Land Use Cover Types Research Articles

TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.

TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT

Abstract. Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.

Surface energy balance algorithm for land-based consumption water use of different land use-cover types in arid-semiarid regions

Spatiotemporal distributions of water consumption for various land use-cover types over the Eastern province of Saudi Arabia were estimated using Surface Energy Balance Algorithm. Water consumption of various land use and cover classes shows similar seasonal dynamic trends. The spatial distribution of annual actual evapotranspiration (AET) shows low values in the Empty Quarter (231–438 mm/yr), and moderate values in the Eastern Province borders (439–731 mm/yr). Very high AET values were observed in irrigated croplands in the Northern plains, Hafar Al-Batin, the central coastal lowlands, and the southern coastal lowlands, where annual AET ranged from 732 to 1,790 mm/yr, representing the majority of the study area agricultural land. Evaporative behavior of land use-cover types indicated that irrigated cropland, which occupies 0.37% of the study area, has an average daily AET ranging from 9.2 mm/day in January to a maximum value in April (30 mm/day). Average annual water use by irrigated cropland is relatively very high and it is roughly 1,786.9 mm/yr, while water bodies, which cover 0.023% (121.2 km2) of the study area, also had relatively high mean AET (660.8 mm/yr). Overall, AET rates for irrigated cropland are much higher than for other land uses.

Integrating Global Satellite-Derived Data Products as a Pre-Analysis for Hydrological Modelling Studies: A Case Study for the Red River Basin

With changes in weather patterns and intensifying anthropogenic water use, there is an increasing need for spatio-temporal information on water fluxes and stocks in river basins. The assortment of satellite-derived open-access information sources on rainfall (P) and land use/land cover (LULC) is currently being expanded with the application of actual evapotranspiration (ETact) algorithms on the global scale. We demonstrate how global remotely sensed P and ETact datasets can be merged to examine hydrological processes such as storage changes and streamflow prior to applying a numerical simulation model. The study area is the Red River Basin in China in Vietnam, a generally challenging basin for remotely sensed information due to frequent cloud cover. Over this region, several satellite-based P and ETact products are compared, and performance is evaluated using rain gauge records and longer-term averaged streamflow. A method is presented for fusing multiple satellite-derived ETact estimates to generate an ensemble product that may be less susceptible, on a global basis, to errors in individual modeling approaches. Subsequently, monthly satellite-derived rainfall and ETact are combined to assess the water balance for individual subcatchments and types of land use, defined using a global land use classification improved based on auxiliary satellite data. It was found that a combination of TRMM rainfall and the ensemble ETact product is consistent with streamflow records in both space and time. It is concluded that monthly storage changes, multi-annual streamflow and water yield per LULC type in the Red River Basin can be successfully assessed based on currently available global satellite-derived products.

Impacts of land use change on ecosystem services and implications for human well-being in Spanish drylands

The arid southeastern Iberian Peninsula is a unique region in which conservation and human development have coexisted and coevolved over many decades. However, conflicts between economic development and conservation have generated increasing concern due to the rapid expansion of greenhouse horticulture and the abandonment of rural and mountainous areas. Human decisions regarding land use management have affected the status of ecosystems and therefore the ecosystem services supply. We identified four land use-land cover changes that summarize the most common management decisions. These include greenhouse horticulture expansion, urban intensification, rural abandonment, and conservation actions, which occur through protected area declarations. This study aims to explore the social relevance of land use-land cover changes on the delivery of eight key services provided by Spanish arid ecosystems as follows: provisioning services related to intensive and traditional agriculture, regulating services associated with water regulation, climate regulation, air quality and erosion control, and cultural services linked to local identity and tourism. Through 402 face-to-face questionnaires, we analyzed the arguments for and against these four land use-land cover types. We also assessed their impact on ecosystem services and the social importance and vulnerability of ecosystem services. We found significant differences in the social perception of the positive and negative impacts of land use types on ecosystem services. The sample population recognized the negative impacts of greenhouse horticulture on regulating services that are, in terms of water regulation, essential for the sustained delivery of final provisioning services related to agricultural activities. Overall, traditional agriculture and tourism are recognized as the most important services. A controversy between the two opposite models of territorial development – urban development and nature conservation – highlights the need to promote new strategies of land management. Finally, we discuss the usefulness of this approach to understand the arguments affecting the promotion of land use-land cover changes and to visualize the ecosystem service trade-offs under different management strategies.

