An analysis of the drivers that affect greening and browning trends in the context of pursuing land degradation-neutrality

Gichenje, Helene; Pinto‐Correia, Teresa; Godinho, Sérgio

doi:10.1016/j.rsase.2019.100251

Cited by 9 publications

(14 citation statements)

References 32 publications

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“…The relationship between the predictors and the spatial distribution of Cabrera vole was evaluated in a three-step statistical approach. The first step consisted in selecting the relevant variables from a set of 67 candidate predictors using the Boruta algorithm [44,78,79]. Basically, Boruta algorithm relies on an extension of the random forest (RF) [80,81] method by introducing an iterative procedure to compare the relative importance of the original variables with the importance of their randomized copies [44].…”

Section: Habitat Suitability Modelmentioning

confidence: 99%

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

et al. 2020

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Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao's Q) were good proxies of Cabrera voles' microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles' distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes.

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Section: Habitat Suitability Modelmentioning

confidence: 99%

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

et al. 2020

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“…The share of greening and browning trends within the agriculture and forest layers (at a 300-m resolution), as well as the number of observations, are presented in Table 1. • Explanatory variables: On the basis of the study undertaken by [28] on the analysis of the drivers that affect greening and browning trends in Kenya, the same dataset of 28 explanatory variables (broadly grouped into natural and anthropogenic variables) were used to identify the key drivers affecting greening and browning trends in the LVWC. A full description of the explanatory variables, the sources of data, and the SDG each variable most closely represents are contained in [28].…”

Section: Drivers Of Greening and Browning Trendsmentioning

confidence: 99%

“…Following [28], the methodological approach used to identify the key drivers of greening and browning trends in the LVWC was the random forest (RF) machine learning algorithm. The methodological steps, outputs (variable importance (VI) plots using the mean decrease in accuracy (MDA) measure; and relative importance plots by SDG group), and performance metrics (accuracy; and Kappa) are described in [28]. However, the current study differs from the [28] study in the method used to balance the data.…”

Section: Drivers Of Greening and Browning Trendsmentioning

confidence: 99%

“…The methodological steps, outputs (variable importance (VI) plots using the mean decrease in accuracy (MDA) measure; and relative importance plots by SDG group), and performance metrics (accuracy; and Kappa) are described in [28]. However, the current study differs from the [28] study in the method used to balance the data. As indicated in Table 1, browning trends are predominant (more than 90%) in agriculture and forest areas.…”

Section: Drivers Of Greening and Browning Trendsmentioning

confidence: 99%

“…For the agriculture dataset, none of the 28 explanatory variables were rejected from the Boruta procedure (i.e., step 1 of the methodology as described in [28]. However, for the forest dataset, the variable night-time lights was rejected from the Boruta procedure.…”

Section: Drivers Of Greening and Browning Trends And Comparison With mentioning

confidence: 99%

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A Climate-Smart Approach to the Implementation of Land Degradation Neutrality within a Water Catchment Area in Kenya

Gichenje

Godinho

2019

Climate

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At the sub-national level, the United Nations Convention to Combat Desertification (UNCCD) proposes the analysis and contextualization of land degradation-neutrality (LDN) at a water catchment scale to provide decision support for the formulation of policies and programmes towards transformative LDN interventions. Building on a number of national LDN studies in Kenya, an approach for the implementation of LDN that is based on the spatial and temporal characterization of key land degradation and climate change variables was defined. For a selected water catchment area, the LDN baseline was computed, the drivers that affect land degradation and regeneration trends within the main land cover types were identified and described, the trends of key climate change variables were described, and appropriate sustainable land management interventions for the main land cover types were identified. A climate-smart landscape approach that delineated the catchment area into zones focused on adaptation, and both adaptation and mitigation objectives was then proposed. The operationalization of a climate-smart landscape will require significant investment to not only provide an understanding of the bio-physical processes and interactions occurring at the catchment level but also to develop the institutional and technical capacities of relevant actors. The landscape approach proposed for the catchment area has the potential to improve livelihoods and the productivity of ecosystems while concurrently facilitating synergies between land degradation, climate change, and other development objectives.

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From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions

et al. 2020

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Remote sensing observations such as normalized difference vegetation index (NDVI) trends can provide important insights into past and present land condition. However, they do not directly provide comprehensive information about our representation of land degradation and the processes at work. This study aimed to analyze vegetation productivity underlying factors in order to assess land degradation and to highlight the impact of definitions on its quantitative assessment, using Mozambique as casestudy. Land productivity change were first analyzed using NDVI time-series (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016), and a two-step framework was then used to understand the main factors of these productivity changes. The impact of land degradation's definition was assessed based on four types of stakeholder, with different priorities in terms of ecosystem services. The results show that 25% of the country display a significant land productivity decrease, while only 3% display a land productivity increase. A large part of these land productivity changes (>61% of the decrease, and >98% of the increase) is directly assigned to human activities, such as native forest growth or tree plantations (for the increase), or forest degradation, deforestation and loss of grassland productivity (for the decrease). We showed that the fraction of degraded land varies according to stakeholders' definitions, ranging from 12% to 20% of the Country, much less than the 39% estimated by Tier 1 United Nations Convention to Combat Desertification. This study provides a sound methodological framework for assessing land degradation status that could help stakeholders to design national and locally relevant land degradation mitigation policies or programmes.

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An analysis of the drivers that affect greening and browning trends in the context of pursuing land degradation-neutrality

Cited by 9 publications

References 32 publications

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

A Climate-Smart Approach to the Implementation of Land Degradation Neutrality within a Water Catchment Area in Kenya

From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions

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