Various Types Of Land Cover Research Articles

Scale-dependent effects of landscape composition and configuration on natural enemy diversity, crop herbivory, and yields.

(1) Land-use intensification in agricultural landscapes has led to changes in the way habitats and resources are distributed in space. Pests and their natural enemies are influenced by these changes, and by the farming intensity of crop fields. However, it is unknown whether the composition of landscapes (amount and diversity of land cover types) or their configuration (spatial arrangement of cover types) are more important for natural enemy diversity, and how they impact crop damage and yields. In addition, effects of interactions between local farming practices (organic vs. conventional) and landscape variables are unclear. (2) Here, we make use of a data set where landscape composition and configuration were uncorrelated across multiple spatial scales. Natural enemies, crop damage, and yields were sampled in 35 organic and conventional crop fields. Out of seven broad natural enemy taxa, five were positively affected by a complex landscape configuration. In contrast, only carabids were positively affected by the amount of seminatural habitat around fields. Increasing diversity of land cover types had positive effects on some, but negative effects on other taxa. Effect sizes varied among taxa but increased with increasing spatial scale, defined by circular areas of increasing radius around fields. (3) The diversity of aerial, but not of ground-dwelling enemies was higher in fields under organic than conventional management. Interactions of local and landscape variables were important for birds, but not other enemies. Bird richness was higher in organic fields in simple landscapes, but not in landscapes with complex configuration or high land cover diversity. (4) Crop damage decreased with landscape diversity, but increased in conventional fields with complex configuration. Yields increased with both parameters in conventional fields only, and were higher on average in organic compared to conventional fields. Enemy diversity was positively related to crop damage, indicating positive density-dependence of enemies on pests. However, the diversity of aerial enemies was also positively related to yields. (5) Our results suggest that the effectiveness of agrienvironmental schemes for managing natural enemy diversity, crop damage and yields could be enhanced by optimizing the effects of distinct landscape parameters, particularly landscape configuration and diversity, across scales.

Accuracy assessment of LiDAR-derived digital elevation models in a rural landscape with complex terrain

Digital elevation models (DEMs) are essential in many professional areas as they produce georeferenced elevation data that are critical for a wide range of studies, computations, decision-making processes, and derived products. Quality control thus becomes necessary to quantify the accuracy of the information provided. We assessed the accuracy of elevation data estimated by DEMs derived from LiDAR data representing diverse land cover types. For this purpose, we used the FUSION software and explored variations in accuracy in relation to the following factors: input data, interpolation methods, terrain slope, heterogeneity of land cover, and LiDAR point density. We selected and measured 1157 checkpoints by using total station and GPS techniques and following a stratified random design in order to validate the LiDAR-derived DEMs. We used robust estimators, nonparametric tests, and analysis of variance to examine the elevation errors. The study findings showed the following: (1) using the full set of LiDAR returns did not improve elevation accuracy relative to using the last-return data set; (2) using the minimum switch for interpolation did not improve accuracy compared to the default behavior of the interpolator; (3) land cover and slope significantly affected accuracy; (4) DEMs tended to underestimate elevation; and (5) the mean density of the returns classified as ground was significantly affected by land cover and slope factors.

A Knowledge‐Based Approach for Mapping Land Degradation in the Arid Rangelands of North Africa

AbstractRangelands cover about 82% of the arid area of Morocco. It is generally acknowledged that these areas are threatened by desertification. Monitoring desertification requires accurate knowledge about the current status of rangeland degradation. Remote sensing is widely used to assess changes in land cover, but its use in arid rangelands has limitations because of spectral confusion among various types of land cover. The objective of this study was to assess the severity and spatial extent of rangeland degradation in the high plateaus of eastern Morocco, using a knowledge‐based approach combining remote sensing and ancillary data. This approach relies on analyzing datasets derived from Landsat TM satellite imagery, lithology, bioclimatic data and field measurements. The level of rangeland degradation was assessed using indicators such as vegetation parameters, grazing levels and cultivation intensity, which provided a high level of accuracy for mapping and monitoring the degradation of the arid rangelands. The results showed that the total area of degraded rangeland in the high plateaus of eastern Morocco is about 17,417 km2, accounting for 48% in the studied area. Copyright © 2015 John Wiley & Sons, Ltd.

Landscape simplification filters species traits and drives biotic homogenization.