Effects of toposequence and land use-land cover on the spatial distribution of soil properties

Topographic properties can influence pedogenic processes, soil profile depth and spatial variations of soil properties. The objectives of this study were to determine the spatial variation of soil properties at different slope positions and land use-land cover and also the relationships among soil properties, slope and land use-land cover. The study area was divided into five topographic positions by an elevation transect from 935 to 1370 m. These positions were shoulder, flat plateau-1, flat plateau-2, lower back slope, and toe slope which were sampled at 10 × 20 m grid size. A total of 500 soil samples were collected from 0 to 20 cm depth. Soil properties showed significant variation among slope positions and land use-land cover type when analysed with ANOVA and post hoc multiple comparison tests. Slope was responsible for large discrimination effects compared to land use-land cover. It was also the main factor contributing to the transportation and accumulation of fine soil particles when soil organic matter content and water stable aggregates amount were low. Moreover, different land use-land cover type affected soil organic matter content and water stable aggregate amount and caused significant variance in them. Overall, the spatial variability of soil physico-chemical properties was strongly affected by pedogenic factors, slope and land use.

Spatio-Temporal Modeling of the Urban Heat Island in the Phoenix Metropolitan Area: Land Use Change Implications

This study examines the spatial and temporal patterns of the surface urban heat island (SUHI) intensity in the Phoenix metropolitan area and the relationship with land use land cover (LULC) change between 2000 and 2014. The objective is to identify specific regions in Phoenix that have been increasingly heated and cooled to further understand how LULC change influences the SUHI intensity. The data employed include MODerate-resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) 8-day composite June imagery, and classified LULC maps generated using 2000 and 2014 Landsat imagery. Results show that the regions that experienced the most significant LST changes during the study period are primarily on the outskirts of the Phoenix metropolitan area for both daytime and nighttime. The conversion to urban, residential, and impervious surfaces from all other LULC types has been identified as the primary cause of the UHI effect in Phoenix. Vegetation cover has been shown to significantly lower LST for both daytime and nighttime due to its strong cooling effect by producing more latent heat flux and less sensible heat flux. We suggest that urban planners, decision-makers, and city managers formulate new policies and regulations that encourage residential, commercial, and industrial developers to include more vegetation when planning new construction.

Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines

We explored the potential for rapid land use/land cover (LULC) mapping using time-series Landsat satellite imagery and training data (for supervised classification) automatically extracted from crowdsourced OpenStreetMap (OSM) “landuse” (OSM-LU) and “natural” (OSM-N) polygon datasets. The main challenge with using these data for LULC classification was their high level of noise, as the Landsat images all contained varying degrees of cloud cover (causes of attribute noise) and the OSM polygons contained locational errors and class labeling errors (causes of class noise). A second challenge arose from the imbalanced class distribution in the extracted training data, which occurred due to wide discrepancies in the area coverage of each OSM-LU/OSM-N class. To address the first challenge, three relatively noise-tolerant algorithms – naïve bayes (NB), decision tree (C4.5 algorithm), and random forest (RF) – were evaluated for image classification. To address the second challenge, artificial training samples were generated for the minority classes using the synthetic minority over-sampling technique (SMOTE). Image classification accuracies were calculated for a four-class, five-class, and six-class LULC system to assess the capability of the proposed methods for mapping relatively broad as well as more detailed LULC types, and the highest overall accuracies achieved were 84.0% (four-class SMOTE-RF result), 81.0% (five-class SMOTE-RF result), and 72.0% (six-class SMOTE-NB result). RF and NB had relatively similar overall accuracies, while those of C4.5 were much lower. SMOTE led to higher classification accuracies for RF and C4.5, and in some cases for NB, despite the noise in the training set. The main advantages of the proposed methods are their cost- and time-efficiency, as training data for supervised classification is automatically extracted from the crowdsourced datasets and no pre-processing for cloud detection/cloud removal is performed.

