Land‐use change outweighs projected effects of changing rainfall on tree cover in sub‐Saharan Africa

Global change will likely affect savanna and forest structure and distributions, with implications for diversity within both biomes. Few studies have examined the impacts of both expected precipitation and land use changes on vegetation structure in the future, despite their likely severity. Here, we modeled tree cover in sub‐Saharan Africa, as a proxy for vegetation structure and land cover change, using climatic, edaphic, and anthropic data (R² = 0.97). Projected tree cover for the year 2070, simulated using scenarios that include climate and land use projections, generally decreased, both in forest and savanna, although the directionality of changes varied locally. The main driver of tree cover changes was land use change; the effects of precipitation change were minor by comparison. Interestingly, carbon emissions mitigation via increasing biofuels production resulted in decreases in tree cover, more severe than scenarios with more intense precipitation change, especially within savannas. Evaluation of tree cover change against protected area extent at the WWF Ecoregion scale suggested areas of high biodiversity and ecosystem services concern. Those forests most vulnerable to large decreases in tree cover were also highly protected, potentially buffering the effects of global change. Meanwhile, savannas, especially where they immediately bordered forests (e.g. West and Central Africa), were characterized by a dearth of protected areas, making them highly vulnerable. Savanna must become an explicit policy priority in the face of climate and land use change if conservation and livelihoods are to remain viable into the next century.     

Expansion of gallery forests into central Brazilian savannas

Upland tropical forests have expanded and contracted in response to past climates, but it is not clear whether similar dynamics were exhibited by gallery (riparian) forests within savanna biomes. Because such forests generally have access to ample water, their extent may be buffered against changing climates. We tested the long‐term stability of gallery forest boundaries by characterizing the border between gallery forests and savannas and tracing the presence of gallery forest through isotopic analysis of organic carbon in the soil profile. We measured leaf area index, grass vs. shrub or tree coverage, the organic carbon, phosphorus, nitrogen and calcium concentrations in soils and the carbon isotope ratios of soil organic matter in two transitions spanning gallery forests and savanna in a Cerrado ecosystem. Gallery forests without grasses typically show a greater leaf area index in contrast to savannas, which show dense grass coverage. Soils of gallery forests have significantly greater concentrations of organic carbon, phosphorus, nitrogen and calcium than those of savannas. Soil organic carbon of savannas is significantly more enriched in ¹³C compared with that of gallery forests. This difference in enrichment is in part caused by the presence of C₄ grasses in savanna ecosystem and its absence in gallery forests. Using the ¹³C abundance as a signature for savanna and gallery forest ecosystems in 1 m soil cores, we show that the borders of gallery forests have expanded into the savanna and that this process initiated at least 3000–4000 bp based on ¹⁴C analysis. Gallery forests, however, may be still expanding as we found more recent transitions according to ¹⁴C activity measurements. We discuss the possible mechanisms of gallery forest expansion and the means by which nutrients required for the expansion of gallery forest might accumulate.     

Herbaceous vegetation responses to experimental fire in savannas and forests depend on biome and climate

Fire–vegetation feedbacks potentially maintain global savanna and forest distributions. Accordingly, vegetation in savanna and forest ecosystems should have differential responses to fire, but fire response data for herbaceous vegetation have yet to be synthesized across biomes. Here, we examined herbaceous vegetation responses to experimental fire at 30 sites spanning four continents. Across a variety of metrics, herbaceous vegetation increased in abundance where fire was applied, with larger responses to fire in wetter and in cooler and/or less seasonal systems. Compared to forests, savannas were associated with a 4.8 (±0.4) times larger difference in herbaceous vegetation abundance for burned versus unburned plots. In particular, grass cover decreased with fire exclusion in savannas, largely via decreases in C₄ grass cover, whereas changes in fire frequency had a relatively weak effect on grass cover in forests. These differential responses underscore the importance of fire for maintaining the vegetation structure of savannas and forests.     

