Extent Of Land Cover Research Articles

A Spatiotemporal Characterization of Water Resource Conditions and Demands as Influenced by the Hydrogeologic Framework of the Willcox Groundwater Basin, Southeastern Arizona, USA

In the Willcox Groundwater Basin (WGB), increasing rates of agricultural groundwater withdrawal have led to significant regional groundwater level decline, threatening the basin’s long-term water resource security. Updated characterization of the basin’s water resource conditions and agricultural water demand is critically important for informing groundwater resource management efforts. We developed the hydrogeologic framework of the WGB and linked groundwater level data with land cover classification data to provide a spatiotemporal assessment of water resource conditions and agricultural development in the WGB. A correlation analysis evaluated the degree of association between the basin’s mean annual depth-to-groundwater and agricultural land cover extent. Results of this study indicate that between 2008 and 2021, agricultural land cover in the WGB increased by 29%. The average rate of groundwater level change in the basin’s measured wells was calculated at −13.8 m between 2006 and 2021. We found a strong correlation between the basin’s mean annual measured depth-to-groundwater and the annual agricultural land cover extent, further reinforcing the understanding of agricultural water use in the basin as a principal driver of groundwater level decline. The methodological framework employed proved a simple and effective way to assess groundwater resources as influenced by geology and land use. The outcomes of this study provide critical information toward improved water resources management by providing an integrated understanding of local hydrogeology, groundwater level variability, and changes in agricultural land cover in arid inland basins such as those found in Arizona, USA.

Foraging Habitat and Its Effects on the Tricolored Blackbird’s Breeding Distribution and Abundance in the Sierra Nevada Foothills, California

The grassland-dominated eastern Central Valley and Sierra Nevada foothills of California, from Placer to Stanislaus counties, supported at least 43,000–55,000 breeding Tricolored Blackbirds (Agelaius tricolor) annually from 2014 to 2018—about 30% of the statewide population. We found that within 5 km of a colony, the extent of land cover used for foraging, grassland–herbaceous, and to a lesser extent annual crops and irrigated pasture, exceeded their proportions in this region as a whole, suggesting that nesting Tricolored Blackbirds select colony sites where these habitats are more abundant. Other land-cover types were underutilized for foraging, suggesting avoidance. The probability of Tricolored Blackbird occupancy of blocks of 100 km2 was strongly associated with the extent of the selected land-cover types. The relationship between average density of the breeding population and the extent of the selected land-cover types was significant but weaker, implying that other factors are important in determining density. From 2014 to 2018, development and mining eliminated or degraded 9 of 79 colony sites and made 4 others unsuitable by reducing the extent of nearby foraging habitat, although the total breeding population in the region did not decline. We recommend that conservation measures for the Tricolored Blackbird in federally and state-approved habitat-conservation plans in Placer and Sacramento counties, which support the largest breeding populations in the central Sierra foothills but where urban development is rapid, be reevaluated on the basis of recent colony locations and recognition of the critical role of grassland, annual crops, and irrigated pasture as foraging habitat.

Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century.

Land use has greatly transformed Earth's surface. While spatial reconstructions of how the extent of land cover and land-use types have changed during the last century are available, much less information exists about changes in land-use intensity. In particular, global reconstructions that consistently cover land-use intensity across land-use types and ecosystems are missing. We, therefore, lack understanding of how changes in land-use intensity interfere with the natural processes in land systems. To address this research gap, we map land-cover and land-use intensity changes between 1910 and 2010 for 9 points in time. We rely on the indicator framework of human appropriation of net primary production (HANPP) to quantify and map land-use-induced alterations of the carbon flows in ecosystems. We find that, while at the global aggregate level HANPP growth slowed down during the century, the spatial dynamics of changes in HANPP were increasing, with the highest change rates observed in the most recent past. Across all biomes, the importance of changes in land-use areas has declined, with the exception of the tropical biomes. In contrast, increases in land-use intensity became the most important driver of HANPP across all biomes and settings. We conducted uncertainty analyses by modulating input data and assumptions, which indicate that the spatial patterns of land use and potential net primary production are the most critical factors, while spatial allocation rules and uncertainties in overall harvest values play a smaller role. Highlighting the increasing role of land-use intensity compared to changes in the areal extent of land uses, our study supports calls for better integration of the intensity dimension into global analyses and models. On top of that, we provide important empirical input for further analyses of the sustainability of the global land system.

