A new land-cover map of Africa for the year 2000

Aim In the framework of the Global Land Cover 2000 (GLC 2000), a land‐cover map of Africa has been produced at a spatial resolution of 1 km using data from four sensors on‐board four different Earth observing satellites. Location The map documents the location and distribution of major vegetation types and non‐vegetated land surface formations for the entire African continent plus Madagascar and the other surrounding islands. Methods The bulk of these data were acquired on a daily basis throughout the year 2000 by the VEGETATION sensor on‐board the SPOT‐4 satellite. The map of vegetation cover has been produced based upon the spectral response and the temporal profile of the vegetation cover. Digital image processing and geographical information systems techniques were employed, together with local knowledge, high resolution imagery and expert consultation, to compile a cartographic map product. Radar data and thermal sensors were also used for specific land‐cover classes. Results A total of 27 land cover categories are documented, which has more thematic classes than previously published land cover maps of Africa contain. Systematic comparison with existing land cover data and 30‐m resolution imagery from Landsat are presented, and the map is also compared with other pan‐continental land cover maps. The map and digital data base are freely available for non‐commercial uses from http://www.gvm.jrc.it/tem/africa/products.htm Main conclusions The map improves our state of knowledge of the land‐cover of Africa and presents the most spatially detailed view yet published at this scale. This first version of the map should provide an important input for regional stratification and planning purposes for natural resources, biodiversity and climate studies.     

A new forest cover map of continental southeast Asia derived from SPOT‐VEGETATION satellite imagery

Question: Can recent satellite imagery of coarse spatial resolution support forest cover assessment and mapping at the regional level? Location: Continental southeast Asia. Methods: Forest cover mapping was based on digital classification of SPOT4‐VEGETATION satellite images of 1 km spatial resolution from the dry seasons 1998/1999 and 1999/2000. Following a geographical stratification, the spectral clusters were visually assigned to land cover classes. The forest classes were validated by an independent set of maps, derived from interpretation of satellite imagery of high spatial resolution (Landsat TM, 30 m). Forest area estimates from the regional forest cover map were compared to the forest figures of the FAO database. Results: The regional forest cover map displays 12 forest and land cover classes. The mapping of the region's deciduous and fragmented forest cover remained challenging. A high correlation was found between forest area estimates obtained from this map and from the Landsat TM derived maps. The regional and sub‐regional forest area estimates were close to those reported by FAO. Conclusion: SPOT4‐VEGETATION satellite imagery can be used for mapping consistently and uniformly the extent and distribution of the broad forest cover types at the regional scale. The new map can be considered as an update and improvement on existing regional forest cover maps.     

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.     

Continental estimates of forest cover and forest cover changes in the dry ecosystems of Africa between 1990 and 2000

Aim

This study provides regional estimates of forest cover in dry African ecoregions and the changes in forest cover that occurred there between 1990 and 2000, using a systematic sample of medium‐resolution satellite imagery which was processed consistently across the continent.

Location

The study area corresponds to the dry forests and woodlands of Africa between the humid forests and the semi‐arid regions. This area covers the Sudanian and Zambezian ecoregions.

Methods

A systematic sample of 1600 Landsat satellite imagery subsets, each 20 km × 20 km in size, were analysed for two reference years: 1990 and 2000. At each sample site and for both years, dense tree cover, open tree cover, other wooded land and other vegetation cover were identified from the analysis of satellite imagery, which comprised multidate segmentation and automatic classification steps followed by visual control by national forestry experts.

Results

Land cover and land‐cover changes were estimated at continental and ecoregion scales and compared with existing pan‐continental, regional and local studies. The overall accuracy of our land‐cover maps was estimated at 87%. Between 1990 and 2000, 3.3 million hectares (Mha) of dense tree cover, 5.8 Mha of open tree cover and 8.9 Mha of other wooded land were lost, with a further 3.9 Mha degraded from dense to open tree cover. These results are substantially lower than the 34 Mha of forest loss reported in the FAO's 2010 Global Forest Resources Assessment for the same period and area.