Biodiversity loss can affect the viability of ecosystems by decreasing the ability of communities to respond to environmental change and disturbances. Agricultural intensification is a major driver of biodiversity loss and has multiple components operating at different spatial scales: from in-field management intensity to landscape-scale simplification. Here we show that landscape-level effects dominate functional community composition and can even buffer the effects of in-field management intensification on functional homogenization, and that animal communities in real-world managed landscapes show a unified response (across orders and guilds) to both landscape-scale simplification and in-field intensification. Adults and larvae with specialized feeding habits, species with shorter activity periods and relatively small body sizes are selected against in simplified landscapes with intense in-field management. Our results demonstrate that the diversity of land cover types at the landscape scale is critical for maintaining communities, which are functionally diverse, even in landscapes where in-field management intensity is high.

Validation of the ESA CCI soil moisture product in China

The quality of a newly merged soil moisture product (ECV_SM v0.1) from active and passive microwave sensors has attracted widespread international attention. The performance evaluation of this product will benefit studies on climate, meteorology, agriculture, hydrology, ecology and the environment. In this study, meteorological station data and the Noah soil moisture product were used to validate the ECV_SM product in China. First, some conventional statistical measures, such as correlation coefficients, bias, root mean square difference (RMSD) and mean relative error (MRE), were computed to describe the level of agreement between the meteorological station data and ECV_SM values. The accuracy was moderately high (the correlation was significant at the 0.05 level), although the two datasets differed slightly for various types of land cover. Compared with cropland and urban and built-up areas, the performance of ECV_SM was best in grassland regions. Second, the triple collocation technique was used to assess the random error in the meteorological station data, Noah soil moisture product and ECV_SM product. The mean errors in these three datasets were 0.108, 0.079 and 0.075m3m−3, respectively, on July 8, 2010 and 0.099, 0.061 and 0.059m3m−3, respectively, on October 8, 2010. Only two days of data were used for the triple collocation test as a representative, but this cannot precisely indicate that the test results on any other day correspond with the test results on these two days. Additionally, a trend analysis of ECV_SM during 2003–2010 was carried out using the Mann–Kendall trend test.

Assessing Land Cover Change and Anthropogenic Disturbance in Wetlands Using Vegetation Fractions Derived from Landsat 5 TM Imagery (1984–2010)

Anthropogenic disturbance of wetlands in Canada is extensive. In wetland monitoring programs disturbance assessment often relies on single date field and geo-spatial data, thereby rendering detection of the nature, timing and magnitude of disturbance events and trends difficult. The Landsat temporal archive provides potential for more comprehensive temporal change analysis. However, its 30 m pixel size may result in omission of small disturbances or lack of spatial precision near boundaries where change often occurs. Spectral mixture analysis (SMA) is a subpixel technique that has been used to assess change in a variety of land cover types, but rarely for non-coastal wetlands. This research utilized SMA vegetation, bare, and moisture fractions derived from 26 Landsat 5 Thematic Mapper (TM) scenes from 1984 to 2010 in two-date comparisons and time series analysis to assess disturbance in two wetland complexes in eastern Ontario. The 1984–2010 two-date analysis showed overall declines in vegetation and moisture fractions and increases in the bare fraction, while time series analysis over the 26 year period showed more variable inter-annual changes including years of sudden change, cyclic trends and more gradual trends. Anthropogenic disturbances that were identified included a lake created for recreational purposes, logged/cultivated areas, and wetland degradation due to encroaching development.

Sexual size dimorphism mediates effects of spatial resource variability on American black bear space use

AbstractThe distribution of animals is influenced by numerous factors including spatial distribution and temporal availability of resources. We tested the spatial resource variability hypothesis (increasing landscape heterogeneity results in increasing amount of space use) and the temporal resource variability hypothesis (temporal variation in resources reduces amount of space use) using location data from radiomarked American black bears Ursus americanus in Missouri and Arkansas, USA. We used 95% utilization distributions (UDs) to define individual seasonal space use and constructed 22 models using covariates that described composition, spatial arrangement and diversity of land cover types (an index of heterogeneity or patchiness); seasonal hard mast production; and seasonal use of land cover to test our hypotheses using linear modeling and small‐sample Akaike information criterion (AICc) model selection approaches. The AICc best performing model supported the spatial resource variability hypothesis and included Shannon diversity index [95% confidence limit (CL) of coefficient = 1.56–2.42] and sex (male; 95% CL of coefficient = 0.05–0.49) as covariates that explained variation in transformed values of UD size. Predicted and observed values during model evaluation were highly correlated (r = 0.90). As land cover heterogeneity increased, UD size increased, likely a consequence of bears responding to greater patchiness to maintain sufficient resources. Further, the Shannon diversity index was greater for males than females , suggesting larger bodied males used larger areas to meet their higher energetic costs due to landscape fragmentation. Studies of resource hypotheses in solitary species should consider intraspecific allometric relationships such as sexual size dimorphism as has been addressed using group size in social species.

