A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

Systematically linking qualitative elements of scenarios across levels,scales, and sectors

New scenarios for climate change research connect climate model results based on Representative Concentration Pathways to nested interpretations of Shared Socioeconomic Pathways. Socioeconomic drivers of emissions and determinants of impacts are now decoupled from climate model outputs. To retain scenario credibility, more internally consistent linking across scales must be achieved. This paper addresses this need, demonstrating a modification to cross impact balances (CIB), a method for systematically deriving qualitative socioeconomic scenarios. Traditionally CIB is performed with one cross-impact matrix. This poses limitations, as more than a few dozen scenario elements with sufficiently varied outcomes can become computationally infeasible to comprehensively explore. Through this paper, we introduce the concept of ‘linked CIB’, which takes the structure of judgements for how scenario elements interact to partition a single cross-impact matrix into multiple smaller matrices. Potentially, this enables analysis of large CIB matrices and ensures internally consistent linking of scenario elements across scales.     

Developing land use/land cover parameterization for climate–land modelling in East Africa

Regional climate modeling studies now have numerous choices in selecting land use/land cover (LULC) products to provide land surface parameter information. The various LULC products were developed with different objectives, methods and data sources. Not all new LULC products have land classes that match the land class types defined in climate models. More importantly, when used in regional climate models, simulation results can vary significantly depending on the LULC products. Thus, developing appropriate LULC parameterization for climate models becomes critical depending on objectives and efforts. The objective of this paper is to develop the most accurate LULC scheme possible for East Africa for implementation in the Regional Atmospheric Modeling System (RAMS). A crosswalk procedure, based on assessments of various LULC products, was performed connecting land class types in RAMS and the newly created LULC scheme. No simulations are discussed here; rather, we present an outline of the procedures that were carried out to take advantage of the strengths of currently available LULC products, Africover and Global Land Cover 2000, for the purpose of conducting regional climate simulations.     

A Markovian and cellular automata land-use change predictive model of the Usangu Catchment

Usangu Catchment, in Tanzania, is vital for its rice production in which more than 30% of Tanzanian rice is grown. The catchment is a part of the Southern Agricultural Corridor of Tanzania where major agricultural intensification is expected to take place. Given the role of this catchment, it is important to investigate the effect of agricultural intensification, land-use/land-cover (LULC) change and climate variability on water balance in the catchment. Thus, the objective of the study was to simulate Usangu Catchment’s LULC of 2020 based on LULC of 2000, 2006 and 2013 using Markov Chain and Cellular Automata Analysis.Social, edaphic, climatic and landscape geomorphology factors governing the LULC change and distribution were used to prepare LULC suitability maps in geographical information system.The relative importance of LULC change factors was determined using the analytic hierarchy process and aggregated using weighted linear combination under multi-criteria evaluation approach. The model was validated using simulated and observed LULC 2013. The standard kappa coefficient (κ-standard) and overall agreements of the model were 0.6776 and 0.9125, respectively. The error due to quantity is 0.0243 while error due to allocation is 0.0667. The simulated LULC 2020 scenario shows the increase in urban area by 8.2% and a major decrease in forestland and shrubs by 20.6% and 6.9%, respectively. About 19.6% grassland and 8.5% of agricultural land in 2013 will be converted to urban land by 2020. On the other hand, about 372.0 km² (10.4%) of wetlands and 368.2 km² (10.3%) of woodlands will be converted to agricultural land. The 2020 LULC simulation model of Usangu developed in this study provide some useful information for future LULC scenarios and data for water balance models and preparation of future ecological conservation plans.     