Main conclusions

Our method generates the first consistent and robust estimates of forest cover and change in dry Africa with known statistical precision at continental and ecoregion scales. These results reduce the uncertainty regarding vegetation cover and its dynamics in these previously poorly studied ecosystems and provide crucial information for both science and environmental policies.     

Remote sensing imagery for resource inventories in central Africa: The importance of detailed field data

The rate of rain forest clearing throughout central Africa is of national and international interest because it affects both the region's contribution to global warming and impacts the sustainable productive capacity of its natural resource base. The size and inaccessibility of much of central Africa makes remote sensing imagery the most suitable data source for regional land cover mapping and land transformation monitoring. Present image availability is poor. Most regional studies have had to rely on coarse resolution AVHRR 1 km data that fails to detect the small-scale agricultural clearings that are the primary cause of land cover change throughout the region. This study demonstrates that higher spatial resolution Landsat MSS imagery, which comprises the most available, geographically comprehensive and longest time series dataset, is too coarse to map land cover in low population density areas typical of most of central Africa. Furthermore, this study cautions that the use of high resolution imagery without detailed collateral field data on population density and land use practices while generating superficially plausible results, will most probably produce highly inaccurate estimates of land cover and land transformation. Policies for future regional remote sensing surveys of central Africa should focus on acquisition of higher spatial, spectral, and radiometric resolution imagery and must be accompanied by detailed, systematic field data collection.     

Ecotope mapping for landscape ecological assessment of habitat and ecosystem

An ecotope (spatial eco-space) map that considers topography and bio-organism-relevant variables emerges as an important basic framework when landscape-scale characteristics for ecosystem management and wildlife conservation are needed. A spatio-geoecological framework based on geographic information systems (GIS) and a vegetation survey were developed for wildlife habitat evaluation of national parks and applied to a representative rugged valley area of Mt. Sorak National Park in Korea. An ecotope map was classified into hundreds of types and dozens of groups by combining biological and geophysical variables. Variables included: forest vegetation type, topographic solar radiation, normalized difference vegetation index (NDVI), elevation, and anthropogenic factors, such as, streams and roads. Layers of GIS variables were produced by field surveys, modeling, satellite images, or digitalization. Vegetation surveys were carried out to identify finer-scale distribution of vegetation types in the rugged valley area. Digital forest vegetation maps from the Forestry Administrator were then modified using the field-surveyed vegetation maps. Topographic solar radiation was predicted with a daily topographic radiation model. The NDVI was calculated from the satellite imagery of a Landsat Thematic Mapper. A digital elevation model (DEM) was used and the other layers were digitized using topographical maps with a scale of 1:25000. The aim of this study is to determine the geoecological factors relating to the spatial pattern of plant community. It was cleared by the spatial pattern of environmental variables and vegetation characteristics by detrended correspondence analysis using plant species and the environmental variables of each plot. The ordination component value of the first axis shows significant regression to some environmental variables. A case study of habitat evaluation was carried out using the resultant ecotope map. The spatial distribution of potential goral habitat and vegetation characteristics were predicted and the impact of human trails on the neighboring vegetation was also examined for restoration planning. The GIS-based framework developed for wildlife habitat evaluation is useful for natural resource management and human activity control in national parks in Korea.     

Assessment of forest spatial differentiation in Murmansk Province using field surveys and remote sensing data

The results of study of the spatial differentiation of forest using field data, remote sensing, and map data are presented. Different classification approaches are used while analyzing the diversity of forest communities, i.e., ecological-dominant, ecological-topological, and dynamical. The interpolation of local chatacteristics of plant associations and syntaxonomic units at the upper levels using the data of spectral satellite imagery and quantitative methods of processing allow to use in mapping important information on the structure and properties of vegetation. The results of our studies include thematic maps of the specific parameters of forest and a 1: 100000-scale vegetation map of the central part of Murmansk Province. The role of natural and anthropogenic factors is reflected in the legend to the map.     

