Land-use land-cover classification analysis of Giba catchment using hyper temporal MODIS NDVI satellite images

Landsat-based land-use land-cover (LULC) mapping studies were previously conducted in Giba catchment, comprising an area of 4019 km². No attempt has been done to map LULC of this catchment through the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series data. This article is aimed to see whether time-series MODIS NDVI data set is applicable for LULC mapping of Giba catchment or not. MODIS NDVI data sets of the year 2010 were used for classification analysis. The original data were subjected to MODIS Reproduction Tool and stacking. The re-projected and stacked images were filtered using Harmonic Analysis of Time-Series filtering algorism to remove the effects of cloud and other noises. The MODIS NDVI data sets (16-day maximum value composite) were classified using the ISODATA clustering algorithm available under ERDAS IMAGINE software. A series of unsupervised classification runs were carried out with a pre-defined number of classes (5–24). From this classification, the optimal numbers of classes were determined to be eight after checking for average divergence analysis. The classification result became eight LULC classes namely: bare land, grass land, irrigated land, cultivated land, area closure, shrub land, bush land, and forest land with an overall accuracy of 87.7%. It was therefore concluded that MODIS NDVI time-series image is applicable for mapping large watersheds.     

Assessing the spatio-temporal rates and patterns of land-use and land-cover changes in the Cerrados of southeastern Mato Grosso,Brazil

The Cerrados of central Brazil have undergone profound landscape transformation in recent decades due to agricultural expansion, and this remains poorly assessed. The present research investigates the spatial-temporal rates and patterns of land-use and land-cover (LULC) changes in one of the main areas of agricultural production in Mato Grosso State (Brazil), the region of Primavera do Leste. To quantify the different aspects of LULC changes (e.g. rates, types, and spatial patterns) in this region, we applied a post-classification change detection method, complemented with landscape metrics, for three dates (1985, 1995, and 2005). LULC maps were obtained from an object-based classification approach, using the nearest neighbour (NN) classifier and a multi-source data set for image object classification (e.g. seasonal Thematic Mapper (TM) bands, digital elevation model (DEM), and a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived index), strategically chosen to increase class separability. The results provided an improved mapping of the Cerrados natural vegetation conversion into crops and pasture once auxiliary data were incorporated into the classification data set. Moreover, image segmentation was crucial for LULC map quality, in particular because of crop size and shape. The changes detected point towards increasing loss and fragmentation of natural vegetation and high rates of crop expansion. Between 1985 and 2005, approximately 42% (6491 km²) of Cerrados in the study area were converted to agricultural land uses. In addition, it was verified that cultivated areas are encroaching into fragile environments such as wetlands, which indicates the intense pressure of agricultural expansion on the environment.     

A decision-oriented model to evaluate the effect of land use and agricultural management on herbicide contamination in stream water

Modelling stream water pollution by herbicides in agricultural areas is a critical issue since numerous and incompletely known processes are involved. A decision-oriented model, SACADEAU-Transf, which represents water and pesticide transfer in medium-sized catchments (10–50 km²) is presented. This model aims at evaluating the effect of land use, agricultural practice and landscape on the contamination of stream water in rural catchments. The processes are represented in an easily understandable way with a moderate amount of information, producing semi-quantitative and spatialized outputs. Modelling focuses on the first few months after herbicide application when high levels of contamination are generally observed, by considering transfer through the catchment area via surface and subsurface flow. The surface flow, based on a tree plot network representation of the catchment, is controlled by soil-surface properties and saturated conditions. The subsurface flow based on Topmodel concepts is controlled by the topography. Herbicide transfer is coupled to water transfer by taking into account the main characteristics of the chemicals. The model simulates the daily water and herbicide outflow at the outlets of the farmers' fields as well as from the catchment. Preliminary results on maize herbicide transfer are presented for an agricultural catchment with an area of 17 km² located in north-western France. The relevance of SACADEAU-Transf model is discussed in view of the qualities required for the decision-oriented models developed for improving agro-environmental management.     

