research is needed to get in-depth detailed conclusions. Conversely, it is found that, in protected mountain areas, a lower NDVI pixels trend than expected (>20%) occurs, whereas it is less than anticipated in unprotected mountain areas. This could be caused by management and the land cover type. [ABSTRACT FROM AUTHOR]

In this study, we deal with the application of multiangular data from the Multiangle Imaging Spectroradiometer (MISR) sensor for studying the effect of surface anisotropy and directional information on the classification accuracy for different land covers with different rate of disaggregation classes (from four to 35 different classes) from a Mediterranean bioregion in Iberian, Spain. We used various MISR band groups from nadir to blue, green, red, and NIR channels at nadir and off-nadir. The MISR data utilized here were provided by the L1B2T product (275 m spatial resolution) and belonged to two different orbits. We performed 23 classifications with the k-means algorithm to test multiangular data, number of clusters, and iteration effects. Our findings confirmed that the multiangular information, in addition to the multispectral information used as the input of the k-means algorithm, improves the land cover classification accuracy, and this improvement increased with the level of disaggregation. A very large number of clusters produced even better improvements than multiangular data. [ABSTRACT FROM AUTHOR]