Application of machine learning approaches for land cover monitoring in northern Cameroon

Machine learning (ML) models are a leading analytical technique used to monitor, map and quantify land use and land cover (LULC) and its change over time. Models such as k-nearest neighbour (kNN), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF) have been used effectively to classify LULC types at a range of geographical scales. However, ML models have not been widely applied in African tropical regions due to methodological challenges that arise from relying on the coarse-resolution satellite images available for these areas. In this study, we compared the performance of four ML algorithms (kNN, SVM, ANN and RF) applied to LULC monitoring within the Mayo Rey department, North Province, Cameroon. We used satellite data from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) combined with 8 Operational Land Imager (OLI) images of northern Cameroon for November 2000 and November 2020. Our results showed that all four classification algorithms produced relatively high accuracy (overall classification accuracy >80%), with the RF model (> 90% classification accuracy) outperforming the kNN, SVM, and ANN models. We found that approximately 7% of all forested areas (dense forest and woody savanna) were converted to other land cover types between 2000 and 2020; this forest loss is particularly associated with an expansion of both croplands and built-up areas. Our study represents a novel application and comparison of statistical and ML approaches to LULC monitoring using coarse-resolution satellite images in an African tropical forest and savanna setting. The resulting land cover maps serve as an important baseline that will be useful to the Cameroon government for policy development, conservation planning, urban planning, and deforestation and agricultural monitoring.     

Numeric classification as an aid to spectral mapping of vegetation communities

This study demonstrates a vegetation mapping methodology that relates the reflectance information contained in multispectral imagery to traditionally accepted ecological classifications. Key elements of the approach used are (a) the use of cover rather than density or presence/absence to quantify the vegetation, (b) the inclusion of physical components as well as vegetation cover to describe and classify field sites, (c) development of an objective land cover classification from this quantitative data, (d) use of the field sample sites as training areas for the spectral classification, and (e) the use of a discriminant function to effectively tie the two classifications together. Land cover over 39000 ha of Australian chenopod shrubland was classified into nine groups using agglomerative hierarchical clustering, a discriminant function developed to relate cover and spectral classes, and the vegetation mapped using a maximum likelihood classification of multi-date Landsat TM imagery. The accuracy of the mapping was assessed with an independent set of field samples and by comparison with a map of land systems previously interpreted from aerial photography. Overall agreement between the digital classification and the land system map was good. The units that have been mapped are those derived from numeric vegetation classification, demonstrating that accepted ecological methods and sound image analysis can be successfully combined.     

The savannization of moist forests in the Sierra Nevada de Santa Marta, Colombia

In the Rio Ranchería watershed of the Sierra Nevada de Santa Marta, between 500 and 1500 m, savanna vegetation is interspersed with moist forests. The savannas are composed of native savanna grasses like Aristida adscensionis L., Arundinella sp., Panicum olyroides Kunth, and Schyzachyrium microstachyum (Desv.) Roseng., Arrill & Izag and the African Melinis minutiflora P. Beauv. There is also Curatella americana L. and Byrsonima crassifolia (L.) H.B.K., two typical tree species of the neotropical savannas. Although moist forest patches occur more often on lower slopes and narrow valley bottoms, they can also be found on mid- and upper-slopes and less often on ridges. Thus, these forest patches are not gallery forests as are found throughout the neotropics, but the result of deforestation and fractionation of a continuous forest. A comparison of soil profiles between the savannas and remnant forest patches on the same slope, showed the disappearance of the A and B horizons (approx. 50 cm) under savanna vegetation. The sharp difference between the savanna and forest soils at the Rio Ranchería does not appear to be due to a change in soil water status along a toposequence or differences in the underlying bedrock. We hypothesize that the savannas of the Rio Ranchería watershed, are the result of deforestation and land practices on infertile soils derived from granite. The savannization process was likely initiated by Amerindians by means of the frequent use of fire or clearing lands for the cultivation of maize. The introduction of cattle by Spaniards (c. 1530) and the frequent use of fire to maintain grazing fields, contributed to further degradation of the habitat. While some tropical landscapes recovered their forest cover when human pressure was removed approximately 500 years ago, areas such as the Rio Ranchería watershed have suffered permanent damage. The savannas of this region are likely to remain unless fire is suppressed and soil restoration practices implemented.     