A national-scale GIS-based system for modelling impacts of land use on water quality

Management of freshwater quality requires modelling tools for rapid evaluation of land use and management scenarios. This paper presents the CLUES (Catchment Land Use and Environmental Sustainability) model to address this need. CLUES provides steady state, spatially distributed, integrated catchment models tightly coupled to GIS software to predict mean annual loads of total nitrogen, total phosphorus, sediments and E. coli, and concentration of nutrients throughout New Zealand (268,000 km²) with a subcatchment resolution of 0.5 km². CLUES also estimates potential nutrient concentrations for estuaries and provides key farm socio-economic indicators. The model includes a user interface for study area selection, scenario creation, data geo-visualisation, and export of results. It is pre-populated with spatial data and parameter values for New Zealand. Evaluation of the model and a summary of applications demonstrate the tractability and utility of national-scale rapid scenario assessment tools within a GIS framework.     

Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil

Since 2000, the southwestern Brazilian Amazon has undergone a rapid transformation from natural vegetation and pastures to row-crop agricultural with the potential to affect regional biogeochemistry. The goals of this research are to assess wavelet algorithms applied to MODIS time series to determine expansion of row-crops and intensification of the number of crops grown. MODIS provides data from February 2000 to present, a period of agricultural expansion and intensification in the southwestern Brazilian Amazon. We have selected a study area near Comodoro, Mato Grosso because of the rapid growth of row-crop agriculture and availability of ground truth data of agricultural land-use history. We used a 90% power wavelet transform to create a wavelet-smoothed time series for five years of MODIS EVI data. From this wavelet-smoothed time series we determine characteristic phenology of single and double crops. We estimate that over 3200 km² were converted from native vegetation and pasture to row-crop agriculture from 2000 to 2005 in our study area encompassing 40,000 km². We observe an increase of 2000 km² of agricultural intensification, where areas of single crops were converted to double crops during the study period.     

Dynamics of land-use and land-cover change in Freetown,Sierra Leone and its effects on urban and peri-urban agriculture – a remote sensing approach

This paper presents findings of a land-use and land-cover (LULC) change mapping exercise conducted in Freetown, Sierra Leone. Nine LULC classes were mapped from multi-temporal Landsat data of 1974, 1986 and 2000. Special attention was given to the growth or otherwise of agricultural land in relation to other LULC classes. Conversion of one land-use/-cover type to the other was identified, and its effects discussed. Major conversions occurred between agricultural lands, grasslands, evergreen forest, built-up areas and barren land. Built-up areas increased by at least 140% between 1974 and 2000, suggesting a high urbanization rate. About 882 ha (27%) of agricultural lands in 1986 were converted to residential purposes in 2000, especially at the urban fringes, in response to an increase in population. Some 14% of evergreen forest was found to have been converted to agricultural land. These major conversions suggest a strong linkage between urbanization, agriculture and deforestation.     

Discriminating native and plantation forests in a Landsat time-series for land use policy design

The Warren River Catchment of south-western Australia is an area of high biodiversity threatened by the loss of native vegetation and dryland salinity. Over the last 20 years, it has been the target of a series of policies that encourage conversion of agricultural land to plantation forest. Remote sensing has a key role in measuring trends in the area of plantation forest observed across the landscape and hence the effectiveness of policy initiatives. Despite its importance to land use policy, accurate data on historical land use and land cover (LULC) dynamics of two spectrally similar but ecologically distinct forest types – such as native forest and plantation forest – are not readily available for south-western Australia, largely due to prohibitive data delivery costs. However, we argue that regular low-cost monitoring of long-term change in the spatial distribution of plantation forest through remote sensing is a critical input into environmental policy for the catchment. To this end, a 35-year time-series of Landsat imagery was acquired, and three different classifiers were tested (Support Vector Machines – SVM; Random Forests – RF; and Classification and Regression Trees – CART) on spectral and textural indices applied to four spectral bands. The six major LULC classes considered were agriculture, water, native forest, sand dunes, plantation forest and harvested native forest. In classifying the imagery the SVM and RF outperformed the CART across all classes. However, the SVM classifier gave a slightly higher F-score for most individual classes than the RF. Eucalypt dominated plantation forest reaching full canopy cover was subject to the highest rates of misclassification inasmuch as it shares spectral properties with the Eucalypt dominant native forest. When applied to Landsat time-series imagery, SVM classifier combined with four bands held in common between the four Landsat sensors, and derived textures metrics are valuable in classifying plantation and native forest, particularly where these have a similar species composition. The differences in prediction accuracy when including additional Landsat bands were not statistically significant, as demonstrated by the McNemar test. Thus, we achieved a trade-off in reducing processing time without significantly impacting on classification accuracy (≥86%). The relatively high accuracy of the proposed method enables the effects of past policy initiatives to be observed, and hence the efficient design of environmental and conservation policy in the future.     

