Mapping fire scars in global boreal forests using imaging radar data

This study is an extension of earlier research which demonstrated the utility of ERS SAR data for detection and monitoring of fire-disturbed boreal forests of Alaska. Fire scars were mappable in Alaska due to the ecological changes that occur post-burn including increased soil moisture. High soil moisture caused a characteristic enhanced backscatter signal to be received by the ERS sensor from burned forests. Since regional ecological differences in the global boreal biome may have an effect on post-fire ecosystem changes, it may also affect how fire scars appear in C-band SAR imagery. In the current study we evaluate the use of C-band SAR data to detect, map and monitor boreal fire scars globally. Study sites include four regions of Canada and an area in central Russia. Fire boundaries were mapped from SAR data without a priori knowledge of fire scar locations. SAR-derived maps were validated with fire service records and field checks. Based on results from test areas in Northwest Territories, Ontario, southeastern Quebec, and central Russia, C-band SAR data have high potential for use in detecting and mapping fire scars globally.     

Spectral indices and fire behavior simulation for fire risk assessment in savanna ecosystems

The lack of information on the vegetation status before the use of fire as a management tool in protected areas leads to drastic destruction of the natural vegetation and biodiversity. This paper describes the use of spectral indices and simulation of savanna burning to assess risk of intensive fire propagation in a National Park (Niokolo Koba, Senegal, West Africa). Spectral parameters corresponding to thematic information (wetness, brightness, and greenness) were retrieved using an orthogonal transformation, the Tasseled Cap approach on LANDSAT-ETM images. Wetness and brightness indices were normalized (σ=1 and mean=0) and then combined in a simple semi-empirical algorithm of fire risk levels discrimination. These two indices are proven to reflect qualitatively both fuel moisture and its distribution, which constitute the most foreseen determinants of fire propagation in savanna areas. The fire risk assessment algorithm (FIRA algorithm) was used to produce a fire risk map at the beginning of the management fire implement period. In parallel, a fire area simulator (FARSITE) developed by USDA was used with randomly spaced fire sources to determine areas which can be potentially burned in the study site. These simulated burned areas and the FIRA algorithm results were cross-compared to a real fire scars dated at the end of the same burning period and to land cover map. A great consistency was found between the different sources of information. More than 85% of fire prone areas identified by the FIRA algorithm or simulated by FARSITE were located in trees-shrub, woodland, and shrub savannas. These cover types included also 95% of real fire scars. Almost 88% and 84% of real fire scars were found in the risk zones determined by the FIRA algorithm and the simulated burned areas by FARSITE, respectively. The method used is simple and suited for an operational use for management fire implementation in savanna ecosystems.     

Full-polarimetric burn scar mapping – the differences of active fire and post-fire situations

Fire is a threat to human lives, infrastructure, and forestry. Satellite-based Earth observations enable a fast, efficient, and reliable estimation of burnt area. In most cases, optical satellite data are used for burn scar detection. However, smoke and cloud coverage strongly limits the suitability of optical imagery for rapid mapping of burn scars. Here, an automated procedure based on quad-polarized L-band Synthetic Aperture Radar (SAR) data is presented to enable fast burn scar mapping independently of the weather and smoke conditions. Full-polarimetric SAR data enable the decomposition of the SAR backscatter into different scattering mechanisms, describing the scatterer more precisely. Important differences in the polarimetric backscattering behaviour during the active fire and post-fire situations are reported. While the SPAN difference is best suited for burn scar mapping during active fire situations, the Entropy-Anisotropy-Alpha and the generalized Freeman–Durden decompositions showed the best suitability for burnt area mapping several months after the end of the fire. The proposed methodology is an object-based image analysis approach based on change detection. The big fire event which affected Fort McMurray, Alberta, Canada, in May–June 2016 was investigated as a case study.     

Use of multitemporal ERS-2 SAR images for identification of burned scars in south-east Asian tropical rainforest

Evaluation of an area severely affected by fires in 1998 using a multitemporal series of ERS-2 Synthetic Aperture Radar (SAR) images showed that fire induced changes of the vegetation cover strongly affected C-band radar backscatter. We investigated the changes in radar backscatter over a period of ten months in areas of interest that represented different land-cover types at a study site in East Kalimantan, Indonesia. The impact of fire was found to cause a strong decrease in backscatter (2-5 dB) for all land-cover classes while areas not affected by fire showed only slight variations in backscatter (maximum 0.5 dB). Ground and aerial evidence suggests that the marked decrease in backscatter can be attributed to the removal of the vegetation cover and subsequently higher contribution of backscatter from dry soil. After the onset of rain the radar backscatter increased to 5.5 dB in areas severely affected by fire while in unburned forests it returned to values similar to those before the drought. Burned scars could be identified visually in multitemporal principal component analysis-enhanced ERS SAR colour composites.     

