•14% of deforestation was found in new residential areas of the city.•Deforestation and heat islands were spatially related.•Deforestation was an indicator of land surface temperature.
With ...1.76 million inhabitants, Mérida city has recently become the most populated city in southeast Mexico, being one of the most attractive cities for investment in real-estate projects. This has increased deforestation to the periphery, resulting in heat islands. We used time series of Landsat images and the BFAST algorithm to analyze annual deforestation around Mérida city over the 2000–2018 period. Land surface temperature was also estimated using Landsat images to compare temperatures before and after deforestation and examine heat islands in the city. The deforestation maps had a 96.82% overall accuracy on average, user’s and producer’s accuracy values of 91.62% and 95.46%, respectively, and an estimated total deforested area of 5413 ha over the study period. Land surface temperature increased in 2.36–3.94 °C after deforestation, and heat islands of varying intensity were detected in 80% of the urban territory, mainly where deforestation occurred. Our results demonstrate the effectiveness of Landsat images and the BFAST algorithm for detecting deforestation in peri-urban spaces, as well as the usability of Landsat images for estimating land surface temperature. These images are effective tools for urban land-use planning and for examining the formation of heat islands in tropical cities.
Research on tropical dry forest (TDF) succession i0s needed for effective conservation and management of this threatened and understudied ecosystem. We used a highly replicated chronosequence within ...a 37,242‐ha TDF landscape to investigate successional patterns by plant size class and to evaluate the influence of stand age, topographic position, soil properties and spatial autocorrelation on forest structure and composition. We used a SPOT5 satellite image to obtain a land‐cover thematic map, and sampled woody vegetation (adults: >5 cm diam; saplings: 1–5 cm) and soil properties in 168 plots distributed among four vegetation classes: VC1 (3–8‐yr‐old forest), VC2 (9–15‐yr‐old forest), VC3 (>15‐yr‐old forest on flat areas), VC4 (>15‐yr‐old forest on hills). Stem density decreased with stand age and was lowest in VC3, while height, basal area and species density increased with age and were higher in older than in younger forests. Topographic position also influenced forest structure and composition. Basal area and height were largely determined by stand age, whereas stem and species density, and composition were influenced mostly by soil variables associated with fertility, and by spatial autocorrelation. Adults and saplings showed contrasting patterns and correlates of community structure, but similar patterns and correlates of composition, possibly due to the prevalence of coppicing. Our results show that our sampling approach can overcome several limitations of chronosequence studies, and provide insights in the patterns and drivers of succession, as well as guidelines for forest management and conservation. Abstract in Spanish is available at http://www.blackwell‐synergy.com/loi/btp.
Climate change has severe consequences on ecosystem processes, as well as on people’s quality of life. It has been suggested that the loss of vegetation cover increases the land surface temperature ...(LST) due to modifications in biogeochemical patterns, generating a phenomenon known as “urban heat island” (UHI). The aim of this work was to analyze the effects of urban land-cover changes on the spatiotemporal variation of surface temperature in the tropical city of Mérida, Mexico. To find these effects we used both detected land-cover changes as well as variations of the Normalized Difference Vegetation Index (NDVI). Mérida is ranked worldwide as one of the best cities to live due to its quality of life. Data from satellite images of Landsat were analyzed to calculate land use change (LUC), LST, and NDVI. LST increased ca. 4 °C in the dry season and 3 °C in the wet season because of the LUC. In addition, a positive relationship between the LST and the NDVI was observed mainly in the dry season. The results confirm an increase in the LST as a consequence of the loss of vegetation cover, which favors the urban heat island phenomenon.