Classification accuracy and trend assessments of land cover- land use changes from principal components of land satellite images

The paper evaluated the accuracy of classifying Land Cover-Land Use (LCLU) types and assessed the trends of their changes from Principal Components (PC) of Land satellite (Landsat) images. The accuracy of the image classification of LCLU was evaluated using the confusion matrices and assessed with cross-referencing of samples of LCLU types interpreted and classified from System Pour l’Observation de la Terre (SPOT) images and topographical map. LCLU changes were detected, quantified, and statistically analysed. The interpretation error of the composite image of Landsat Enhanced Thematic Mapper Plus (Landsat ETM⁺) (2006) was high compared with that from the PC image of Landsat ETM⁺ (2006). From 1986 – 2006 the area covered by settlements increased by 0.8% (230,380.00 km²), agricultural land decreased by 7.5% (1009.40 km²), vegetation cover decreased by 0.9% (114.00 km²) while waterbody increased by 0.2% (25.91 km²). Also, from 1986 – 2006 the average annual rates of change in the area of settlements was 6.7%. Agricultural land and bare land showed fluctuations of change rates from 6.7% and 5.0% annually in 1986 and 2006 respectively. The quantitative evidences of LCLU changes revealed the growth of settlements. The conversions of land from agriculture to urban land represent the most significant land cover changes. The rate of change was as high as 4.8% for settlements while agricultural lands were converted at 5.0% per year. The Principal Component Analysis (PCA) of the Landsat images and supervised classification method used made it possible to classify and determine the area of LCLU classes from the set of Landsat images without prior depiction and delimitation of individual LCLU type. It permitted the measurement of area of each LCLU class at a high accuracy level and kept the level of error relatively constant. The PCA analysis in this study affirms the previous research findings. Future research works should focus on the use of remotely sensed images with high temporal and spatial resolutions such as Quick Bird and SPOT 6 to develop effective and accurate LCLU change mapping and monitoring at the local scale.

The PCA technique has been used quite widely to study changes in land cover and land use in many ‘developed’ countries but much still needs to be done in developing and undeveloped countries where land cover and land use change is poorly mapped and knowledge of such changes is very important for planning development of the country.     

Digital terrain analysis using Landsat‐7 ETM+ imagery and SRTM DEM: a case study of Nevsehir province (Cappadocia), Turkey