Assessing soil quality under different land cover types within shifting agriculture in South Cameroon

The common agricultural practice in the humid tropical forest zone of Cameroon is shifting cultivation, which leads to a landscape mosaic system characterized by a diversity of land cover types. Our objective was to evaluate soil properties and soil quality under these land cover types and to investigate on their interrelationships. Topsoil samples (0–20cm) were collected at the same time from 8 different land cover types (bare soil with burned vegetation biomass (FR1), bare soil with unburned vegetation biomass (FR), Chromolaena odorata fallow (JC), bush ligneous fallow (JR), secondary forest (SF), primary forest (FC), Gilbertiodendron dewevrei forest (FG), and raffia and Uapacca forest (RA)) and analysed for routine laboratory determinations. A soil quality index (SQI) computed based on soil chemical properties, analysis of variance and multiple comparison tests were used to compare soils from different land cover types. Principal component analysis (PCA) was used to select the most appropriate indicators that control soil quality. Several soil properties showed high to very high coefficient of variation within the land cover types. Organic matter was significantly high under forested land cover types (FC, FG, RA) and under bare soil with burned vegetation (FR1). Soil quality differs significantly (p=0.000) from one land cover to another and shows the following ranking: SQI_FR1≫SQI_FG>SQI_RA>SQI_FR>SQI_FC>SQI_JC>SQI_FS>SQI_JR. Organic matter (OM), available P, calcium (Ca), and pHw combined, accounted for 88.5% of the variation of soil quality. The soil quality appeared to be highly influenced by ashes from burned vegetation biomass which temporally enriches soils with nutrient elements and by the organic matter supplied by forested land cover types.

Intra-annual variability of satellite observed surface albedo associated with typical land cover types in China

Surface albedo is a primary causative variable associated with the process of surface energy exchange. Numerous studies have examined diurnal variation of surface albedo at a regional scale; however, few studies have analyzed the intra-annual variations of surface albedo in concurrence with different land cover types. In this study, we amalgamated surface albedo product data (MCD43) from 2001 to 2008, land-use data (in 2000 and 2008) and land cover data (in 2000); quantitative analyses of surface albedo variation pertaining to diverse land cover types and the effect of the presence/absence of ground snow were undertaken. Results indicate that intra-annual surface albedo values exhibit flat Gaussian or triangular distributions depending upon land cover types. During snow-free periods, satellite observed surface albedo associated with the non-growing season was lower than that associated with the growing season. Satellite observed surface albedo during the presence of ground snow period was 2–4 times higher than that observed during snow-free periods. Surface albedo reference values in typical land cover types have been calculated; notably, grassland, cropland and built-up land were associated with higher surface albedo reference values than barren while ground snow was present. Irrespective of land cover types, the lowest surface albedo reference values were associated with forested areas. Proposed reference values may prove extremely useful in diverse research areas, including ecological modeling, land surface process modeling and radiation energy balance applications.

InSAR Coherence Estimation for Small Data Sets and Its Impact on Temporal Decorrelation Extraction

A novel coherence estimation method for small data sets is presented for interferometric synthetic aperture radar (SAR) (InSAR) data processing and geoscience applications. The method selects homogeneous pixels in both the spatial and temporal spaces by means of local and nonlocal adaptive techniques. Reliable coherence estimation is carried out by using such pixels and by correcting the bias in the estimated coherence caused by the non-Gaussianity in high-resolution SAR scenes. As an example, the proposed method together with coherence decomposition is applied to extract the temporal decorrelation component over an area in Macao. The results show that the proposed algorithms work well over various types of land cover. Moreover, the coherence change with time can be more accurately detected compared to other conventional methods.