Spatial driving forces of dominant land use/land cover transformations in the Dongjiang River watershed, Southern China.

Information about changes in, and causes of, land use/land cover (LULC) is crucial for land use resource planning. We investigated the processes involved in LULC change (LUCC) in the Dongjiang Watershed, in Southern China, over a 15-year period to gain a better understanding of the causes of the main types of LUCC. Using a depth transition matrix and redundancy analysis (RDA), the major types and causes of LUCC for each LULC type over the past 15 years were identified. LUCC exhibited obvious net change, relatively low persistence, and high swap change. The swap changes in most LULC types were considered as a strong signal of LULC transformations. The driving forces behind swap changes were quantified and identified through RDA. The results showed that all driving forces played important roles in explaining swap changes of LULC, although the relative effects of these drivers varied widely with both LULC type and time period. Swap changes of the LULC types were generally classified into two categories. Some, e.g., built-up land and wetland, were affected mostly by landform and/or distance factors, while others, e.g., grassland and woodland, were modulated mostly by climate and/or socioeconomic factors. Selected spatial driving forces and local land use policies played important roles in explaining the dominant LUCC types, but on different timescales. These findings may improve understanding of the detailed processes involved in LUCC, landscape transformation, and the causes of LUCC in other areas with extensive LUCC and could help managers plan, design, and implement land resource management.

Determination of Urban Thermal Characteristics on an Urban/Rural Land Cover Gradient Using Remotely Sensed Data

The transformation from natural to impervious surfaces in an urbanization process and the urban heat island (UHI) phenomenon is known to significantly compromise urban environmental quality and has been linked to climate change and associated impacts. Whereas the existence of UHI is common knowledge, the implication of urban land use land cover (LULC) gradient on intra-urban thermal characteristics is often poorly understood. A recent proliferation of remotely sensed datasets offer great potential in understanding the relationship between urban LULCs and their respective thermal characteristics, a critical basis for urban environmental management and designing climate change mitigation measures. This study explores the potential of multispectral remotely sensed dataset in determining the influence of rural/urban LULC gradient on urban thermal characteristics. A rectangular eleven band Landsat 8 image subset was delineated from the central business district to the rural periphery and classified into most dominant LULCs and a corresponding Landsat 8 thermal layer used to determine the LULCs thermal characteristics. Digitized point data was used to determine differences in land surface temperature (LST) over gradient's LULC types. Results showed that there was varied contribution of LULCs to the LST. As expected, the density of built up surfaces and LST decreased towards the city’s periphery while a decline in vegetation density from the periphery led to an increase in LST. These results provide valuable insights into the value of remotely sensed datasets in understanding the implication of intra-urban LULC gradient on LST characteristics. Specifically, the study demonstrates the value of remotely sensed data as aids to sustainable urban environmental planning.

Image Segmentation Parameter Optimization Considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels: Test Case for Mapping Residential Areas Using Landsat Imagery

Multi-scale/multi-level geographic object-based image analysis (MS-GEOBIA) methods are becoming widely-used in remote sensing because single-scale/single-level (SS-GEOBIA) methods are often unable to obtain an accurate segmentation and classification of all land use/land cover (LULC) types in an image. However, there have been few comparisons between SS-GEOBIA and MS-GEOBIA approaches for the purpose of mapping a specific LULC type, so it is not well understood which is more appropriate for this task. In addition, there are few methods for automating the selection of segmentation parameters for MS-GEOBIA, while manual selection (i.e., trial-and-error approach) of parameters can be quite challenging and time-consuming. In this study, we examined SS-GEOBIA and MS-GEOBIA approaches for extracting residential areas in Landsat 8 imagery, and compared naïve and parameter-optimized segmentation approaches to assess whether unsupervised segmentation parameter optimization (USPO) could improve the extraction of residential areas. Our main findings were: (i) the MS-GEOBIA approaches achieved higher classification accuracies than the SS-GEOBIA approach, and (ii) USPO resulted in more accurate MS-GEOBIA classification results while reducing the number of segmentation levels and classification variables considerably.

Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China.

Land use/land cover (LULC) inventory provides an important dataset in regional planning and environmental assessment. To efficiently obtain the LULC inventory, we compared the LULC classifications based on single satellite imagery with a rule-based classification based on multi-seasonal imagery in Lianyungang City, a coastal city in China, using CBERS-02 (the 2nd China-Brazil Environmental Resource Satellites) images. The overall accuracies of the classification based on single imagery are 78.9, 82.8, and 82.0% in winter, early summer, and autumn, respectively. The rule-based classification improves the accuracy to 87.9% (kappa 0.85), suggesting that combining multi-seasonal images can considerably improve the classification accuracy over any single image-based classification. This method could also be used to analyze seasonal changes of LULC types, especially for those associated with tidal changes in coastal areas. The distribution and inventory of LULC types with an overall accuracy of 87.9% and a spatial resolution of 19.5 m can assist regional planning and environmental assessment efficiently in Lianyungang City. This rule-based classification provides a guidance to improve accuracy for coastal areas with distinct LULC temporal spectral features.

Impacts of future land use/land cover on wildfire occurrence in the Madrid region (Spain)

This paper assesses the relative importance of socioeconomic factors linked to fire occurrence through the simulation of future land use/land cover (LULC) change scenarios in the Madrid region (Spain). This region is a clear example of the socioeconomic changes that have been occurring over recent decades in the European Mediterranean as well as their impact on LULC and fire occurrence. Using the LULC changes observed between 1990 and 2006 as a reference, future scenarios were run up to 2025 with the conversion of land use and its effects model. Simultaneously, the relationship between LULC arrangement (interfaces) and historical fire occurrence was calculated using logistic regression analysis and used to quantify changes in future fire occurrence due to projected changes in LULC interfaces. The results revealed that it is possible to explain the probability of fire occurrence using only variables obtained from LULC maps, although the explanatory power of the model is low. In this context, border areas between some LULC types are of particular interest (i.e., urban/forest, grassland/forest and agricultural/forest interfaces). Results indicated that expected LULC changes in Euro-Mediterranean regions, particularly given the foreseeable increase in the wildland–urban interface, will substantially increase fire occurrence (up to 155 %). This underlines the importance of future LULC scenarios when planning fire prevention measures.

Land cover-based ecosystem service assessment of irrigated rice cropping systems in southeast Asia—An explorative study

Continuing global population growth requires an increase in food production, but also new strategies to reduce negative effects of intensive land use on the environment. Rice as key staple food for a majority of the human population is of crucial importance for global and particularly Southeast Asian food supply. As food provision is one key ecosystem service (ES), it is important to know which ESs are provided at which places. Therefore, an ES scoring exercise harnessing local experts’ knowledge in a ‘rapid assessment’ was conducted in seven rice cropping regions in Vietnam and the Philippines. The expert-based scoring values were linked in an ‘ES-matrix’ to the different land use/land cover (LULC) classes abundant in the study areas. The LULC classifications were based on SPOT satellite image interpretation. The matrices were used to compile ES supply maps that give first indications about ES in regions with different intensive agriculture. The outcomes provide a first ‘screening’ of ES supply related to different LULC types in rice-dominated regions enabling the communication of the relevance of specific ecosystems for local communities and decision makers. Uncertainties inherent in expert- and land cover-based ES assessments are discussed and recommendations for improvements of future studies are given.