Land‐cover change and environmental impact analysis in the Greater Mankato area of Minnesota using remote sensing and GIS modelling

Land use and land‐cover (LULC) data provide essential information for environmental management and planning. This research evaluates the land‐cover change dynamics and their effects for the Greater Mankato Area of Minnesota using image classification and Geographic Information Systems (GIS) modelling in high‐resolution aerial photography and QuickBird imagery. Results show that from 1971 to 2003, urban impervious surfaces increased from 18.3% to 32.6%, while cropland and grassland decreased from 54.2% to 39.1%. The dramatic urbanization caused evident environmental impacts in terms of runoff and water quality, whereas the annual air pollution removal rate and carbon storage/sequestration remained consistent since urban forests were steady over the 32‐year span. The results also indicate that highly accurate land‐cover features can be extracted effectively from high‐resolution imagery by incorporating both spectral and spatial information, applying an image‐fusion technique, and utilizing the hierarchical machine‐learning Feature Analyst classifier. This research fills the high‐resolution LULC data gap for the Greater Mankato Area. The findings of the study also provide valuable inputs for local decision‐makers and urban planners.     

Spatiotemporal analysis of land-use and land-cover change in the Brazilian Amazon

This paper provides a comparative analysis of land-use and land-cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired during the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes – forest, savanna, other vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water – was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition, and rates among the three study areas and indicates the importance of analysing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g. urban expansion, roads, and land-use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.     

Effects of spatial and temporal climatic variability on terrestrial carbon and water fluxes in the Pacific Northwest,USA

The Pacific Northwest (PNW) of the conterminous United States is characterized by large variations in climate and topography, and provides an ideal geographic domain for studying interactions between regional climate and vegetation dynamics. We examined vegetation carbon (C) and water dynamics along PNW climate and topographic gradients using a process-based biogeochemical model, BIOME-BGC, the algorithms of which form bases for a fully-prognostic treatment of carbon and nitrogen cycles in Land Community Model (CLM). Simulation experiments were used to (1) analyze spatial and temporal variability of terrestrial carbon (C) stocks and flux, (2) investigate primary climatic variables controlling the variability, and (3) predict effects of future climate projections on vegetation productivity and water flux variables including evapotranspiration and water supply. The model experiments focused on two 18-year (1980–1997 and 2088–2105) simulations using future climate predictions for A2 (+4.2 °C, −7% precipitation) and B2 (1.6 °C, +11% precipitation) emissions scenarios through year 2100. Our results show large west to east spatial variations in C and water fluxes and C stocks associated with regional topography and distance from coastal areas. Interannual variability of net primary productivity (NPP) and evapotranspiration (ET) are 57% and 33%, respectively, of the 18-year mean annual fluxes for 1980–1997. The annual NPP and ET are positively correlated with precipitation but inversely proportional to vapor pressure deficit; this suggests that modeled NPP and ET are predominantly water limited in the PNW. The A2 scenario results in higher NPP and ET of 23% and 10%, respectively, and 15% lower water outflow. The B2 scenario results in higher NPP and ET of 12% and 15%, respectively, and 2% lower water outflow, despite projected increases in precipitation. Simulation experiments indicate that most PNW ecosystems are water limited, and that annual water outflow will decrease under both drier (A2) and wetter (B2) scenarios. However, higher elevations with high snowpacks of long duration may buffer the loss of water resources in some areas, even if precipitation is lower.     

Use of a weather generator for simulating climate change effects on ecosystems: A case study on Lake Constance

We present a new vector-autoregressive weather generator developed to generate meteorological time series for climate impact studies on ecosystems.As an example, the weather generator was applied in combination with a hydrodynamic-ecological lake model (DYRESM-CAEDYM). The effects of a warmer and more variable climate on hydrodynamics and phytoplankton in large monomictic lakes were analysed.The weather generator reproduced dependency structures of measured meteorological data. Variability was altered at a time scale similar to lengths of synoptic disturbances, resulting in longer than day-to-day fluctuation changes.Sensitivity of spring bloom development towards a warmer climate, increased climate variability and a combination of both was addressed. For this purpose, 500 meteorological time series per scenario were generated as input for the lake model. We found that onset and maximum of phytoplankton spring bloom are sensitive towards spring weather conditions and that an increase in variability favours early as well as late blooms.     