Sixteen hundred years of increasing tree cover prior to modern deforestation in Southern Amazon and Central Brazilian savannas

Tropical ecosystems are under increasing pressure from land‐use change and deforestation. Changes in tropical forest cover are expected to affect carbon and water cycling with important implications for climatic stability at global scales. A major roadblock for predicting how tropical deforestation affects climate is the lack of baseline conditions (i.e., prior to human disturbance) of forest–savanna dynamics. To address this limitation, we developed a long‐term analysis of forest and savanna distribution across the Amazon–Cerrado transition of central Brazil. We used soil organic carbon isotope ratios as a proxy for changes in woody vegetation cover over time in response to fluctuations in precipitation inferred from speleothem oxygen and strontium stable isotope records. Based on stable isotope signatures and radiocarbon activity of organic matter in soil profiles, we quantified the magnitude and direction of changes in forest and savanna ecosystem cover. Using changes in tree cover measured in 83 different locations for forests and savannas, we developed interpolation maps to assess the coherence of regional changes in vegetation. Our analysis reveals a broad pattern of woody vegetation expansion into savannas and densification within forests and savannas for at least the past ~1,600 years. The rates of vegetation change varied significantly among sampling locations possibly due to variation in local environmental factors that constrain primary productivity. The few instances in which tree cover declined (7.7% of all sampled profiles) were associated with savannas under dry conditions. Our results suggest a regional increase in moisture and expansion of woody vegetation prior to modern deforestation, which could help inform conservation and management efforts for climate change mitigation. We discuss the possible mechanisms driving forest expansion and densification of savannas directly (i.e., increasing precipitation) and indirectly (e.g., decreasing disturbance) and suggest future research directions that have the potential to improve climate and ecosystem models.     

Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors

Aim Traditional methodologies of mapping vegetation, as carried out by ecologists, consist primarily of field surveying or mapping from aerial photography. Previous applications of satellite imagery for this task (e.g. Landsat TM and SPOT HRV) have been unsuccessful, as such imagery proved to have insufficient spatial resolution for mapping vegetation. This paper reports on a study to assess the capabilities of the recently launched remote sensing satellite sensor Ikonos, with improved capabilities, for mapping and monitoring upland vegetation using traditional image classification methods. Location The location is Northumberland National Park, UK. Methods Traditional remote sensing classification methodologies were applied to the Ikonos data and the outputs compared to ground data sets. This enabled an assessment of the value of the improved spatial resolution of satellite imagery for mapping upland vegetation. Post‐classification methods were applied to remove noise and misclassified pixels and to create maps that were more in keeping with the information requirements of the NNPA for current management processes. Results The approach adopted herein for quick and inexpensive land cover mapping was found to be capable of higher accuracy than achieved with previous approaches, highlighting the benefits of remote sensing for providing land cover maps. Main conclusions Ikonos imagery proved to be a useful tool for mapping upland vegetation across large areas and at fine spatial resolution, providing accuracies comparable to traditional mapping methods of ground surveys and aerial photography.     

Mapping and monitoring of coastal wetlands of Çukurova Delta in the Eastern Mediterranean region

The aim of this research was to link vegetation characteristics, such as spatial and temporal distribution, and environmental variables, with land cover information derived from remotely sensed satellite images of the Eastern Mediterranean coastal wetlands of Turkey. The research method was based on (i) recording land cover characteristics by means of a vegetation indicator, and (ii) classifying and mapping coastal wetlands utilizing a Landsat Thematic Mapper (TM) image of Çukurova Deltas in Turkey. Vegetation characteristics of various habitats, such as sand dunes, salt marshes, salty plains and afforestation areas, were identified by field surveys. A Landsat TM image of 4 July 1993 was pre-processed and then classified using the Maximum Likelihood (ML) algorithm and Artificial Neural Networks (ANN). As a result of this supervised classification, the land cover types were classified with a largest accuracy of 90.2% by ANN. The classified satellite sensor imagery was linked to vegetation and bird census data, which were available through literature in a Geographical Information System (GIS) environment to determine the spatial distribution of plant and bird biodiversity in this coastal wetland. The resulting data provide an important baseline for further investigations such as monitoring, change detections and designing conservation policies in this coastal ecosystem.     