Biodiversity conservation and ecosystem-service provision will increasingly depend on the existence of secondary vegetation. Our success in achieving these goals will be determined by our ability to ...accurately estimate the structure and diversity of such communities at broad geographic scales. We examined whether the texture (the spatial variation of the image elements) of very high-resolution satellite imagery can be used for this purpose. In 14 fallows of different ages and one mature forest stand in a seasonally dry tropical forest landscape, we estimated basal area, canopy cover, stem density, species richness, Shannon index, Simpson index, and canopy height. The first six attributes were also estimated for a subset comprising the tallest plants. We calculated 40 texture variables based on the red and the near infrared bands, and EVI and NDVI, and selected the best-fit linear models describing each vegetation attribute based on them. Basal area (R(2) = 0.93), vegetation height and cover (0.89), species richness (0.87), and stand age (0.85) were the best-described attributes by two-variable models. Cross validation showed that these models had a high predictive power, and most estimated vegetation attributes were highly accurate. The success of this simple method (a single image was used and the models were linear and included very few variables) rests on the principle that image texture reflects the internal heterogeneity of successional vegetation at the proper scale. The vegetation attributes best predicted by texture are relevant in the face of two of the gravest threats to biosphere integrity: climate change and biodiversity loss. By providing reliable basal area and fallow-age estimates, image-texture analysis allows for the assessment of carbon sequestration and diversity loss rates. New and exciting research avenues open by simplifying the analysis of the extent and complexity of successional vegetation through the spatial variation of its spectral information.
Soil resistance and recovery during neotropical forest succession van der Sande, Masha T; Powers, Jennifer S; Kuyper, Thom W ...
Philosophical transactions of the Royal Society of London. Series B. Biological sciences,
01/2023, Letnik:
378, Številka:
1867
Journal Article
Recenzirano
Odprti dostop
The recovery of soil conditions is crucial for successful ecosystem restoration and, hence, for achieving the goals of the UN Decade on Ecosystem Restoration. Here, we assess how soils resist forest ...conversion and agricultural land use, and how soils recover during subsequent tropical forest succession on abandoned agricultural fields. Our overarching question is how soil resistance and recovery depend on local conditions such as climate, soil type and land-use history. For 300 plots in 21 sites across the Neotropics, we used a chronosequence approach in which we sampled soils from two depths in old-growth forests, agricultural fields (i.e. crop fields and pastures), and secondary forests that differ in age (1-95 years) since abandonment. We measured six soil properties using a standardized sampling design and laboratory analyses. Soil resistance strongly depended on local conditions. Croplands and sites on high-activity clay (i.e. high fertility) show strong increases in bulk density and decreases in pH, carbon (C) and nitrogen (N) during deforestation and subsequent agricultural use. Resistance is lower in such sites probably because of a sharp decline in fine root biomass in croplands in the upper soil layers, and a decline in litter input from formerly productive old-growth forest (on high-activity clays). Soil recovery also strongly depended on local conditions. During forest succession, high-activity clays and croplands decreased most strongly in bulk density and increased in C and N, possibly because of strongly compacted soils with low C and N after cropland abandonment, and because of rapid vegetation recovery in high-activity clays leading to greater fine root growth and litter input. Furthermore, sites at low precipitation decreased in pH, whereas sites at high precipitation increased in N and decreased in C : N ratio. Extractable phosphorus (P) did not recover during succession, suggesting increased P limitation as forests age. These results indicate that no single solution exists for effective soil restoration and that local site conditions should determine the restoration strategies. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.
Many geospatial tools have been advocated in spatial ecology to estimate biodiversity and its changes over space and time. Such information is essential in designing effective strategies for ...biodiversity conservation and management. Remote sensing is one of the most powerful approaches to identify biodiversity hotspots and predict changes in species composition in reduced time and costs. This is because, with respect to field-based methods, it allows to derive complete spatial coverages of the Earth surface under study in a short period of time. Furthermore, remote sensing provides repeated coverages of field sites, thus making studies of temporal changes in biodiversity possible. In this paper we discuss, from a conceptual point of view, the potential of remote sensing in estimating biodiversity using various diversity indices, including alpha- and beta-diversity measurements.
•Many geospatial tools have been advocated in spatial ecology to estimate biodiversity and its changes over space and time.•Remote sensing is one of the most powerful methods to identify biodiversity hotspots.•In this paper we discuss, from a conceptual point of view, the potential of remote sensing in estimating biodiversity.
Aim
Optical satellite imagery has been used for mapping the spatial distribution of vegetation structure attributes; however, obtaining accurate estimates with optical imagery can be difficult in ...tropical forests due to their dense canopy and multi‐layered vegetation. Synthetic aperture radar imagery can be more suitable in this case, as the radar signal can penetrate the forest canopy and interact with stems, providing a better estimation of the vegetation structure. This study compared the accuracy of forest species richness, tree diameter, height, and basal area estimates obtained using Sentinel‐2 and Advanced Land Observing Satellite ‐1 (ALOS) Phased Array type L‐band Synthetic Aperture Radar (PALSAR) data, either combined or separately.
