The effect of highly variable topography on the spatial distribution of &lt;i&gt;Aniba perutilis&lt;/i&gt; (Lauraceae) in the Colombian Andes

Fagua, J. Camilo; Cabrera, Edersson; González, Víctor H.

doi:10.15517/rbt.v61i1.11129

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…Terrain features are potentially related to key features for forest stand development, such as overall climate characteristics, insolation, evapotranspiration, run-off, infiltration, wind exposure and site productivity [16,21]. Climatic and topographic variables can also affect aboveground biomass through their effects on species composition distribution and the interaction of forest species growth with different site conditions [28,30,72]. For example, Gallaun et al [84] found in Europe that conifers had higher stocks at high altitude while the contrary occurred with broadleaves.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, topographic variables represent a local heterogeneous factor that influence on forest diversity, distribution and development, directly influencing forest productivity and structure [28]. The combination of spectral and topographic variables, such as elevation [12,29], slope and aspect [22,30], or topographic wetness indices [23] is, therefore, potentially useful for improving the mapping of forest productivity at a landscape scale.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

López-Serrano

Domínguez

Corral-Rivas

et al. 2019

Forests

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An accurate estimation of forests’ aboveground biomass (AGB) is required because of its relevance to the carbon cycle, and because of its economic and ecological importance. The selection of appropriate variables from satellite information and physical variables is important for precise AGB prediction mapping. Because of the complex relationships for AGB prediction, non-parametric machine-learning techniques represent potentially useful techniques for AGB estimation, but their use and comparison in forest remote-sensing applications is still relatively limited. The objective of the present study was to evaluate the performance of automatic learning techniques, support vector regression (SVR) and random forest (RF), to predict the observed AGB (from 318 permanent sampling plots) from the Landsat 8 Landsat 8 Operational Land Imager (OLI) sensor, spectral indexes, texture indexes and physical variables the Sierra Madre Occidental in Mexico. The result showed that the best SVR model explained 80% of the total variance (root mean square error (RMSE) = 8.20 Mg ha−1). The variables that best predicted AGB, in order of importance, were the bands that belong to the region of red and near and middle infrared, and the average temperature. The results show that the SVR technique has a good potential for the estimation of the AGB and that the selection of the model hyperparameters has important implications for optimizing the goodness of fit.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

López-Serrano

Domínguez

Corral-Rivas

et al. 2019

Forests

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“…Also, topographic slope was negatively and indirectly correlated to hotspots of deforestation; some scholars have found that places with abrupt topography tend to not be deforested in tropical areas due to the physical restrictions on the establishment of agriculture and grazing operations, as well as difficult access to markets for agricultural products (Coblentz and Keating , Fagua et al. , Sandel and Svenning ).…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, the negative and indirect relationship between annual temperature and deforestation hotspots in our study area could occur due to the limitations imposed on farming activities by high temperatures, high rainfall, and soil degradation of the northern and southern portions of the CGE. Also, topographic slope was negatively and indirectly correlated to hotspots of deforestation; some scholars have found that places with abrupt topography tend to not be deforested in tropical areas due to the physical restrictions on the establishment of agriculture and grazing operations, as well as difficult access to markets for agricultural products (Coblentz and Keating 2008, Fagua et al 2013, Sandel and Svenning 2013.…”

Section: Deforestationmentioning

confidence: 99%

Drivers of forest cover changes in the Chocó‐Darien Global Ecoregion of South America

2019

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Tropical rain forests are suffering the highest deforestation and reforestation ever recorded. Interactions between direct (proximate or direct causes) and indirect (underling or indirect causes) drivers could cluster these forest cover changes forming hotspots (areas that exhibit significant spatial correlation of deforestation or reforestation transitions). Using land use–land cover maps and global (I) and local (Ii) Moran's tests, we identified these hotspots in the Chocó‐Darien Global Ecoregion (CGE) of South America, a natural region that was declared one of the top 25 hotspots for conservation priorities in the world. Subsequently, we tested and studied the effects and interactions between deforestation and reforestation hotspots and their direct and indirect drivers using Bayesian Structural Equation Modeling (Bayesian SEM). We found that deforestation and reforestation were spatially auto‐correlated forming hotspots (I = 0.49, P = 0.001 for deforestation transitions and I = 0.48, P = 0.001 for reforestation transitions). Also, hotspots of deforestation and reforestation were auto‐correlated within municipality borders (I = 0.5, P = 0.001 for deforestation transitions; I = 0.49, P = 0.001 for reforestation transitions). Eighteen municipalities located on the border between Colombia and Ecuador showed significant aggregations of deforestation hotspots, while thirty‐four municipalities in three areas of Colombia and the area between the Colombian and Ecuadorian border showed significant clustering of reforestation hotspots. Eleven of these municipalities presented significant clustering of both reforestation and deforestation hotspots. The Bayesian SEM for deforestation showed that population growth and road density were indirect drivers of deforestation hotspots (0.191 and 0.127 standard deviation units). The Bayesian SEM for reforestation found that armed conflicts, Gross Domestic Product, and average annual rain were indirect drivers related to reforestation hotspots (0.228, 0.076, and 0.081 standard deviation units, respectively). Our assessment shows a novel methodology to study interactions among direct and indirect drivers of forest change and their potential dissimilar effects on forest transitions.