When is a ‘forest’ a savanna,and why does it matter?

Savannas are defined based on vegetation structure, the central concept being a discontinuous tree cover in a continuous grass understorey. However, at the high‐rainfall end of the tropical savanna biome, where heavily wooded mesic savannas begin to structurally resemble forests, or where tropical forests are degraded such that they open out to structurally resemble savannas, vegetation structure alone may be inadequate to distinguish mesic savanna from forest. Additional knowledge of the functional differences between these ecosystems which contrast sharply in their evolutionary and ecological history is required. Specifically, we suggest that tropical mesic savannas are predominantly mixed tree–C₄ grass systems defined by fire tolerance and shade intolerance of their species, while forests, from which C₄ grasses are largely absent, have species that are mostly fire intolerant and shade tolerant. Using this framework, we identify a suite of morphological, physiological and life‐history traits that are likely to differ between tropical mesic savanna and forest species. We suggest that these traits can be used to distinguish between these ecosystems and thereby aid their appropriate management and conservation. We also suggest that many areas in South Asia classified as tropical dry forests, but characterized by fire‐resistant tree species in a C₄ grass‐dominated understorey, would be better classified as mesic savannas requiring fire and light to maintain the unique mix of species that characterize them.     

Differentiating geological fertility derived vegetation zones in Kruger National Park,South Africa,using Landsat and MODIS imagery

Spatial technologies present possibilities for producing frequently updated and accurate habitat maps, which are important in biodiversity conservation. Assemblages of vegetation are equivalent to habitats. This study examined the use of satellite imagery in vegetation differentiation in South Africa's Kruger National Park (KNP). A vegetation classification scheme based on dominant tree species but also related to the park's geology was tested, the geology generally consisting of high and low fertility lithology. Currently available multispectral satellite imagery is broadly either of high spatial but low temporal resolution or low spatial but high temporal resolution. Landsat TM/ETM+ and MODIS images were used to represent these broad categories. Rain season dates were selected as the period when discrimination between key habitats in KNP is most likely to be successful. Principal Component Analysis enhanced vegetated areas on the Landsat images, while NDVI vegetation enhancement was employed on the MODIS image. The images were classified into six field sampling derived classes depicting a vegetation density and phenology gradient, with high (about 89%) indicative classification accuracy. The results indicate that, using image processing procedures that enhance vegetation density, image classification can be used to map the park's vegetation at the high versus low geological fertility zone level, to accuracies above 80% on high spatial resolution imagery and slightly lower accuracy on lower spatial resolution imagery. Rainfall just prior to the image date influences herbaceous vegetation and, therefore, success at image scene vegetation mapping, while cloud cover limits image availability. Small scale habitat differentiation using multispectral satellite imagery for large protected savanna areas appears feasible, indicating the potential for use of remote sensing in savanna habitat monitoring. However, factors affecting successful habitat mapping need to be considered. Therefore, adoption of remote sensing in vegetation mapping and monitoring for large protected savanna areas merits consideration by conservation agencies.     

Gallery forests versus bosquets: conservation of natural fragments at Lopé National Park in central Gabon

Human‐induced forest fragmentation has been relatively well‐studied, however, we know very little about the role of natural fragmentation in sustaining rare or marginal species that could have been lost if the advancement of continuous forest had not been controlled. Between February 2001 and January 2003, we conducted a study on characteristics of natural forest fragments in the mosaic of forests and savannas in the north of Lopé National Park in Central Gabon. We surveyed 61 vegetation plots (0.08 ha each) and compared vegetation characteristics of isolated forest fragments (bosquets) with those of gallery forests. Both shared 39% of all 251 species inventoried. Gallery forests contained 45% plant species on their own, while 16% were encountered only in bosquets. Therefore, bosquets were found to be valuable component of the Lopé landscape worth protecting. In addition, the Shannon–Wienner diversity index (H′) was higher for bosquets neighbouring gallery forests or continuous forests regardless of their sizes because seeds of new plant species were easily dispersed in these bosquets. To protect these gallery forests and bosquets, one of the traditional conservation tools – a controlled savanna burning – should still be used to prevent forest fragments from being engulfed by the expanding continuous forest.     