A machine learning approach to monitoring and forecasting spatio-temporal dynamics of land cover in Cox's Bazar district, Bangladesh from 2001 to 2019

This study uses machine learning techniques to monitor the areal extent of land use land cover (LULC) classes in Cox's Bazar district, a well-known tourist destination in Bangladesh from 2001 to 2019. The main objective of this study is to quantify the changes in LULC classes during this period and forecasting the areal extents classes for 2019 and 2025. Satellite imagery of 2001, 2005, and 2010 from Landsat 4-5 TM and, 2015, and 2019 from Landsat 8 OLI/TIRS were used to classify the study area into four LULC classes (water bodies, urban areas, vegetation, and bare land) using Random Forest (RF) classifier. These classes were used to forecast areal extents of land cover using Cellular Automata Simulation (CAS) for 2025. The eleven LULC classes from MODIS land cover type products (IGBP scheme) were also forecasted using RF regressor to compare prediction accuracy. This study observed an increase in vegetation cover and urban settlements by 26.65% and 2.52%, respectively, with decreased water bodies and bare lands by 17.25% and 11.91%. The overall accuracy of the classification is between 89.04% to 95.24%; CAS forecast accuracy is 93.2% and RF regressor forecast accuracy ranges from 87% to 99.8%. Grid search with cross-validations was performed to boost the accuracy of both models. The results are consistent with overall global trends of LULC change.

Land Use Land Cover Changes in Abeokuta Using Geospatial Techniques

Land use-land cover (LULC) changes are important factors that affect urban conditions and functions. Land and buildings will therefore continue to witness conversion of use as a result of several human activities competing for scarce land. The study investigates LULC in Abeokuta between 1996 and 2016. The specific objectives are to (1) categorize and measure the area extent (2) examine the trend (3) determine the contributing factors and predict future changes in Abeokuta. Multispectral images (1996 – 2016) at interval of ten years were obtained from USGS Land Change Modeler of Idrisi selva and used for the landuse change analysis and predictions. ArcGIS Software was used to enhance the Transitional Map productions. There was a progressive increase in built up area extent throughout the study period. Between the years 1996 and 2006 urban area grew by 12sqkm while the period between 2006 and 2016 witnessed an increase of 8kmsq. Vegetation experienced the highest decrease in land cover extent for the years between 1996 and 2006, decreasing at a rate of 2.4sqkm per year. However, the rate of loss in vegetative land cover reduced significantly over the next 10 years by 4kmsq increase between 2006 to 2016.

Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil

Hydroelectric dams are a major threat to rivers in the Amazon. They are known to decrease river connectivity, alter aquatic habitats, and emit greenhouse gases such as carbon dioxide and methane. Multiscale remotely sensed data can be used to assess and monitor hydroelectric dams over time. We analyzed the Sinop dam on the Teles Pires river from high spatial resolution satellite imagery to determine the extent of land cover inundated by its reservoir, and subsequent methane emissions from TROPOMI S-5P data. For two case study areas, we generated 3D reconstructions of important endemic fish habitats from unmanned aerial vehicle photographs. We found the reservoir flooded 189 km2 (low water) to 215 km2 (high water) beyond the extent of the Teles Pires river, with 13–30 m tall forest (131.4 Mg/ha average AGB) the predominant flooded class. We further found the reservoir to be a source of methane enhancement in the region. The 3D model showed the shallow habitat had high complexity important for ichthyofauna diversity. The distinctive habitats of rheophile fishes, and of the unique species assemblage found in the tributaries have been permanently modified following inundation. Lastly, we illustrate immersive visualization options for both the satellite imagery and 3D products.