Extraction of impervious features from spectral indices using artificial neural network

An urban area comprises a complex mix of diverse land cover types and materials. Urban ecology and environment is significantly influenced by the proportion of impervious cover that is increasing considerably with time due to the continuous influx of people into urban areas. Therefore, it is of vital importance to determine the spatiotemporal pattern and magnitude of urbanization. In the present study, we have employed a supervised backpropagation neural network in order to extract the impervious features using five spectral indices, such as one vegetation index—Soil-Adjusted Vegetation Index (SAVI), one water index—Modified Normalized Water Index (MNDWI), and three urban indices—Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Index-Based Built-up Index (IBI). The study has been performed using Landsat Thematic Mapper data of November, 2011, of the rapidly urbanizing city of Ranchi, capital of Jharkhand state, India. Using different combinations of these spectral indices while keeping SAVI and MNDWI constant, seven composite images were built, and from each of these composites, impervious features were classified and its accuracy assessed with reference to high-resolution images provided by Microsoft Bing Imagery and adequate ground truthing. It was observed that along with SAVI and MNDWI, whenever IBI was used in any combination, it decreased the classification efficiency. On the other hand, NDBI and BUI, individually or when used together, discriminated the impervious features from the others with high accuracy with the combination of SAVI, MNDWI, and BUI achieving the highest accuracy of 90.14 %.

Understanding land-cover change dynamics of a mangrove ecosystem at the village level in Krabi Province, Thailand, using Landsat data

This paper presents an objective approach for quantifying the amount of mangrove loss caused by expansion of shrimp farms in three villages of Krabi Province, Thailand. Landsat images from three time periods of shrimp farm development (pre-shrimp farms – 1989, development – 2001, and post-development – 2007) were analyzed using unsupervised classification algorithm. A post-classification change detection comparison approach revealed only moderate mangrove exploitation and shrimp farms, which displaced a variety of land-cover types in addition to mangroves. In some cases, there was a detectable cycle of use and replenishment of mangroves and a lack of the boom-bust cycle of shrimp farming that other parts of Thailand experienced.

Development of an automated method for mapping fire history captured in Landsat TM and ETM + time series across Queensland, Australia

Remote sensing can quantify past and present fire activity at spatial scales useful for a range of fire and vegetation management applications. In this study, we present a new automated approach to classifying burnt areas across the state of Queensland, Australia. The method is applied to complete time series of Landsat TM/ETM+ imagery rather than single images and considers spectral (band 4, B4, and bands 4+5, B45), thermal, temporal and contextual information within a hierarchical framework. To maximise the available observations and the burnt area detected, we used imagery containing up to 60% cloud that was screened during pre-processing. Median filters were applied to smooth the time series and multi-date change detection used to locate negative outliers (large declines in reflectance relative to the median-smoothed time series). Watershed region growing was used to segment and map a larger spatial extent of the change while minimising commission errors. These segmented change objects were attributed as either burnt or unburnt using their thermal, reflective and contextual characteristics in a classification tree. Thermal information was found to be more important than reflective indices in the change attribution. Algorithm calibration used training data from ten Path/Rows located strategically across Queensland with four images sampled per path row (n=40). Thresholds were optimised to maximise the burnt area detected while limiting under/over-growing of burnt area. Validation data covered a range of burnt areas from ten independent Path/Rows with ten images sampled across a range of burnt area fractions per Path/Row (n=100). The results for burnt area mapping demonstrated an average producer's accuracy of 85% (range of 28 to 100% for individual images) and average user's accuracy of 71% (range of 4 to 99% for individual images). A morphological dilation of one pixel restricted to locations exhibiting a decline in B45 over time, increased the producer's accuracy by 4% but reduced the user's accuracy by 8%. The total accuracy for the burnt area classification was greater than 99%, however this is more a reflection of the small fraction of landscape represented by burnt area rather than the ability to detect burnt area. Areas frequently misclassified were related to areas of high spectral/land use change which included areas of cropping, frequently inundated land, and moisture/ground cover variations over dark soils. In this study, we applied a crop and water mask to minimise commission errors. Significantly, the results of this study demonstrate that an automated time series method for mapping burnt areas can be successfully applied across a diversity of land cover types. The method may be applied in similar savanna dominated environments but is likely to require modification to be applicable in other landscapes.