A vegetation map of tropical continental Asia at scale 1:5 million

Abstract. A vegetation map at scale 1:5 million is presented. * * Attached on the inside of this issue's back cover.
It covers Bangladesh, Burma (Myanmar), India, Cambodia, Laos, Thailand, Vietnam and Sri Lanka and fills a conspicuous gap in the cartography of tropical vegetation, following the publication of vegetation maps of South America, Africa and Malaysia. For conformity, it is presented as one sheet at a scale of 1:5 million. It uses the basic map of the American Geographical Society (1942; bipolar oblique conformal projection) which forms the base for FAO's Soil map of the world. Basic information was obtained from many published maps, unpublished observations and satellite data. The limits of the main vegetation types have been updated with a complete set of Landsat MSS images (369 scenes) with a mosaic of Landsat TM data for 1991 and with recent forest maps from Asia. Nine main vegetation units, which are groups of forest formations, have been identified and mapped, including woodlands, thickets and wooded savannas. Agricultural land has been shown in a uniform pale green colour in order to clearly express the extent of human impacts on woody vegetation. In spite of the necessary oversimplification of the ground data, this map is probably the most explicit expression of the remaining forest stands and of the regression of natural vegetation in the region. It can be considered as a benchmark for future monitoring of tropical vegetation.     

What is the Value of a Good Map? An Example Using High Spatial Resolution Imagery to Aid Riparian Restoration

Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes; whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping; however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems.     

Assessing large area forest cover products derived from the same imaging source across Victoria,Australia

Forest cover products are an essential tool for land managers and policy makers. They are used at a variety of spatial scales to inform decision‐making and policy across a range of ecosystem drivers and services. This article compares three forest cover products (FCP), all of which were created using Landsat satellite imagery, but using different methodologies and covering different spatial extents that range from global to state. It also explores their use and utility across the state of Victoria, Australia. It asks the question, how interchangeable are the forest cover maps? FCP are also validated against a very high‐resolution reference data set. Overall accuracy was around 89% for the state and national FCP, and 84% for the global FCP. The global map produced the lowest estimate of total forest cover, while estimates obtained by the national and state FCP were similar across the study area. Spatially, differences, however, were apparent. The national forest cover map obtained higher estimates across most of Victoria except in the most arid region which is dominated by low open woodland. While the national and global scale forest cover maps were found to have good diagnostic ability for large area assessment and reporting, their use for land management is not optimal and can lead to gross error.     

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.     

Identifying nascent wetland forest conversion in the Democratic Republic of the Congo

Wetlands cover large areas in the Democratic Republic of the Congo. However, their extent and distribution have not been accurately mapped. While wetland forests remain largely undisturbed, increasing threats by anthropogenic activities have been observed in areas with high population density per arable or exploitable land. The scarcity of terra firma forests in some territories of the Democratic Republic of the Congo has forced local communities to develop cropping methods that allow for cultivation in periodically flooded areas. Assessing wetlands extent and status is critical for long term conservation of these highly vulnerable ecosystems. In this study, we use multi-source and multi-resolution optical and radar remotely sensed data and elevation derived indices to map the wetlands of the Democratic Republic of the Congo. Results showed that wetlands are a significant part of the landscape in the country, covering an estimated 440,000 km² or 19.2 % of the total country area. By combining the wetlands map with a previously produced land cover depiction of the Democratic Republic of the Congo, a map including forested wetlands as a thematic class was derived. We investigated whether high terra firma population density and low percent remaining terra firma forest are related at the lowest administrative level (Sector); specifically, we tested these two variables as predictors of wetland forest cover loss. A polynomial regression relating population and primary terra firma forest to wetland forest cover loss yielded an r ² of 0.76, illustrating a nascent and significant land cover change dynamic. Areas most at risk for future wetland forest loss lie in the western Cuvette, and include (north–south) the Sud-Ubangi, Mongala, Equateur and Mai-Ndombe Districts. By quantifying available upland forest resources and overlaying with population density, it was possible to identify stressed areas inside of the forest domain (traditionally known for having generically high levels of forest resources). Results illustrate the need for addressing issues of wetland forest management and protection in the Democratic Republic of the Congo, especially where increasing populations are exhausting primary terra firma forest resources.     