沂蒙山区植被NDVI的时空特征及其对水热条件的响应

植被是生态环境变化的综合指示器,研究其对水热条件的响应已成为当前气候变化研究中的主要内容之一。选取北方土石山区典型代表--沂蒙山区为研究对象,基于沂蒙山区1980~2010年的气温、降水和2001~2010年MODIS\|NDVI数据,结合相关分析和最小二乘法,定量分析该区植被指数的年际、年内的时空变化及其对水热条件的响应。结果表明:①近10 a沂蒙山区NDVI max的变化斜率为0.0026;②植被显著退化区和良好区分别占研究区总面积的10.52%和28.62%;③不同季节(主要是春、夏和秋季)植被状况均呈现良性发展趋势;④台站数据显示植被年际变化与年降水和年均气温的关系并不密切,而在月时间尺度上植被与气温的相关性要强于与降水的相关性。综上所述,沂蒙山区植被状况总体呈良性发展趋势,气温可能是影响该区植被生长的主导因子。     

Assessing the long-term impact of urbanization on run-off using a remote-sensing-supported hydrological model

Land use/land cover (LULC) change in Dongguan, a city belonging to the Pearl River Delta metropolitan area, and its impact on the run-off of this city since the rapid urbanization period starting in 1979 are analysed in this article. Historic remote-sensing imagery (acquired in 1979, 1989, 2000, 2006, and 2013) was processed by the Support Vector Machine (SVM) method to obtain LULC data, and a long-term hydrologic impact assessment (L-THIA) model was applied to evaluate the long-term effects of LULC changes on surface run-off. The results show the urbanized area in Dongguan has increased more than 52% from 1979 to 2013 and the percentages of annual surface run-off depth and annual surface run-off coefficient are 58% and 5.83%, respectively. The increase in annual surface run-off is related to urbanization, and the centre area of the city has experienced the largest increase in annual surface run-off. The 32 towns’ local indicators of spatial association show centre towns are hot spots of increases in annual surface run-off in Dongguan city.     

Spectral analysis of civil conflict-induced forced migration on land-use/land-cover change: the case of a primate and lower-ranked cities in Sierra Leone

Involuntary migration triggered by war has the capacity to generate substantial socioeconomic and environmental changes in cities of developing countries, resulting in aberrant alterations to land use and land cover (LULC). This scenario has the potential to diminish the quality of life of inhabitants of a city and present administrative challenges for government and other officials. Gauging the scope and trajectory of urban LULC changes in a war-related environment is pivotal for urban and regional planning, the sustainability of natural resources, and information needs of policy makers. Scholarships that link remote sensing to social science mostly focus on a non-conflict environment, resulting in little or no information on the ramifications of conflict-induced forced migration on changes in LULC. As a result, the role of civil conflict-induced forced migration on the composition and configuration of urban landscape in developing countries remains elusive. This study employs a dense time stack of Landsat-5 Thematic Mapper (TM) images and a hybrid classification approach that integrates linear spectral unmixing and an ensemble decision-tree classifier to characterize LULC in a primate city and two lower-ranked cities in Sierra Leone. The study examined three time-steps which span 1986–1991, 1991–2002, and 2002–2010 with the overarching goal of elucidating changes in LULC conditioned by civil conflict. Image classification accuracy (overall accuracy) ranged between 84.0% and 90.2%. The study demonstrated that civil conflict has the capacity to trigger notable growth in urban agricultural land (37.4%) in a primate city, while the expansion of residential (112.7%) and industrial/commercial (18.7%) lands is more prominent in a lower-ranked city. The study further revealed that population expansion does not necessarily result in significant growth in residential area in a primate city that has experienced civil conflict.     

Impact of climate model uncertainties on socioeconomics: A case study with a medium mitigation scenario

Carbon dioxide (CO₂) emissions are strongly associated with economy. The amount of CO₂ that human society can emit in order to achieve a climate target depends on physical and biogeochemical properties in the climate system; these vary among climate models or earth system models (ESMs). Thus, uncertainties in such models, the spread remained when we both consider the range of existing models and observational data for key variables, can affect analysis of future global economy. In this study, using a computable general equilibrium model, we analyze the impacts on socioeconomics under a medium climate mitigation scenario by following three emission pathways considering uncertainties in existing ESMs (the lower and upper bounds as well as the mean). The results indicate that the impacts are larger in the lower bound case, despite the fact that economic and energy demands will increase continuously. In a comparison between the upper and lower bound cases, the carbon price of the latter case is approximately three times higher than that of the former case in 2100. Consequently, primary/final energy demand in the lower bound case becomes 1.0%/14% lower, and more renewables and carbon capture and storage are required to be used. Furthermore, the gross domestic product in the lower bound case is 4.1% smaller. Thus, within the scenario, the socioeconomic impacts caused by ESM uncertainties are not insignificant, but are smaller than the differences in annual and cumulative emissions.     