A landscape classification map of Ireland and its potential use in national land use monitoring

This study presents a novel landscape classification map of the Republic of Ireland and is the first to identify broad landscape classes by incorporating physiographic and land cover data. The landscape classification responds to commitments to identify and classify the Irish landscape as a signatory to the European Landscape Convention. The methodology applied a series of clustering iterations to determine an objective multivariate classification of physiographic landscape units and land cover datasets. The classification results determined nine statistically significant landscape classes and the development of a landscape classification map at a national scale. A statistical breakdown of land cover area and diversity of each class was interpreted, and a comparison was extended using independent descriptive variables including farmland use intensity, elevation, and dominant soil type. Each class depicts unique spatial and composition characteristics, from coastal, lowland and elevated, to distinct and dominating land cover types, further explained by the descriptive variables. The significance of individual classes and success of the classification is discussed with particular reference to the wider applicability of the map. The transferability of the methodology to other existing physiographic maps and environmental datasets to generate new landscape classifications is also considered. This novel work facilitates the development of a strategic framework to efficiently monitor, compare and analyse ecological and other land use data that is spatially representative of the distribution and extent of land cover in the Irish countryside.

LUCAS Copernicus 2018: Earth-observation-relevant in situ data on land cover and use throughout the European Union

Abstract. The Land Use/Cover Area frame Survey (LUCAS) is an evenly spaced in situ land cover and land use ground survey exercise that extends over the whole of the European Union. LUCAS was carried out in 2006, 2009, 2012, 2015, and 2018. A new LUCAS module specifically tailored to Earth observation (EO) was introduced in 2018: the LUCAS Copernicus module. The module surveys the land cover extent up to 51 m in four cardinal directions around a point of observation, offering in situ data compatible with the spatial resolution of high-resolution sensors. However, the use of the Copernicus module being marginal, the goal of the paper is to facilitate its uptake by the EO community. First, the paper summarizes the LUCAS Copernicus protocol to collect homogeneous land cover on a surface area of up to 0.52 ha. Secondly, it proposes a methodology to create a ready-to-use dataset for Earth observation land cover and land use applications with high-resolution satellite imagery. As a result, a total of 63 364 LUCAS points distributed over 26 level-2 land cover classes were surveyed on the ground. Using homogeneous extent information in the four cardinal directions, a polygon was delineated for each of these points. Through geospatial analysis and by semantically linking the LUCAS core and Copernicus module land cover observations, 58 426 polygons are provided with level-3 land cover (66 specific classes including crop type) and land use (38 classes) information as inherited from the LUCAS core observation. The open-access dataset supplied with this paper (https://doi.org/10.6084/m9.figshare.12382667.v4 d'Andrimont, 2020) provides a unique opportunity to train and validate decametric sensor-based products such as those obtained from the Copernicus Sentinel-1 and Sentinel-2 satellites. A follow-up of the LUCAS Copernicus module is already planned for 2022. In 2022, a simplified version of the LUCAS Copernicus module will be carried out on 150 000 LUCAS points for which in situ surveying is planned. This guarantees a continuity in the effort to find synergies between statistical in situ surveying and the need to collect in situ data relevant for Earth observation in the European Union.

A climate change indicator framework for rangelands and pastures of the USA

Rangelands and pastures include grasslands, savannas, shrublands, and woodlands and are often maintained to support grazing animals. Rangelands and pastures cover more than one-third of the land area in the USA and a similar extent globally. The ecosystem goods and services associated with rangeland and pastureland include critical wildlife habitat, forage for livestock, amenities related to water conservation, sustainable soil functions, and soil stabilization and support a diversity of biota and livelihoods. This paper provides a framework for development of a socio-ecological system (SES)–oriented set of indicators for rangeland and pasture systems to support evaluation of impacts of climate and land use changes. These indicators will also serve to inform adaptive management practices. We present a rationale for using an SES approach to evaluate trends and vulnerabilities of rangeland and pasture systems and provide an example of a set of system indicators arising from the SES approach. The indicators include evaporative demand, land cover extent, aboveground plant biomass, human demographics (population age distribution), cattle numbers, and economic value of cattle products relative to total agricultural value. These indicators are not meant to be comprehensive but are offered to illustrate how they might be used in a SES approach to plan for, assess, and mitigate climate change impacts. The conceptual framework provides a systems perspective on the impact of climate change on the socio-ecological dynamics of rangeland and pasture systems including measures of the resilience and vulnerability of ecosystem services with respect to the six indicators. The article focusses on livestock production in rangeland ecosystems, recognizing that additional work is needed to address pastures and other ecosystem services. Examples of the types of regional information associated with the indicators are provided. Guidance for future efforts in indicator development is offered. This framework will serve to guide future development of indicators for rangeland and pasture components of a larger national effort of indicators.