A three‐dimensional (3D) model of land‐use/land‐cover (LULC) and a digital terrain model of Nevsehir province (Cappadocia), Turkey, were generated and analysed using a Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) multispectral image set and a Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM). Stream drainage patterns, lineaments and structural‐geological features (landforms) were extracted and analysed. In the process of analysing and interpreting the multispectral images of geological features, criteria such as colour and colour tones, topography and stream drainage patterns were used to acquire information about the geological structures of the land, including as geomorphological, topographic and tectonic structures. Landsat‐7 ETM+ multispectral imagery and an SRTM DEM of the study region were used experimentally for classification and analysis of a digital terrain model. Using the multispectral image data, the LULC types were classified as: settlement (1.2%); agricultural land (70.1%); forest (scrubland, orchard and grassland) (2.9%); bare ground (25.5%); and water bodies (lakes and rivers) (0.3%) of the study area (5434 km²). The results of the DEM classification in the study area were: river flood plain (11.3%); plateau (52.3%); high plateau (28.4%); mountain (7.6%); and high mountain (0.3%). Lineament analysis revealed that the central Kizilirmak River divides the region into two nearly equal parts: the Kirsehir Plateau in the north and the Nevsehir Plateau in the south. In terms of the danger of catastrophe, the settlements of Kozakli, Hacibektas and Acigol were found to be at less risk of earthquake and/or flooding than those of Avanos, Gulsehir, Urgup, Nevsehir, Gumuskent and Derinkuyu, which are located on river flood plains and/or the main stream drainage channels, particularly stream beds, where the lineaments are deep valleys or fracture or fault‐line indicators.     

Mapping agriculture's impact by combining farm management handbooks,life-cycle assessment and search engine science

Despite great advancements in recent years, the availability of detailed and regionalised farm practice data at national scale remains an obstacle for spatially-detailed research on sustainable intensification. Parsing and information retrieval techniques were applied to 385 farm management handbooks to estimate farm practices (use of fertilisers, pesticides, water, fuel) of 72 commodities grown in 42 regions. Life-cycle inventories were used to derive GHG emissions and energy use from farm practice data. Practices and impacts were mapped at 1.1 km² resolution using agricultural census data and a remote-sensing-based land use map. Existing data was linearly extrapolated using a rule-based approach to fill spatial gaps. Estimates were, in aggregate, comparable to the best available data at national and local scales. Our method contributes to the push to create more spatially-detailed assessments of agricultural impacts at a national scale by focusing on the production of basic data at the farm level.     

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.     

The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: the case of Mekelle City of northern Ethiopia

Information on the rate and pattern of urban expansion is required by urban planners to devise proper urban planning and management policy directions. This study evaluated the dynamics and spatial pattern of Mekelle City’s expansion in the past three decades (1984–2014). Multi-temporal Landsat images and Maximum Likelihood Classifier were used to produce decadal land use/land cover (LULC) maps. Changes in LULC and spatial pattern of urban expansion were analysed by post-classification change detection and spatial metrics, respectively. The results showed that in the periods 1984–1994, 1994–2004, and 2004–2014, the built-up area increased annually by 10%, 9%, and 8%, respectively; with an average annual increment of 19% (100 ha year^?1), from 531 ha in 1984 to 3524 ha in 2014. Between 1984 and 2014, about 88% of the gain in built-up area was from conversion of agricultural lands, which decreased by 39%. Extension of existing urban areas was the dominant growth type, which accounted for 54%, 75%, and 81% of the total new development during 1984–1994, 1994–2004, and 2004–2014, respectively. The spatial metrics analyses revealed urban sprawl, with increased heterogeneity and gradual dispersion in the outskirts of the city. The per capita land consumption rate (ha per person) increased from 0.009 in 1984 to 0.014 in 2014, indicating low density urban growth. Based on the prediction result, the current (2014) built-up area will double by 2035, and this is likely to have multiple socioeconomic and environmental consequences unless sustainable urban planning and development policies are devised.     

基于随机森林的遥感土地利用分类及景观格局分析

2009年福建平潭综合实验区设立,作为闽台合作及国家对外开放的窗口,其土地利用变化主要受社会经济因素的影响和自然地理环境的制约,也与未来的土地利用规划密切相关.本文利用1990、2000、2010和2017年4期Landsat遥感影像数据,定量分析近27年的土地利用变化对景观格局的影响.结果表明:(1)在选择合适训练样本的情况下,利用随机森林方法可获得较高的遥感土地利用分类精度(4期遥感影像分类的总体精度均在87%以上,Kappa系数均在0.84以上);(2)1990~2017年,水域面积急剧减少31.04 km2,流失的水域主要转化为建设用地和林地;建设用地增加40.98 km2,年平均增长1.52 km2.近十年呈快速增长趋势,年平均增长3.87 km2;(3)在斑块类型级别上,逐年增加的建设用地导致最大斑块占景观面积比例(LPI)、聚合度(AI)和边缘密度(ED)呈上升趋势,其中LPI受到建设用地增加的影响最显著.在景观类型级别上,多样性(SHDI)和景观形状(LSI)呈下降趋势.     