基于环境星与MODIS时序数据的面向对象森林植被分类

林区地形复杂、植被分布无序,且森林植被光谱信息相近,因而森林二级类型边界的确定成为土地覆盖遥感分类的难点。选择吉林省东部山区为研究区,以环境星影像(HJ-1 CCD)和中等分辨率成像光谱仪(MODIS)时序数据为基础,采用面向对象的分类方法进行森林植被类型的提取。分类特征参数主要选取了HJ-1 CCD的光谱和纹理特征,以及MODIS时序数据的物候特征。研究区总体分类精度为91.5%,Kappa系数为0.88,森林二级类型的分类精度均较高,其中落叶阔叶林的制图精度达到了97.1%。所用的面向对象分类方法与未加入物候特征的面向对象分类方法相比,森林二级类型的分类精度得到大幅度提高。     

Spatial patterns of the Chaco vegetation of central Argentina: Integration of remote sensing and phytosociology

Abstract. In this study we report the first application of Landsat TM imagery to Chaco vegetation studies at a regional scale in Argentina. We produced a map showing 13 clearly differentiated land‐cover types, and described the composition and structure of the plant communities, in an area of almost 42002 km² in central Argentina. The land‐cover map obtained shows that the Chaco vegetation in central Argentina is highly disturbed. In the lowland part of the area the dominant land‐cover types are largely cultural landscapes and substitute shrublands, which have displaced the original Chaco forests, leaving only small isolated remnants generally confined to sites with some kind of constrain for agriculture. The use of TM images and the multivariate analysis of phytosociological data showed a qualified, high accuracy mapping capability for land‐cover types in the Chaco region (ca. 85% overall accuracy). Our results highlight the utility of TM and field data in a subtropical to warm‐temperate region, which is promising where other ancillary data are not available and a rapid acquisition of reliable vegetation data is required, so constituting a starting point for an imperative and more extensive classification and mapping of the endangered Chaco region.     

Mapping land cover and forest density in Zagros forests of Khuzestan province in Iran: A study based on Sentinel-2, Google Earth and field data

Zagros forests in western Iran have widely been destroyed because of various reasons. This study was performed to provide the land cover and forest density maps in Zagros forests of Khuzestan province using Sentinel-2, Google Earth and field data. The forest boundary in Khuzestan province was digitized in Google Earth. Sentinel-2 satellite images were provided for the study area. One 1:25000 index sheet of Iranian Mapping Organization (IMO) was selected as pilot area in the province. Sentinel-2 image of the pilot area was classified using different supervised classification algorithms to select the best algorithm for land cover mapping in Khuzestan province. In addition, to evaluate the accuracy of Google Earth data, field sampling was performed using random plots in different land covers. Field data of forest plots were applied to investigate tree canopy cover percent (forest density), as well. Classification of Sentinel-2 image in Zagros area of Khuzestan province was done using the best algorithm and the land cover was obtained. The forest density map was also obtained using a linear regression model between tree canopy cover percent (obtained from field plots) and normalized difference vegetation index (NDVI) (obtained from NDVI map). Finally, the accuracy of land cover map was assessed by some square plots on Google Earth. Results demonstrated that support vector machine (SVM) algorithm had the highest accuracy for land cover mapping. Results also showed that Google Earth images had a good accuracy in the Zagros forests of Khuzestan province. Results demonstrated that NDVI has been a good predicator to estimate tree canopy cover in the study area. Based on results, an area of 443,091.22 ha is covered by Zagros forests in Khuzestan province. Results of accuracy assessment of the land cover map showed the good accuracy of this map in Khuzestan province (overall accuracy: 91% and kappa index: 0.83). For optimum management of Zagros forests, it is suggested that the land cover and forest density mapping will be performed using SVM algorithm, NDVI, and Sentinel-2 satellite images in Zagros forests of Khuzestan province in the certain periods.     

