Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allows information to be gathered over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (β‐diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript, we propose novel techniques to measure β‐diversity from airborne or satellite remote sensing, mainly based on: (1) multivariate statistical analysis, (2) the spectral species concept, (3) self‐organizing feature maps, (4) multidimensional distance matrices, and the (5) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating β‐diversity from remotely sensed imagery and potentially relating them to species diversity in the field.

Abstract:Montado decline has been reported since the end of the nineteenth century in southern Portugal and increased markedly during the 1980s. Consensual reports in the literature suggest that this decline is due to a number of factors, such as environmental constraints, forest diseases, inappropriate management, and socioeconomic issues. An assessment on the pattern of montado distribution was conducted to reveal how the extent of land management, environmental variables, and spatial factors contributed to montado area loss in southern Portugal from 1990 to 2006. A total of 14 independent variables, presumably related to montado loss, were grouped into three sets: environmental variables, land management variables, and spatial variables. From 1990 to 2006, approximately 90,054 ha disappeared in the montado area with an estimated annual regression rate of 0.14 % year-1. Variation partitioning showed that the land management model accounted for the highest percentage of explained variance (51.8 %), followed by spatial factors (44.6 %) and environmental factors (35.5 %). These results indicate that most variance in the large-scale distribution of recent montado loss is due to land management, either alone or in combination with environmental and spatial factors. The full GAM model showed that different livestock grazing is one of the most important variables affecting montado loss. This suggests that optimum carrying capacity should decrease to 0.18-0.60 LU ha-1 for livestock grazing in montado under current ecological conditions in southern Portugal. This study also showed that land abandonment, wildfire, and agricultural practices (to promote pastures, crops or fallow lands) were three significant variables influencing montado loss.

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The land degradation-neutrality (LDN) national baseline for Kenya in 2015 was established in terms of the three LDN indicators (land cover, land productivity, and carbon stocks), and using trends in GIMMS NDVI and land cover datasets over the 24-year period from 1992 to 2015. Human-induced land degradation was separated from degradation driven by climate factors using soil moisture data and the residual trend method. On the basis of Kendall's tau of the NDVI residuals computed using annual mean data of the NDVI and soil moisture relationship, the country has experienced persistent negative trends (browning) over 21.6% of the country, and persistent positive trends (greening) in 8.9% of the country. The land cover change map for the period 1992-2015 showed that in 5.6% of the area there was a change from one land cover class to another. Pronounced changes in terms of land area were the increase in grasslands by 12,171 km 2 , the decrease of bare land by 9,877 km 2 , and the decrease in forests by 7,182 km 2 . Browning and greening trends account for 13% and 12%, respectively, of the land cover change areas. By establishing the LDN national baseline, the LDN concept is now operational. As a first step, targeted field level assessments, alongside the collection of data for the computation of soil organic carbon stocks, should be undertaken in selected browning, greening, and land cover change sites. These field studies will provide decision makers with key information on how to plan for the implementation and monitoring of LDN interventions.

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