Geospatial variability in the egg raft distribution and abundance of Culex pipiens and Culex restuans in Urbana‐Champaign,Illinois

The aim of this study was to determine whether areas of high Culex pipiens and Culex restuans abundance in an urban environment, based on fixed oviposition surveillance sites, corresponded to remotely sensed data. A land use land cover (LULC) classification, based on Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) data acquired in July 2003 and Landsat‐5 TM data from July 1991, was compared to the abundance of Culex egg rafts in Urbana‐Champaign, East‐Central Illinois. We performed a maximum likelihood, unsupervised classification and generated three land cover classifications: urban, non‐urban and water. Ground coordinates of the Culex surveillance sites were overlaid onto LULC maps with 10 m² grid cells. The grid was stratified based on levels of drainage: 0 = poorly drained and 1 = well‐drained. Total LULC change from 1991 to 2003 in the Urbana‐Champaign study site was relatively low, at 12.1%. The most frequent LULC category was maintained urban. The egg raft rate was significantly higher in urban LULC habitats. Remote stratification of the urban land cover using QuickBird visible and near‐infrared (NIR) data revealed that high‐density canopy coverage was most frequently associated with high Culex abundance in oviposition traps. We demonstrate that optical remote sensing can identify land use parameters associated with high Culex oviposition.     

Monitoring land-use change-associated land development using multitemporal Landsat data and geoinformatics in Kom Ombo area,South Egypt

Due to the progressive increase in population, sustainable development of desert land in Egypt has become a strategic priority in order to meet the increasing demands of a growing population for food and housing. Such obligations require efficient compilation of accurate land-cover information in addition to detailed analysis of archival land-use changes over an extended time span. In this study, we applied a methodology for mapping land cover and monitoring change in patterns related to agricultural development and urban expansion in the desert of the Kom Ombo area. We utilized the available records of multitemporal Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images to produce three land-use/land-cover maps for 1988, 1999 and 2008.

Post-classification change detection analysis shows that agricultural development increased by 39.2% through the study period with an average annual rate of land development of 8.7 km² year^?1. We report a total increase in urbanization over the selected time span of approximately 28.0 km² with most of this urban growth concentrated to the east of the Nile and occurring through encroachment on the former old cultivated lands. The archival record of the length of irrigation canals showed that their estimated length was 341.5, 461.8 and 580.1 km in the years 1988, 1999 and 2008, respectively, with a 70% increase in canal length from 1988 to 2008. Our results not only accurately quantified the land-cover changes but also delineated their spatial patterns, showing the efficiency of Landsat data in evaluating landscape dynamics over a particular time span. Such information is critical in making effective policies for efficient and sustainable natural resource management.     

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.     

Systematic and random transitions of land-cover types in Burkina Faso,West Africa

In-depth statistical analysis of forest transition between land-cover types over time can reveal the dominant signals of landscape transformation, which are needed in order to develop appropriate land management strategies. We applied a recently developed methodology to analyse the transition matrix of six land-cover classes, derived from 1986 and 2002 Landsat images of an area of 15?675 km² in southern Burkina Faso. Results show that most landscape transformations followed a systematic process. In addition, some transitions occurred as an apparently random process, probably caused by uncommon or sporadic events. Degradation of woodland to shrub-/grassland over 15.7% of the landscape, increases in biomass from woodland to dense forest on 10% of the landscape and conversion of 6% of the landscape from shrub-/grassland to cropland were the dominant signals of forest-cover transitions. From a planning perspective, the dominance of systematic processes should facilitate regional land-use planning and sustainable forest management in a context of immigration and agricultural intensification.     