Fire weather risk differs across rain forest—savanna boundaries in the humid tropics of north‐eastern Australia

Alternative stable state theory has been applied to understanding the control by landscape fire activity of pyrophobic tropical rain forest and pyrophytic eucalypt savanna boundaries, which are often separated by tall eucalypt forests. We evaluate the microclimate of three vegetation types across an elevational gradient and their relative fire risk as measured by McArthur's Forest Fire Danger Index (FFDI). Microclimatic data were collected from rain forest, tall eucalypt forest and savanna sites on eight vegetation boundaries throughout the humid tropics in north Queensland over a 3‐year period and were compared with data from a nearby meteorological station. There was a clear annual pattern in daily FFDI with highest values in the austral winter dry season and lowest values in the austral summer wet season. There was a strong association of the meteorological station FFDI values with those from the three vegetation types, albeit they were substantially lower. The rank order of FFDI values among the vegetation types decreased from savanna, tall eucalypt forest, then rain forest, a pattern that was consistent across each transect. Only very rarely would rain forest be flammable, despite being adjacent to highly flammable savannas. These results demonstrate the very strong effect of vegetation type on microclimate and fire risk, compared with the weak effect of elevation, consistent with a fire–vegetation feedback. This study is the first demonstration of how vegetation type influences microclimate and fire risk across a topographically complex tropical forest–savanna gradient.     

Role of mixed‐species flocks in the use of adjacent savannas by forest birds in the central Cerrado,Brazil

Abstract: We examined the role of mixed‐species flocks for forest birds during their breeding and non‐breeding seasons in the use of savannas adjacent to forests in central Cerrado, Brazil. Transect surveys (n = 64) were conducted in eight savanna patches. Distances of birds from forests were estimated. Recorded birds were classified as members or not of mixed‐species flocks. About half of the bird species recorded in savannas were found in at least one mixed‐species flock. As distance from the forest increased, the number of species in mixed‐species flocks tended not to vary, while the number of species foraging alone or in mono‐specific groups decreased. Thus, for some forest species, participation in mixed‐species flocks allowed a greater use of more distant savannas. This tendency of being in mixed‐species flocks at greater distances from forests also can be interpreted as a reluctance to forage alone or in mono‐specific groups due to higher predation risk in less protective vegetation distant from cover. There was strong seasonal variation in the participation of bird species in mixed‐species flocks. There were significantly more species in mixed‐species flocks than out of these associations in the non‐breeding season, while differences in the breeding season were not significant. These patterns occurred, in part because mixed‐species flocks tended to be more frequent, to have more species and to forage at greater distances from forests during the early non‐breeding season than in other periods. This study suggests that the formation of mixed‐species flocks plays an important role in promoting the use of adjacent savannas by forest birds at forest/savanna boundaries in Cerrado. It also pointed out a novel advantage gained by birds with participation in mixed‐species flocks – greater use of adjacent vegetation patches.     

Multi‐decadal stability of woody cover in a mesic eucalypt savanna in the Australian monsoon tropics

Previous analyses of historical aerial photography and satellite imagery have shown thickening of woody cover in Australian tropical savannas, despite increasing fire frequency. The thickening has been attributed to increasing precipitation and atmospheric CO₂ enrichment. These analyses involved labour‐intensive, manual classification of vegetation, and hence were limited in the extent of the areas and the number of measurement times used. Object‐based, semi‐automated classification of historical sequences of aerial photography and satellite imagery has enabled the spatio‐temporal analysis of woody cover over entire landscapes, thus facilitating measurement, monitoring and attribution of drivers of change. Using this approach, we investigated woody cover change in 4000 ha of intact mesic savanna in the Ranger uranium lease and surrounding Kakadu National Park, using imagery acquired on 10 occasions between 1950 and 2016. Unlike previous studies, we detected no overall trend in woody cover through time. Some variation in cover was related to rainfall in the previous 12 months, and there were weak effects of fire in the year of image acquisition and the antecedent 4 years. Our local‐scale study showed a mesic eucalypt savanna in northern Australia has been resilient to short‐term variation in rainfall and fire activity; however, changes in canopy cover could have occurred in other settings. When applying this semi‐automated approach to similar studies of savanna dynamics, we recommend maximising the time depth and number of measurement years, standardising the time of year for image acquisition and using many plots of 1 ha in area, rather than fewer, larger plots.