Evaluation of RADARSAT-1 data for identification of burnt areas in Southern Europe

This study presents the combined analysis of RADARSAT products of different spatial resolutions acquired under different incidence angles for mapping burnt scars on forested areas of Central Portugal. Prior to the SAR data analysis, a number of pre-processing procedures were carried out. Noise was eliminated by adaptive texture preserving filtering. A specific algorithm for the geocoding of SAR images, based on a range-Doppler approach, enabled precise geocoding of the SAR data by means of a single very accurate ground control point. A novel incidence-angle-normalization for SAR imagery was applied to analyze the backscatter coefficient to a given incidence angles. Further, a backscatter coefficient analysis was performed according to the slope on forested areas and fire disturbed areas. A qualitative and quantitative investigation of the backscattering as related to the slope angle and time changes was performed. As a result of this analysis, the scenes that allowed maximizing the discrimination of burnt areas were selected as the input for the neural network classification. The investigation on the effect of the SAR incidence angle in burnt area discrimination determined that low incidence angles are required for discriminating burnt areas in hilly regions. It was also demonstrated that topography influences the level of discrimination of burnt areas since areas affected by forest fires on face-slopes presents higher backscatter coefficient than those back-slopes. Therefore, SAR data can play a significant role for burnt area mapping in Europe in those areas where optical data cannot be used due to persistent cloud cover.     

Short contributions

Studies of ERS-1 synthetic aperture radar (SAR) imagery have shown that fire scars in Alaskan forests are significantly brighter (3–6 dB) than surrounding unburned forest. The signature varies seasonally and changes as vegetation re-establishes on the site over longer time periods (>5years). Additionally, it is known that soil water content typically increases following forest fires due to changes in evapotranspiration rates and melting of the permafrost.

The objective of this study was to understand the relation between soil water content and the ERS-1 SAR signature at fire-disturbed sites. To accomplish this objective, we compared soil water in six burned black spruce (Picea mariana (Mill.) B.S.P.) forest sites in interior Alaska to ERS-1 SAR backscalter measurements. The six sites are of various age since burn. Soil water was periodically measured at each site during the summer of 1992 and at one site in 1993 and 1994 when the ERS-1 imaging radar was scheduled to pass overhead. Results indicate that a positive linear relation exists between soil water content and the SAR backscatter coefficient in young burns ( < ～4years). Older burns do not show this relation, a result of vegetation establishment following the burn. This interaction between soil moisture condition and ERS-1 SAR backscatter shows great potential for measuring soil water content and monitoring seasonal variations in soil water content in black spruce sites recently disturbed by wildfire.     

A novel approach for the characterization of tundra wetland regions with C-band SAR satellite data

A circumpolar representative and consistent wetland map is required for a range of applications ranging from upscaling of carbon fluxes and pools to climate modelling and wildlife habitat assessment. Currently available data sets lack sufficient accuracy and/or thematic detail in many regions of the Arctic. Synthetic aperture radar (SAR) data from satellites have already been shown to be suitable for wetland mapping. Envisat Advanced SAR (ASAR) provides global medium-resolution data which are examined with particular focus on spatial wetness patterns in this study. It was found that winter minimum backscatter values as well as their differences to summer minimum values reflect vegetation physiognomy units of certain wetness regimes. Low winter backscatter values are mostly found in areas vegetated by plant communities typically for wet regions in the tundra biome, due to low roughness and low volume scattering caused by the predominant vegetation. Summer to winter difference backscatter values, which in contrast to the winter values depend almost solely on soil moisture content, show expected higher values for wet regions. While the approach using difference values would seem more reasonable in order to delineate wetness patterns considering its direct link to soil moisture, it was found that a classification of winter minimum backscatter values is more applicable in tundra regions due to its better separability into wetness classes. Previous approaches for wetland detection have investigated the impact of liquid water in the soil on backscatter conditions. In this study the absence of liquid water is utilized.

Owing to a lack of comparable regional to circumpolar data with respect to thematic detail, a potential wetland map cannot directly be validated; however, one might claim the validity of such a product by comparison with vegetation maps, which hold some information on the wetness status of certain classes. It was shown that the Envisat ASAR-derived classes are related to wetland classes of conventional vegetation maps, indicating its applicability; 30% of the land area north of the treeline was identified as wetland while conventional maps recorded 1–7%.     