Multi-scale segmentation approach for object-based land-cover classification using high-resolution imagery

Image segmentation is a basic and important procedure in object-based classification of remote-sensing data. This study presents an approach to multi-scale optimal segmentation (OS), given that single-scale segmentation may not be the most suitable approach to map a variety of land-cover types characterized by various spatial structures; it objectively measures the appropriate segmentation scale for each object at various scales and projects them onto a single layer. A 1.8 m spatial resolution Worldview-2 image was used to perform successive multi-scale segmentations. The pixel standard deviation of an object was used to measure the optimal scale that occurred on the longest, feature unchanged scale range during multi-scale segmentation. Results indicate that the classification of multi-scale object OS can improve the overall accuracy by five percentage points compared to traditional single segmentation.

Digital Elevation Quality Derived from Medium‐Format Digital Cameras

AbstractMedium‐format digital cameras are a useful tool in airborne lidar missions to produce digital ortho‐images. This study investigates the quality of digital elevation models (DEMs) and digital surface models (DSMs) derived from such cameras. An image‐matching strategy is used to produce height information from highly overlapping images and the quality of the derived height information is analysed using synchronous lidar point clouds as the reference data. The evaluation focused on various types of land cover and DEM generation methods. The RMSE between lidar and image‐derived height information for two datasets was 0·054% and 0·027% of the flying height in flat, bare‐earth areas, but studies with manually edited vegetated areas using stereo photogrammetry revealed high uncertainties, especially in steep terrain.

Técnicas de mineração de dados para identificação de áreas com cana-de-açúcar em imagens Landsat 5

Neste trabalho, verificou-se a aderência de técnicas de mineração de dados voltadas para problemas de classificação de dados na identificação automatizada de áreas cultivadas com cana-de-açúcar, em imagens do satélite Landsat 5/TM. Para essa verificação, foram estudadas imagens de áreas cultivadas com cana-de-açúcar em três fases fenológicas diferentes. Os pixels foram convertidos em valores de refletância de superfície, nas vizinhanças das cidades de Araras, São Carlos e Araraquara, no Estado de São Paulo. Foram gerados cinco modelos de árvores de decisão binária, induzidos pelo algoritmo C4.5, em que todos produziram taxas de acerto superiores a 90%. A introdução de atributos de textura trouxe ganhos significativos na acurácia do modelo de classificação e contribuiu para melhorar a distinção de áreas cultivadas com cana-de-açúcar em meio a tipos diversos de cobertura do solo, como solo exposto, área urbana, lagos e rios. Os índices de vegetação mostraram-se relevantes na distinção da fase e do estado fenológico das culturas. Os resultados reforçam o potencial forte das árvores de decisão no processo de classificação e identificação de áreas cultivadas com cana-de-açúcar, em diferentes cidades produtoras, no Estado de São Paulo.

Validating the BERMS in situ Soil Water Content Data Record with a Large Scale Temporary Network

Calibration and validation of soil moisture satellite products requires data records of large spatial and temporal extent and for diverse land cover types. Obtaining these data, especially for forests, can be challenging. These challenges can include the remoteness of the locations and expense of equipment. The Boreal Ecosystem Research and Monitoring Sites (BERMS) network in Saskatchewan, Canada, is a long‐term ecosystem network which includes five soil water content profile stations. These stations provide a critical but incomplete view of the soil water content patterns across a study domain of 10,000 square km; however, the representativeness of these observations for this purpose has not yet been evaluated. In coordination with the Canadian Experiment‐Soil Moisture 2010 (CANEX‐SM10), a temporary network of surface soil water content sensors was installed during the summer of 2010 to enhance the data resources of the BERMS network. This short term data record was then used as a basis for up‐scaling and validating the BERMS network. This large domain is approximately 1200 square km and provides a higher confidence because of the increased number of sampling sites. Using temporal stability analysis, this network verified that the BERMS network could be scaled to a satellite scale footprint with a root mean square error (RMSE) of 0.025 m3 m−3, and applied to the entire period of record.