Evaluating four downscaling methods for assessment of climate change impact on ecological indicators

Assessments of climate change impacts on freshwater ecosystems are generally based on global climate models (GCMs) and ecologically relevant “time-averaged” hydrological indicators derived from long-term records. Although uncertainties from GCMs have been recognized, the influence of downscaling methods remains unclear. This paper evaluates the influence of applying different downscaling methods of increasing complexity (annual scaling, monthly scaling, quantile scaling, and weather generator method) on the assessment of ecological outcomes. In addition to time-averaged indicators, “sequence-dependent” metrics which involve ecological dynamics by considering the impacts of flow sequencing are also adopted. In a case study in Australia, the condition of river red gum forest was assessed. Results show that the choice of downscaling methods can be of similar importance as that of GCMs in ecological impact studies. Where sequence-dependent metrics are adopted, more sophisticated downscaling techniques should be used to better represent changes in the frequency and sequence of flow events.     

Monitoring annual urbanization activities in Guangzhou using Landsat images (1987–2015)

Rapid land-use/land-cover (LULC) changes such as urbanization have tremendous impacts on regional climate and environment. Satellite images acquired by fast-developing remote-sensing techniques provide frequent observations of the land surface, thereby allowing for continuous mapping of urbanization activities. In this study, we investigated the annual urbanization activities over the past three decades in Guangzhou, one of the largest metropolises in China. To enhance the efficiency of training sample extraction in long-term land-cover mapping, we developed a three-step method: 1) three spectral indices were derived to extract the candidates of training samples based on decision trees; 2) a spatial filter was used to extract homogenous samples for each land-cover type; and 3) temporal consistency checking was performed for the samples of urban areas. We applied the developed method to time-series Landsat images and produced annual land-cover maps of Guangzhou from 1987 to 2015. We evaluated the produced land-cover maps and found an average overall accuracy of 89.80% for all studied years. Our results show that dramatic urbanization has occurred in the region of the Guangzhou city, where built-up areas have mostly expanded to the northwest, east, and south of the central regions of Guangzhou. The average growth rate of urban areas in Guangzhou from 1987 to 2015 was at 38.72 km² per year, which was generally consistent with the government survey data. Future studies are required to understand how rapid urbanization in Guangzhou influences social economy and environmental sustainability.     

Harmonic amplitude-terms mask to highlight agriculture in the savanna domain below the Brazilian Amazonian frontier

In the present decade, agricultural monitoring has increased in importance world-wide mainly due to sensitive matters, such as the current food crises, bio-fuels boom and the land-use/land-cover (LULC) changes from savanna (Brazilian Cerrados) and forest areas to agricultural expansion. In this complex context there is a requirement for quick and operational procedures to monitor change. We propose a simple but effective technique to mask out and identify annual crop areas (e.g. soybean) in the Brazilian Amazonian frontier using lower-order terms from a harmonic analysis approach derived by the Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Preliminary results highlighted how annual crop areas and planted pasture from other vegetal units are easily separated using threshold slicing to generate a binary-mask of annual crops. Comparison with the reference map reached an overall accuracy of 91.89%, with overall kappa statistics of 0.8238.     