GIS and Logit Regression Model Applications in Land Use/Land Cover Change and Distribution in Usangu Catchment

This study applied time series analysis to examine land use/land cover (LULC) change and distribution in Usangu watershed and multinomial logistic regression in the GIS environment to model the influence of the related driving factors. Historical land use/cover data of the watershed were extracted from the 2000, 2006 and 2013 Landsat images using GIS and remote sensing data processing and analysis techniques. Data was analyzed using ArcMap 10.1, ERDAS Imagine, SPSS and IDRISI Selva software. Eight factors likely to influence LULC change and LULC distribution were assessed. These include elevation, slope, distance from roads, distance from rivers networks, population density, Normalized Vegetation Index (NDVI), annual rainfall and soil types. Results show that LULC changes are mainly influenced by variations in annual rainfall, population density and distance from road networks. LULC distribution is determined mainly by terrain and edaphic factors namely elevation, slope and soil types. NDVI does not influence LULC change nor determine the LULC distribution, but can be used to show concentration of LULC types on a landscape. Combination of GIS, remote sensing and statistical analysis capabilities are powerful tools for assessing and model processes of land use change and their underlying causes in terms of time and space. It is concluded that ingeniously integration of remote sensing, GIS application combined with multi-source spatial data analysis give great possibility of quantifying and explaining the temporal and spatial LULC changes and distribution in a given watershed.

Satellite-Based Investigation and Evaluation of the Observational Environment of Meteorological Stations in Anhui Province, China

In this paper, by using multi-temporal and high resolution Landsat data and geographic information system techniques, the land use/land cover (LULC) in the 2-km buffer zone of 52 meteorological stations in the Anhui province of China is retrieved and categorized into three types: vegetation (including farmland, forest and grass land), water (including lakes, rivers and pools), and construction (including buildings and roads). Besides, the land surface temperature (LST) in the buffer zone of these stations is also obtained from thermal infrared data. The normalized LST index (NLI) and the heat effect contribution index (HECI) of different LULC types are calculated. Via case studies and statistical analysis, the LULC and thermal environment’s temporal-spatial variance in the 2-km buffer zone of these stations are surveyed, and their impacts on the observational environment are investigated. The study shows that the observational environments of the meteorological stations in Anhui province have been greatly influenced by rapid urbanization. The study proposes two new methods to classify the stations’ observational environment into three types (urban, sub-urban, and rural). One uses the NLI and the other uses the HECI. The NLI method needs only LST information. The HECI method combines both LULC and LST information and, hence, is considered more reliable. The evaluation methods and criteria can be used conveniently, effectively, and quantitatively, and are especially useful when analyzing observational data from meteorological stations in weather and climate research and when choosing a location for a new meteorological station.

Comparative analysis of RISAT-1 and simulated RADARSAT-2 hybrid polarimetric SAR data for different land features

Abstract. The purpose of this study is to compare the performance of first hybrid polarimetric spaceborne satellite RISAT-1 data and simulated hybrid polarimetric data from quad-pol RADARSAT-2 data for different land use land cover (LULC) classes. The present study compares Stokes (g0, g1, g2 and g3) and its decomposed parameters (m, chi, delta and CPR) for satellite data acquired from RISAT- 1 and RADARSAT-2 over Vijayawada, Andhra Pradesh, India. Further, backscattering coefficients are also compared for different LULC types. The results indicate that both the satellites are following approximately the same trend for different classes except for settlements in RISAT-1.

Geographically weighted regression of land cover determinants of Plasmodium falciparum transmission in the Ashanti Region of Ghana.