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“…Only few studies have previously analyzed the relationship between environmental variables and species distribution in steep tropical mountain forests at local scales (SvenninG et al 2009;JoneS et al 2011;aiba et al 2004;Homeier et al 2010;leDo et al 2013;FaGua et al 2013;balDecK et al 2013). In these studies, topographic variables (e.g.…”

Section: Introductionmentioning

confidence: 99%

Assessing the importance of topographic variables for the spatial distribution of tree species in a tropical mountain forest

Kübler¹,

Hildebrandt²,

Günter³

et al. 2015

Erdkunde

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Availability and improved access to high-resolution digital terrain models (DTM) enables new approaches for the analysis of spatially explicit biological data. In this study, the spatial distribution of 16 tree species in a tropical mountain rain forest in South Ecuador and its relationship with topographic variables was evaluated at a fine-scale ecological level using two presence-only species distribution modelling techniques: The maximum entropy model (Maxent) and the ecological niche factor analysis (ENFA). Spatially explicit tree data stem from long-term forest monitoring plots in three microcatchments with a total area of 11.1 ha. Topographic variables were derived from a high-resolution DTM. Model performance was assessed by the true skill statistic (TSS) and area under curve (AUC) of the receiver operator characteristic (ROC), using both a k-fold approach and null-models. Performance varied among species and techniques, but generally Maxent models showed better performance than ENFA models. Furthermore, the ecological plausibility of the models was confirmed by comparing them with a previously established forest type classification. Among the explanatory topographic variables, elevation and a Topographic Position Index (TPI) appear as the main determinants for the distribution of most of the tree species. This study demonstrates that even on a small scale, the use of presence-only species distribution modelling techniques is a viable option for modelling suitable habitat for tree species in tropical mountain rain forests, indicating suitability for supporting stand-level planning and site-species matching techniques for natural forest management. Zusammenfassung: Die zunehmende Verfügbarkeit von hochauflösenden digitalen Geländemodellen ermöglicht neue Ansätze zur Analyse von räumlich expliziten biologischen Daten. In dieser Studie haben wir die räumliche Verteilung von 16 Baumarten und deren Zusammenhang mit topographischen Variablen auf einer feinskaligen ökologischen Ebene in einem tropischen Bergregenwald in Südecuador evaluiert. Dafür haben wir zwei auf Präsenzdaten basierende Techniken zur Habitatmodellierung angewandt: Die Maximum-Entropie Methode (Maxent) und "Ecological Niche Factor Analysis" (ENFA). Räumlich explizite Baumdaten stammen aus Langzeitbeobachtungsflächen von drei kleinen Wassereinzugsgebieten mit einer Fläche von 11.1 ha. Topographische Variablen wurden aus einem hochauflösenden digitalen Geländemodel abgeleitet. Zur Bewertung der Modelle wurden die True Skill Statistic (TSS) und die Fläche unterhalb der Kurve der Receiver Operating Characteristic (ROC) für sowohl k-fache Kreuzvalidierungen als auch Null-Modelle berechnet. Die Güte der Modelle hat zwischen den beiden Techniken und verschiedenen Arten variiert, aber generell schnitten Maxent-Modelle besser als ENFA-Modelle ab. Des Weiteren haben wir die ökologische Plausibilität der Modelle überprüft, indem wir sie mit einer bereits bestehenden Waldtypenklassifizierung verglichen haben. Unter den erklärenden topographischen Variablen...

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The effect of highly variable topography on the spatial distribution of <i>Aniba perutilis</i> (Lauraceae) in the Colombian Andes

Cited by 7 publications

References 21 publications

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

Drivers of forest cover changes in the Chocó‐Darien Global Ecoregion of South America

Assessing the importance of topographic variables for the spatial distribution of tree species in a tropical mountain forest

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