Location
The Yucatan Peninsula, Mexico.
Methods
Field data were collected in three 3600‐km2‐window areas with three different types of tropical dry forest. Three random forest regression models were fitted: one using explanatory variables derived from Sentinel‐2 data, a second using predictor variables derived from ALOS PALSAR, and the third using a combination of explanatory variables from both sensors. A variance partitioning analysis was carried out to examine the percent variability of each vegetation attribute that was explained by the models combining the explanatory variables of the two sensors (ALOS PALSAR and Sentinel‐2).
Results
Vegetation attribute estimation errors ranged from 13% to 38.5% when using ALOS PALSAR variables and from 11% to 33% when using Sentinel‐2 variables. Combining variables from both sensors provided more accurate estimates of vegetation attributes. A 5% reduction of the estimated error, and an increase from 0.50 to 0.63 of the percentage of variation explained by the models (R2) were achieved.
Conclusions
Our results suggest that both ALOS PALSAR and Sentinel‐2 data provide accurate estimates of vegetation structure and species richness in tropical dry forests. However, combining explanatory variables from the two sensors improved the estimation accuracy of vegetation attributes.
This study mapped the spatial distribution of vegetation structure and species richness in three different ecosystems of the Yucatan Peninsula. Here we relate national forest inventory data obtained with Sentinel‐2 and ALOS PALSAR, either combined or separately, using random forest regression models. Our results suggest that combining explanatory variables from the two sensors improved the estimation accuracy of vegetation attributes.
The aim of this study was to evaluate the relative contributions of the environment, landscape patterns, and spatial structure to explaining the variation in richness of rare woody species at three ...levels of rarity (low, medium, and high) and at different grain sizes and spatial extents. We used herbarium records of 195 rare woody species to quantify species richness—overall and for three levels of rarity—of the Yucatan Peninsula, Mexico. We assessed relationships between rare species richness and different sets of explanatory variables (environmental, landscape patterns, and spatial structure of sampling units) using linear regression and variation partitioning analyses at three grain sizes (625, 400, and 225 km
2
). We also conducted a principle coordinates of neighbor matrices analysis to allow interpretation of the results in terms of different spatial extents. The percentage of variation in rare species richness explained by the models was highest for the largest grain size and spatial extent. At the larger extents, rare species richness was explained mainly by the environment, whereas landscape patterns played a more prominent role at the local extent. Landscape patterns also contributed more to explaining species richness at low to medium levels of rarity, whereas the richness of extremely rare species was better explained by spatial structure. We conclude that the relative contribution of the factors explaining the variation of rare species richness depends on both grain and extent, as well as on the level of rarity. These results underscore the importance of considering the different components of scale (grain and extent) as well as different levels of species rarity in order to better understand the patterns of distribution of rare species richness and to be able to frame appropriate conservation strategies.
The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for ...supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure.
Tree beta-diversity denotes the variation in species composition at stand level, it is a key indicator of forest degradation, and is conjointly required with alpha-diversity for management decision ...making but has seldom been considered. Our aim was to map it in a continuous way with remote sensing technologies over a tropical landscape with different disturbance histories. We extracted a floristic gradient of dissimilarity through a non-metric multidimensional scaling ordination based on the ecological importance value of each species, which showed sensitivity to different land use history through significant differences in the gradient scores between the disturbances. After finding strong correlations between the floristic gradient and the rapidEye multispectral textures and LiDAR-derived variables, it was linearly regressed against them; variable selection was performed by fitting mixed-effect models. The redEdge band mean, the Canopy Height Model, and the infrared band variance explained 68% of its spatial variability, each coefficient with a relative importance of 49%, 32.5%, and 18.5% respectively. Our results confirmed the synergic use of LiDAR and multispectral sensors to map tree beta-diversity at stand level. This approach can be used, combined with ground data, to detect effects (either negative or positive) of management practices or natural disturbances on tree species composition.
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