BackgroundMalaria is a mosquito-borne parasitic disease that causes severe mortality and morbidity, particularly in Sub-Saharan Africa. As the vectors predominantly bite between dusk and dawn, risk of infection is determined by the abundance of P. falciparum infected mosquitoes in the surroundings of the households. Remote sensing is commonly employed to detect associations between land use/land cover (LULC) and mosquito-borne diseases. Due to challenges in LULC identification and the fact that LULC merely functions as a proxy for mosquito abundance, assuming spatially homogenous relationships may lead to overgeneralized conclusions.MethodsData on incidence of P. falciparum parasitaemia were recorded by active and passive follow-up over two years. Nine LULC types were identified through remote sensing and ground-truthing. Spatial associations of LULC and P. falciparum parasitaemia rate were described in a semi-parametric geographically weighted Poisson regression model.ResultsComplete data were available for 878 individuals, with an annual P. falciparum rate of 3.2 infections per person-year at risk. The influences of built-up areas (median incidence rate ratio (IRR): 0.94, IQR: 0.46), forest (median IRR: 0.9, IQR: 0.51), swampy areas (median IRR: 1.15, IQR: 0.88), as well as banana (median IRR: 1.02, IQR: 0.25), cacao (median IRR: 1.33, IQR: 0.97) and orange plantations (median IRR: 1.11, IQR: 0.68) on P. falciparum rate show strong spatial variations within the study area. Incorporating spatial variability of LULC variables increased model performance compared to the spatially homogenous model.ConclusionsThe observed spatial variability of LULC influence in parasitaemia would have been masked by traditional Poisson regression analysis assuming a spatially constant influence of all variables. We conclude that the spatially varying effects of LULC on P. falciparum parasitaemia may in fact be associated with co-factors not captured by remote sensing, and suggest that future studies assess small-scale spatial variation of vegetation to circumvent generalised assumptions on ecological associations that may in fact be artificial.Electronic supplementary materialThe online version of this article (doi:10.1186/1476-072X-13-35) contains supplementary material, which is available to authorized users.

A multi-scale accuracy assessment of the MODIS irrigated agriculture data-set (MIrAD) for the state of Nebraska, USA

Accurate and timely information about the geographic distribution of irrigated cropland is important for a range of applications including crop assessments, water resources management, drought monitoring, and environmental modeling. In the United States, a consistent, seamless irrigated agricultural lands map was not available until the development of the 250-m moderate resolution imaging spectroradiometer (MODIS) irrigated agriculture data-set (MIrAD), which was developed from time-series MODIS normalized difference vegetation index, county-level crop area statistics, and land use/land cover (LULC) data using an automated county-level classification approach. This study performed a detailed multi-scale assessment of the MIrAD over the state of Nebraska, which is extensively irrigated, to determine the thematic accuracy of classified irrigation patterns at various spatial scales (i.e., landscape, field, and subfield) and over various crop types. The MIrAD was found to map comparable irrigated cropland patterns to a Landsat-derived LULC map (82% pixel-level thematic agreement) over Nebraska, with most areas of disagreement occurring in transitional mixed pixel locations between adjacent LULC types. Classification accuracy was found to have little variation by general field size and crop type, but accuracies were lower for pixel locations at the field’s edges. Overall, this study found the MIrAD to be a relatively accurate and consistent irrigated cropland classification within the US Central Great Plains region that can be used for local- and regional-scale applications.

Soil characterization based on land cover heterogeneity over a tropical landscape: an integrated approach using earth observation data-sets

Soil is a vital part of the natural environment and is always responding to changes in environmental factors, along with the influences of anthropogenic factors and land use changes. The long-term change in soil properties will result in change in soil health and fertility, and hence the soil productivity. Hence, the main aim of this paper focuses on the analysis of land use/land cover (LULC) change pattern in spatial and temporal perspective and to present its impact on soil properties in the Merawu catchment over the period of 18 years. Post classification change detection was performed to quantify the decadal changes in historical LULC over the periods of 1991, 2001 and 2009. The pixel to pixel comparison method was used to detect the LULC of the area. The key LULC types were selected for investigation of soil properties. Soil samples were analysed in situ to measure the physicochemical soil properties. The results of this study show remarkable changes in LULC in the period of 18 years. The effect of land cover change on soil properties, soil compaction and soil strength was found to be significant at a level of <0.05.