Change in Vegetation Patterns Over a Large Forested Landscape Based on Historical and Contemporary Aerial Photography

Changes to vegetation structure and composition in forests adapted to frequent fire have been well documented. However, little is known about changes to the spatial characteristics of vegetation in these forests. Specifically, patch sizes and detailed information linking vegetation type to specific locations and growing conditions on the landscape are lacking. We used historical and recent aerial imagery to characterize historical vegetation patterns and assess contemporary change from those patterns. We created an orthorectified mosaic of aerial photographs from 1941 covering approximately 100,000 ha in the northern Sierra Nevada. The historical imagery, along with contemporary aerial imagery from 2005, was segmented into homogenous vegetation patches and classified into four relative cover classes using random forests analysis. A generalized linear mixed model was used to compare topographic associations of dense forest cover on the historical and contemporary landscapes. The amount of dense forest cover increased from 30 to 43% from 1941 to 2005, replacing moderate forest cover as the most dominant class. Concurrent with the increase in extent, the area-weighted mean patch size of dense forest cover increased tenfold, indicating greater continuity of dense forest cover and more homogenous vegetation patterns across the contemporary landscape. Historically, dense forest cover was rare on southwesterly aspects, but in the contemporary forest, it was common across a broad range of aspects. Despite the challenges of processing historical air photographs, the unique information they provide on landscape vegetation patterns makes them a valuable source of reference information for forests impacted by past management practices.     

Vegetation mapping using hierarchical object‐based image analysis applied to aerial imagery and lidar data

Aims: The primary objective of this study is to map the distribution and quantify the cover of vegetation alliances over the entirety of San Clemente Island (SCI). To this end, we develop and evaluate the mapping method of hierarchical object‐based classification with a rule‐based expert system. Location: San Clemente Island, California, USA. Methods: We developed and tested an approach based on hierarchical object‐based classification with a rule‐based expert system to effectively map vegetation communities on SCI following the Manual of California Vegetation classification system. In this mapping approach, the shrub species defining each vegetation community and non‐shrub growth forms were first mapped using aerial imagery and lidar data, then used as input in an automated mapping rule set that incorporates the percent cover rules of a field‐based mapping rule set. Results: The final vegetation map portrays the distribution of 19 vegetation communities across SCI, with the largest areas comprised of California Annual and Perennial Grassland (35%) and three types of coastal sage scrub and maritime succulent scrub, comprising a combined 53% of the area. Map accuracy was assessed to be 79% based on fuzzy methods and 61% with a traditional accuracy assessment. The accuracy of tree identification was assessed to be 81%, but species‐level tree accuracy was 45%. Conclusions: Semi‐automated approaches to vegetation community mapping can produce repeatable maps over large spatial extents that facilitate ecological management efforts. However, some low‐statured shrub community types were difficult to differentiate due to patchy canopies of co‐occurring species including abundant non‐native grasses characteristic of complex disturbance histories. Species‐level tree mapping accuracy was low due to the difficulty of identifying species within poorly illuminated canyons, resulting from sub‐optimal image acquisition timing.     

Phenological description of natural vegetation in southern Africa using remotely‐sensed vegetation data

Abstract. Various attempts have been made to describe and map the vegetation of southern Africa with recent efforts having an increasingly ecologi cal context. Vegetation classification is usually based on vegetation physiognomy and floristic composition, but phenology is useful source of information which is rarely used, although it can contribute functional information on ecosystems. The objectives of this study were to identify a suite of variables derived from time‐series NDVI data that best describe the phenological phenomena of vegetation in southern Africa and, secondly, to assess a classification of pixels of the study area based on NDVI variables using a preexisting map of the biomes that was delimited on the basis of life forms and climate. A number of variables were derived from the satellite data for describing phenological phenomena, which were analysed by multivariate techniques to determine which variables best explained the variation in the satellite data. This set of variables was used to produce a phenological classification of the vegetation of southern Africa, the results of which are discussed in relation to their concordance with the existing biome boundaries.     