Land cover effects on runoff patterns in eastern Piedmont (USA) watersheds

Physiography and land cover determine the hydrologic response of watersheds to climatic events. However, vast differences in climate regimes and variation of landscape attributes among watersheds (including size) have prevented the establishment of general relationships between land cover and runoff patterns across broad scales. This paper addresses these difficulties by using power spectral analysis to characterize area-normalized runoff patterns and then compare these patterns with landscape features among watersheds within the same physiographic region. We assembled long-term precipitation and runoff data for 87 watersheds (first to seventh order) within the eastern Piedmont (USA) that contained a wide variety of land cover types, collected environmental data for each watershed, and compared the datasets using a variety of statistical measures. The effect of land cover on runoff patterns was confirmed. Urban-dominated watersheds were flashier and had less hydrologic memory compared with forest-dominated watersheds, whereas watersheds with high wetland coverage had greater hydrologic memory. We also detected a 10–15% urban threshold above which urban coverage became the dominant control on runoff patterns. When spectral properties of runoff were compared across stream orders, a threshold after the third order was detected at which watershed processes became dominant over precipitation regime in determining runoff patterns. Finally, we present a matrix that characterizes the hydrologic signatures of rivers based on precipitation versus landscape effects and low-frequency versus high-frequency events. The concepts and methods presented can be generally applied to all river systems to characterize multiscale patterns of watershed runoff. Copyright © 2013 John Wiley & Sons, Ltd.

Validation of MODIS, MISR, OMI, and CALIPSO aerosol optical thickness using ground-based sunphotometers in Hong Kong

Aerosol observations are essential for understanding the Earth's radiation budget and the complexities of climate change, as they are involved in the backscattering of solar radiation and the formation of cloud condensation nuclei. In Hong Kong, the most direct effect is on air quality. Atmospheric haze caused by the emission of aerosols from industrial and vehicular sources creates visibility lower than 8 km for approximately 20% of the time, having risen at 6% per decade since 1980, but regional emissions are at least as influential as local ones. The 179,000 km2 covered by Hong Kong and neighbouring Guangdong Province cannot be adequately covered by the 76 monitoring stations set up by the two governments, and satellite images offer the only potential source of regional air quality data. However, the current satellite-based aerosol optical thickness (AOT) products are intended for global air quality monitoring, and may contain errors over a humid coastal city such as Hong Kong and its surrounding industrialized regions. This research compares the AOT retrieved from several AOT operational products, namely the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD04 product, the MODIS 500 m product, the Multiangle Imaging Spectroradiometer (MISR) product, the Ozone Monitoring Instrument (OMI) multiwavelength aerosol product, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) product, with ground-based AOT from sunphotometers in Hong Kong. These sunphotometers include two AERONET stations, and are deployed in Hong Kong over urban, suburban, and coastal areas. The rigorous correlations, root mean square errors, and mean absolute differences available from the multilocational field data within one city region provide a strong base for validating the AOT products from different sensors and at different spatial scales over different land surface types. The results suggest that the AOT products, especially those from MODIS 10 km, provide reliable and accurate observations for daily air quality monitoring over a variety of land-cover types, as well as for identifying emission sources for coordinated actions by the governments of Hong Kong and the Chinese mainland.

Drought impact assessment from monitoring the seasonality of vegetation condition using long-term time-series satellite images: a case study of Mt. Kenya region

Drought-induced anomalies in vegetation condition over wide areas can be observed by using time-series satellite remote sensing data. Previous methods to assess the anomalies may include limitations in considering (1) the seasonality in terms of each vegetation-cover type, (2) cumulative damage during the drought event, and (3) the application to various types of land cover. This study proposed an improved methodology to assess drought impact from the annual vegetation responses, and discussed the result in terms of diverse landscape mosaics in the Mt. Kenya region (0.4° N 35.8° E ~ 1.6° S 38.4° E). From the 30-year annual rainfall records at the six meteorological stations in the study area, we identified 2000 as the drought year and 2001, 2004, and 2007 as the normal precipitation years. The time-series profiles of vegetation condition in the drought and normal precipitation years were obtained from the values of Enhanced Vegetation Index (EVI; Huete et al. 2002), which were acquired from Terra MODIS remote sensing dataset (MOD13Q1) taken every 16 days at the scale of 250-m spatial resolution. The drought impact was determined by integrating the annual differences in EVI profiles between drought and normal conditions, per pixel based on nearly same day of year. As a result, we successfully described the distribution of landscape vulnerability to drought, considering the seasonality of each vegetation-cover type at every MODIS pixel. This result will contribute to the large-scale landscape management of Mt. Kenya region. Future study should improve this method by considering land-use change occurred during the long-term monitoring period.