Application of spectral mixture analysis to Amazonian land-use and land-cover classification

Abundant vegetation species and associated complex forest stand structures in moist tropical regions often create difficulties in accurately classifying land-use and land-cover (LULC) features. This paper examines the value of spectral mixture analysis (SMA) using Landsat Thematic Mapper (TM) data for improving LULC classification accuracy in a moist tropical area in Rondônia, Brazil. Different routines, such as constrained and unconstrained least-squares solutions, different numbers of endmembers, and minimum noise fraction transformation, were examined while implementing the SMA approach. A maximum likelihood classifier was also used to classify fraction images into seven LULC classes: mature forest, intermediate secondary succession, initial secondary succession, pasture, agricultural land, water, and bare land. The results of this study indicate that reducing correlation between image bands and using four endmembers improve classification accuracy. The overall classification accuracy was 86.6% for the seven LULC classes using the best SMA processing routine, which represents very good results for such a complex environment. The overall classification accuracy using a maximum likelihood approach was 81.4%. Another finding is that use of constrained or unconstrained solutions for unmixing the atmospherically corrected or raw Landsat TM images does not have significant influence on LULC classification performances when image endmembers are used in a SMA approach.     

Optimizing unsupervised classifications of remotely sensed imagery with a data-assisted labeling approach

The quality of remotely sensed land use and land cover (LULC) maps is affected by the accuracy of image data classifications. Various efforts have been made in advancing supervised or unsupervised classification methods to increase the repeatability and accuracy of LULC mapping. This study incorporates a data-assisted labeling approach (DALA) into the unsupervised classification of remotely sensed imagery. The DALA-unsupervised classification algorithm consists of three steps: (1) creation of N spectral-class maps using Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA); (2) development of LULC maps with assistance of reference data; and (3) accuracy assessments of all the LULC maps using independent reference data and selection of one LULC map with the highest accuracy. Classification experiments with a composite image of a Landsat Thematic Mapper (TM) image and an Enhanced Thematic Mapper Plus (ETM+) image suggest that DALA was effective in making unsupervised classification process more objective, automatic, and accurate. A comparison between the DALA-unsupervised classifications and some conventional classifications suggests that the DALA-unsupervised classification algorithm yielded better classification accuracies compared to these conventional approaches. Such a simple, effective approach has not been systematically examined before but has great potential for many applications in the geosciences.     

Water resources of Cyprus under changing climatic conditions: Modelling approach,validation and limitations

We examine trends in the water resources of Cyprus by focussing on water flux changes in the important Kouris catchment. Our modelling approach is general and is a synthesis of an adapted conceptual daily rainfall-runoff model, radiation transfer models that use high resolution MODIS satellite climatological data and GCM scenarios for future climatic change. We used climatic data as input to our models, downscaled to the catchment resolution from two climate scenarios: the mild RCP2.6 and the extreme RCP8.5, to estimate water resources by the end of the 21st century. The models show that the present mean annual rainfall resource of 174 Mm³ will be reduced to 162 Mm³ and 132 Mm³, for the mild and extreme scenario, respectively. The present mean discharge of 21.5 Mm³ into the Kouris dam from the catchment will decrease to 16.6 Mm³ and 6.9 Mm³ under the mild and extreme scenario, respectively.     

Integrating socio-economic and biophysical data to support water allocations within river basins: An example from the Inkomati Water Management Area in South Africa

Sustainable natural resource management requires inputs from both the natural and the social sciences. Since natural and social systems are inter-related and inter-dependent, it is essential that these data can be integrated within a given analysis, which requires that they are spatially compatible. However, existing environmental and socio-economic monitoring networks tend to observe, collect and report socio-economic and biophysical data separately; with the result that much of these data are spatially incompatible, adding to the complexity of objective and consistent resource management. We present an approach for overcoming spatial incompatibilities between socio-economic and biophysical data; based on a meta-modelling approach using Geographical Information Systems and an application of a water-use simulation model. The method is developed and applied to the irrigation agriculture sector in the Inkomati Water Management Area in South Africa. Agricultural census data, which are measured on a magisterial district scale, are integrated with geo-referenced land-cover data, which are independent of political boundaries. This allows us to increase the resolution at which data on the economic value derived from irrigation water are presented, from coarse magisterial district scale to a finer ‘meso-zone’ scale, enabling more efficient allocations of irrigation water within magisterial districts.