An integrated pan‐tropical biomass map using multiple reference datasets

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108 , 2011, 9899; Nature Climate Change, 2 , 2012, 182) into a pan‐tropical AGB map at 1‐km resolution using an independent reference dataset of field observations and locally calibrated high‐resolution biomass maps, harmonized and upscaled to 14 477 1‐km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South‐East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15–21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha^?1 vs. 21 and 28 Mg ha^?1 for the input maps). The fusion method can be applied at any scale including the policy‐relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country‐specific reference datasets.     

Predictive map of Ixodes ricinus high-incidence habitats and a tick-borne encephalitis risk assessment using satellite data

The main objective of this project was to predict Ixodes ricinus abundant habitats reliably as a means of tick-borne encephalitis (TBE) risk assessment for the prevention of this disease. The vegetation types were used as the indicators of an ecosystem suitable for tick occurrence, for TBE virus circulation and, accordingly, for the existence of natural foci of this infection. Remote sensing methods were used to determine the indicative plant cover. Satellite data covering an experimental area of 70 × 70 km in Central Bohemia, the Czech Republic, was acquired by the Landsat 5 TM scanner. Nine forest classes were recognized in the experimental area by successive supervised and unsupervised classifications and identified in a field-checking botanical survey. An epidemiological TBE map based on human cases contracted in the territory under study was exploited for the evaluation of risk in particular forest classes. Predictive maps are expressed both in digital and in printed forms at a scale of 1 : 300 000 for an overall risk evaluation and at a scale of 1 : 25 000 for a detailed local orientation.     

Remote sensing analysis on primary productivity and forest cover dynamics: A Western Ghats India case study

Tropical forest ecosystems are among the most essential habitats on Earth for conserving biological diversity and short-term climate regulation. For this reason, they are key areas of conservation policies in the world. In this paper, we aim to investigate the dynamics of forest cover and their changes in primary productivity by empowering information on historical forest management and fieldwork research with remote sensing vegetation monitoring methods. The study area falls within the central portion of the Indian Western Ghats, a global biodiversity hotspot. In particular, part of the analysis was performed on the Kadamakal Reserve Forest and Pushpagiri Wildlife Sanctuary, which harbours an endemic low elevation dipterocarp evergreen forest. This area was managed by selective logging and became fully protected in 1984. We performed multiple time series macroscale analyses between 1999 and 2020 on the Indian Central Western Ghats region, using satellite products at 1 km spatial resolution from the VITO Copernicus Global Land Service on Dry Matter Productivity, Fraction of Absorbed Photosynthetically Active Radiation, and Normalised Difference Vegetation Index. We also performed a very-high spatial resolution Normalised Difference Vegetation Index differential analysis between 2021 and 2016 with Sentinel 2-L2A products to investigate forest dynamics within the reserve. At the 1 km spatial resolution has been found an increase in all three vegetation indices, by employing the LOESS statistical method for the smoothed transition autoregressive model of raster data medians of our datasets. The boxplot raster distribution analysis also highlighted a significant imbalance in dry matter productivity in the last decade (2010−2020) comparing the previous one (1999–2009). The second part of the analysis, at 10 m spatial resolution within the reserve forest, revealed a growth in the vegetation cover on the top of the Pushpagiri Mountain ridge and in a previously landslide area. The study found new erosion channels down to the upper plateau on the South-West side of the reserve due to an increment of the run-off processes during the monsoon period. This satellite analysis highlighted generalised positive vegetation trends in the Central Western Ghats, India, over the last twenty-two years, enhancing an improvement in the ecosystem functioning and carbon storage ecosystem service. Notably, through this work, we also developed a standardised and open-access framework to monitor the vegetation remotely (SVIT) during periods of forest inaccessibility for fieldwork sampling.