The influence of land cover data on farm-scale valuations of natural capital

The valuation of natural capital within individual farms could inform environmentally beneficial land use change and form the basis of agricultural subsidy schemes based on the provision of ecosystem services. Land cover extents can be used in a benefit transfer approach to produce monetary valuations of natural capital rapidly and at low cost. However, the methodology has not before been used within individual farms, and the impact of land cover data characteristics on the accuracy of valuations is uncertain. Here, we apply the approach to five UK farms of contrasting size, configuration and farming style, using three widely available land cover products. Results show that the land cover product used has a substantial impact on valuations, with differences of up to 58%, and the magnitude of this effect varies considerably according to the landscape structure of the farm. At most sites, valuation differences are driven by the extent of woodland recorded in the landscape, with higher resolution land cover products incorporating larger amounts of woodland through inclusion of smaller patches, leading to higher overall valuations. Integrating more accurate land cover data and accounting for the condition, configuration and location of natural capital has potential to improve the accuracy of valuations.

Land Use and Land Cover Mapping of Davangere using Google Earth Engine

Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defence, intelligence, commerce, economics and administrative planning. One among these applications is the construction of land use and land cover maps through image classification process. Land Use / Land Cover (LULC) information is a crucial input in designing efficient strategies for managing natural resources and monitoring environmental changes from time to time. The present study aims to know the extent of land cover and its usage in Davangere region of Karnataka, India. In this study, satellite image of Davangere during October-November 2018 was used for LULC supervised classification with the help of remote sensing tools like QGIS and Google Earth Engine. Six LULC classes were decided to locate on the map and the accuracy assessment was done using theoretical error matrix and Kappa coefficient. The key findings include LULC under Water bodies (8%), Built up Area (15.1%), Vegetation (9%), Horticulture (20.8%), Agriculture (39.3%) and Others (7%) with overall accuracy of 94.8% and Kappa coefficient of 0.866 indicating almost accurate goodness of classification.

Land use impacts on river health of Uma Oya, Sri Lanka: implications of spatial scales.

Human actions on landscapes are a principal threat to the ecological integrity of river ecosystems worldwide. Tropical landscapes have been poorly investigated in terms of the impact of catchment land cover alteration on water quality and biotic indices in comparison to temperate landscapes. Effects of land cover in the catchment at two spatial scales (catchment and site) on stream physical habitat quality, water quality, macroinvertebrate indices and community composition were evaluated for Uma Oya catchment in the upper Mahaweli watershed, Sri Lanka. The relationship between spatial arrangement of land cover in the catchment and water quality, macroinvertebrate indices and community composition was examined using univariate and multivariate approaches. Results indicate that chemical water quality variables such as conductivity and total dissolved solids are mostly governed by the land cover at broader spatial scales such as catchment scale. Shannon diversity index was also affected by catchment scale forest cover. In stream habitat features, nutrients such as N-NO3-, macroinvertebrate family richness, %shredders and macroinvertebrate community assemblages were predominantly influenced by the extent of land cover at 200m site scale suggesting that local riparian forest cover is important in structuring macroinvertebrate communities. Thus, this study emphasizes the importance of services provided by forest cover at catchment and site scale in enhancing resilience of stream ecosystems to natural forces and human actions. Findings suggest that land cover disturbance effects on stream ecosystem health could be predicted when appropriate spatial arrangement of land cover is considered and has widespread application in the management of tropical river catchments.

Deciphering rangeland transformation—complex dynamics obscure interpretations of woody plant encroachment

Context Woody plant encroachment—the conversion of grasslands to woodlands—is among the greatest challenges faced by rangelands worldwide. Yet this phenomenon is poorly understood, and complex land use dynamics make interpreting the timing and extent of land cover changes a global challenge.