Characterizing changes in grassland desertification based on Landsat images of the Ongniud and Naiman Banners,Inner Mongolia

Desertification is treated as an important and critical environmental hazard. In the face of increasingly serious grassland desertification, China has made great efforts to combat desertification and a series of key national ecological projects has been launched. However, accurate, timely, and effective monitoring and assessment are required to determine whether these projects work well. The Horqin sandy land represents the largest area of sandy land in China. In this article, the Naiman and Ongniud Banners were studied because these contain the main desertified grassland in Horqin. Next, a desertification classification and grading system was designed for the Horqin sandy land after conducting fieldwork. Based on spectral mixture analysis (SMA) and decision-tree methods, we interpreted Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images of the study area from four years: 1985, 1992, 2001, and 2013. Overall, the following results were obtained. The total area of desertified grassland in the Naiman and Ongniud Banners increased from 5979 km² in 1985 to 9195 km² in 1992 (an increase of 53.79%) and then decreased to 7828 km² in 2001 and to 6023 km² in 2013. The changes in the areas of desertified grassland with various degrees of desertification displayed the same trends as that of the total desertified grassland area. The severely desertified grassland expanded from 1872 km² in 1985 to 3723 km² in 1992 before shrinking to 2189 km² in 2013. The annual rates of expansion of desertified grassland during the periods 1985–1992, 1992–2001, and 2001–2013 were 7.68%, ?1.65%, and ?1.92%, respectively, and the corresponding expansion rates of severely desertified grassland were 14.12%, ?3.48%, and ?1.19%, respectively. Both the desertified grasslands and the areas with various degrees of desertification displayed significant expansion during the period 1985–1992. Since 1992, this trend has reduced. During the study period, the changes in temperature and precipitation did not benefit the reversal of grassland sandy desertification. Furthermore, the growing population and expansion of livestock production and farming inhibited such reversal. However, the results presented in this article suggest that a reversal in grassland sandy desertification has been occurring since 1992. The results indicate that ecological engineering measures have helped reverse desertification and have promoted the restoration of grassland vegetation.     

Assessment of remotely sensed and statistical inventories of African agricultural fields

This paper critically examines different sources of remotely sensed and statistical inventories of African agricultural fields. Substantial discrepancies are found across alternative sources of information in both the extent and location of agricultural fields. In one‐third of the countries, the difference between lowest and highest field extent estimate exceeds 25% of the total country area, and the maximum difference at the continental level is 2.6 million km². Much of the disagreement between land‐cover maps arises from areas of low cropping density. These inconsistencies have important implications when using these data directly, e.g. for the assessment of land cover changes, or indirectly in economic or physical models, and indicate a need to explicitly quantify uncertainties arising from the limitations in land‐cover data. They also highlight the need for development of regional land information systems for baseline development and informed policy decisions.     

Land-use and land-cover classification in semi-arid regions using independent component analysis (ICA) and expert classification

This study was focused on addressing the need for accurate land-use/land-cover classification (LULC) maps in Iran and in other similarly developing countries. To generate and validate a new LULC map for northeastern Iran’s 2037.5 km² Hable-roud watershed, a step-by-step process was developed and implemented, consisting of image preprocessing, extraction of training and reference sampling locations, decomposition of multi-spectral thematic mapper bands into features by independent component analysis methods, classification using these features and slope maps, enhancement of land-use classes through image segmentation and zonal statistics, then through consideration of normalized difference vegetation index and climatic zones, followed by ground truthing. This newly developed approach provided maps that distinguished dryland farming, irrigated farmland, forest plantations, and low-, medium-, and high-vegetation density rangelands, while currently available maps for the watershed lef t 39% of lands unclassified or in combined classes. The new maps’ ground-truthing-based overall accuracy and kappa coefficient were 88.3% and 0.83, respectively. In order to develop such an improved LULC map, it was necessary to go beyond the mere analysis of reflectance information, to incorporating climatic and topographic data through this newly proposed step-by-step approach.