Legacy effects of grassland management on soil carbon to depth.

The importance of managing land to optimize carbon sequestration for climate change mitigation is widely recognized, with grasslands being identified as having the potential to sequester additional carbon. However, most soil carbon inventories only consider surface soils, and most large-scale surveys group ecosystems into broad habitats without considering management intensity. Consequently, little is known about the quantity of deep soil carbon and its sensitivity to management. From a nationwide survey of grassland soils to 1m depth, we show that carbon in grassland soils is vulnerable to management and that these management effects can be detected to considerable depth down the soil profile, albeit at decreasing significance with depth. Carbon concentrations in soil decreased as management intensity increased, but greatest soil carbon stocks (accounting for bulk density differences), were at intermediate levels of management. Our study also highlights the considerable amounts of carbon in subsurface soil below 30cm, which is missed by standard carbon inventories. We estimate grassland soil carbon in Great Britain to be 2097TgC to a depth of 1m, with ~60% of this carbon being below 30cm. Total stocks of soil carbon (tha(-1) ) to 1m depth were 10.7% greater at intermediate relative to intensive management, which equates to 10.1tha(-1) in surface soils (0-30cm), and 13.7tha(-1) in soils from 30 to 100cm depth. Our findings highlight the existence of substantial carbon stocks at depth in grassland soils that are sensitive to management. This is of high relevance globally, given the extent of land cover and large stocks of carbon held in temperate managed grasslands. Our findings have implications for the future management of grasslands for carbon storage and climate mitigation, and for global carbon models which do not currently account for changes in soil carbon to depth with management.

Remote regions, remote data: A spatial investigation of precipitation, dynamic land covers, and conflict in the Sudd wetland of South Sudan

Though wetlands are recognized for their wide range of ecosystem services, the sustainable management of wetlands is hindered by a lack of data, especially in large transnational river systems in less-developed countries. The Sudd wetland of South Sudan is the largest freshwater wetland on the Nile River and supports transhumant pastoral cultures and wildlife migrations; yet, it is understudied in terms of its regional hydrologic controls and corresponding changes in land cover. To assess the use of remote data derived from remote sensing systems, the wetland extent between 2000 and 2014 was delineated using MODIS thermal infrared data and baseline changes in the wetland land cover extents were mapped using NDVI products. The upper White Nile basin was subdivided into seven natural sub-basins and correlations between sub-basin precipitation amounts and wetland extents were determined. Areas of high spatial variability in land cover were related to the spatial distribution of conflict in South Sudan. Results indicate that downstream sub-basins, which have largely been ignored, display high correlations (0.58–0.74) with the wetland extent and appear to be major sources of water for the wetland. Precipitation patterns in the Lake Kyoga sub-basin, instead of the neighboring torrent sub-basin that has previously been cited, correspond to inter-annual changes in wetland extents. Land cover transitions in years of reduced flow amounts varied depending upon the precipitation in upstream and downstream sub-basins. Because the downstream sub-basins appear to have a higher influence than previously anticipated, a reduction in water from the upstream sub-basins didn't necessarily lead to a reduced wetland extent. The most highly variable regions of land cover changes correspond not only to locations of conflict, but also to ethnic boundary lines. Though the conflict in this region makes in-situ data collection difficult, the conflict only increases the need for scientific assessments in order to predict changes in ecosystem services.

An analysis of forest land use, forest land cover and change at policy-relevant scales

Quantifying the amount of forest and change in the amount of forest are key to ensure that appropriate management practices and policies are in place to maintain the array of ecosystem services provided by forests. There are a range of analytical techniques and data available to estimate these forest parameters, however, not all ‘forest’ is the same and various components of change have been presented. Forest as defined by use and forest as defined by cover are different, although it is common for scientists and policy makers to infer one from the other. We compare and contrast estimates of forest land cover, forest land use, extent and change at policy-relevant scales in the southeastern US. We found that estimates of forest land use extent and forest land cover extent were not significantly correlated. Estimates of net change based on forest land cover and forest land use were only moderately correlated and net change estimates were independent of gross forest cover loss estimates.

Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).

Assessing the impacts of watershed indexes and precipitation on spatial in-stream E. coli concentrations

Pathogen contamination of waterbodies, which is often identified by the presence of pathogen indicators such as Escherichia coli, is a major water quality concern in the United States. Reducing in-stream pathogen contamination requires an understanding of the combined impacts of land cover, climatic conditions, and anthropogenic activities at the watershed scale. In this study these factors are considered by assessing linear relationships between in-stream E. coli water quality data, watershed indexes, and rainfall for the Squaw Creek Watershed, IA, USA. The watershed indexes consider the undisturbed land cover which encompasses the natural land cover area, wetlands, and vegetated stream corridors, and the disturbed land cover extent which includes areas receiving manure from confined animal feeding operations (CAFOs), tile-drained areas, and areas in cropped and urban land. In addition to disturbed and undisturbed land, we also calculated indexes for barren land and slope. Bivariate analysis was used to assess the linkage between E. coli concentrations, watershed indexes and the cumulative rainfall 15, 30, 45, and 60 days prior to water sample collection. To predict in-stream E. coli concentrations, we developed multivariate regression models, and predictions were compared with observed E. coli concentrations at 46 sampling locations over four sampling periods in two years. Results show that areas receiving manure, wetlands, drained land, and cropped land all influence in-stream E. coli concentrations significantly (p<0.001). The coefficient of determination was higher when indexes were corrected using the cumulative rainfall 30 days prior to the sampling event. Model skill varied from 0.29 to 0.55. More than 95% of the predictions across all spatial locations fall within one order of magnitude of the observed values. This Geographic Information System (GIS) based approach for predicting in-stream E. coli concentrations appears to be a useful technique for assessing the impacts of land management on water quality.

Remote sensing of land cover classes as type 2 fuzzy sets

Abstract In recognition of the idea that land cover classes are vague phenomena, a number of researchers have advocated modelling of Land Cover as fuzzy sets, as opposed to Boolean sets. Once vagueness is admitted, it is not sufficient to stop at modelling land cover simply as a fuzzy set, because any threshold map (α-cut) of the fuzzy set is itself a Boolean set. Rather it may be appropriate to model it as a type 2 fuzzy set recognising the existence of higher order vagueness. Using the example of estimating land cover extents in an area of the forest–savanna ecotone in Bolivia, this paper presents the results and possible advantages of a type 2 fuzzy analysis of land cover. The consideration of type 2 fuzzy sets reveals the depth of the uncertainty, and allows the analyst to be specific about minimum, maximum, and average extents of land cover types and even to report fuzzy area itself as a fuzzy number and to justify descriptive qualifications of the results. The nuanced analysis, which is possible with this approach, seems an advance for the scientific community concerned with the conceptualisation of land cover classes, but it may be a nuisance for those involved in policy matters.

Regional land cover mapping in the humid tropics using combined optical and SAR satellite data—a case study from Central Sulawesi, Indonesia

The frequent mapping of the spatial extent of land cover and its change from satellite data at the regional level provides essential input to spatially explicit land use analysis and scenario modelling. The accuracy of a land cover map is the key factor describing the quality of a map, and hence affecting the results of land use modelling. In tropical regions, land cover mapping from optical satellites is hampered by cloud coverage and thus alternative data sources have to be evaluated. In the present study, data from Landsat‐ETM+ and Envisat‐ASAR satellite sensors were tested for their ability to assess small scaled landscape patterns in a tropical environment. A focus was on the detection of intensively managed perennial and intra‐annual cropping systems (cocoa, rice). The results confirm previous knowledge about the general potential and advantages of multi‐temporal SAR data compared to mono‐temporal SAR‐based mapping but also show the limitations of different polarization modes in SAR analysis for land cover mapping. In the present case study, cross‐polarized data from Envisat‐ASAR did not yield notable profit for tropical land cover mapping compared to common, co‐polarized time series of ASAR data. However, the general outcome of the study underlines the synergy of optical and radar satellite data for land cover mapping in tropical regions.