Detecting a moorland wildfire scar in the Peak District,UK, using synthetic aperture radar from ERS-2 and Envisat ASAR

Wildfires occur annually in UK moorland environments, especially in drought years. They can be severely damaging to the ecosystem when they burn deep into the peat, killing ground-nesting birds and releasing CO₂ into the atmosphere. Synthetic aperture radar (SAR) was evaluated for detecting the 18 April 2003 Bleaklow wildfire scar (7.4 km²). SAR’s ability to penetrate cloud is advantageous in this inherently overcast area. SAR can provide fire scar boundary information which is otherwise labour intensive to collect in the field using a global positioning system (GPS). This article evaluates the potential of SAR intensity and InSAR coherence to detect a large peat moorland wildfire scar in the Peak District of northern England. A time-series of pre-fire and post-fire ERS-2 and advanced synthetic aperture radar (ASAR) Single Look Complex (SLC) data were pre-processed using SARScape 4.2 to produce georeferenced greyscale images. SAR intensity and InSAR coherence values were analysed against Coordinate Information on the Environment (CORINE) land‐cover classes and precipitation data. SAR intensity detected burnt peat well after a precipitation event and for previous fire events within the CORINE peat bog class. For the 18 April 2003 fire event, intensity increased to 0.84 dB post-fire inside the fire scar for the peat bog class. InSAR coherence peaked post-fire for moors and heathland and natural grassland classes inside the fire scar, but peat bog exposed from previous fires was less responsive. Overall, SAR was found to be effective for detecting the Bleaklow moorland wildfire scar and monitoring wildfire scar persistence in a degraded peat landscape up to 71 days later. Heavy precipitation amplified the SAR fire scar signal, with precipitation after wildfires being typical in UK moorlands. Further work is required to disentangle the effects of fire size, topography, and less generalized land‐cover classes on SAR intensity and InSAR coherence for detecting fire scars in degraded peat moorlands.     

Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (σ^o) to soil and plant conditions, we found that SAR σ^o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR σ^o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r²=.67) between normalized SAR σ^o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment.     

The detection and mapping of Alaskan wildfires using a spaceborne imaging radar system

The study presented here focuses on using a spaceborne imaging radar, ERS-1, for mapping and estimating areal extent of fires which occurred in the interior region of Alaska. Fire scars are typically 3 to 6 dB brighter than adjacent unburned forests in the ERS-1 imagery. The enhanced backscatter from burned areas was found to be a result of high soil moisture and exposed rough ground surfaces. Fire scars from 1979 to 1992 are viewed in seasonal ERS-1 synthetic aperture radar (SAR) data obtained from 1991 to 1994. Three circumstances which influence the detectability of fire scars in the ERS-1 imagery are identified and examined; seasonality of fire scar appearances, fires occurring in mountainous regions, and fires occurring in wetland areas. Area estimates of the burned regions in the ERS-1 imagery are calculated through the use of a Geographic Information System (GIS) database. The results of this analysis are compared to fire records maintained by the Alaska Fire Service (AFS) and to estimates obtained through a similar study using the Advanced Very High Resolution Radiometer (AVHRR) sensor.     

Environmental factors and the detection of open surface water areas with X-band radar imagery

The ability to map open surface water is integral to many hydrologic and agricultural models, wildlife management programmes, and recreational and natural resource studies. Open surface water is generally regarded as easily detected on radar imagery. However, this view is an oversimplification. This study used X-band HH polarized airborne Synthetic Aperture Radar ( SAR) imagery to examine the potential of SAR data to map open fresh water areas extant on 1:100000 USGS topographic maps. Seven study sites in the U.S.A. with a combined area of over 68000km²were analysed. Detection accuracies and minimum size for detection varied among the seven locations. Size and shape of water bodies and radar shadow all affected detection. However, environmental modulation factors including vegetation and forest cover, moisture, and landscape composition and morphology had the greatest influence and exhibited the most complex role in explaining variability     

A semi-automatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series

This paper presents a semi-automatic methodology for fire scars mapping from a long time series of remote sensing data. Approximately, a hundred MSS images from different Landsat satellites were employed over an area of 32 100 km² in the north-east of the Iberian Peninsula. The analysed period was from 1975 to 1993. Results are a map series of fire history and frequencies. Omission errors are 23% for burned areas greater than 200 ha while commission errors are 8% for areas greater than 50 ha. Subsequent work based on the resultant fire scars will also help in describing fire regime and in monitoring post-fire regeneration dynamics.     

Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data

The remote sensing of Earth surface changes is an active research field aimed at the development of methods and data products needed by scientists, resource managers, and policymakers. Fire is a major cause of surface change and occurs in most vegetation zones across the world. The identification and delineation of fire-affected areas, also known as burned areas or fire scars, may be considered a change detection problem. Remote sensing algorithms developed to map fire-affected areas are difficult to implement reliably over large areas because of variations in both the surface state and those imposed by the sensing system. The availability of robustly calibrated, atmospherically corrected, cloud-screened, geolocated data provided by the latest generation of moderate resolution remote sensing systems allows for major advances in satellite mapping of fire-affected area. This paper describes an algorithm developed to map fire-affected areas at a global scale using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance time series data. The algorithm is developed from the recently published Bi-Directional Reflectance Model-Based Expectation change detection approach and maps at 500 m the location and approximate day of burning. Improvements made to the algorithm for systematic global implementation are presented and the algorithm performance is demonstrated for southern African, Australian, South American, and Boreal fire regimes. The algorithm does not use training data but rather applies a wavelength independent threshold and spectral constraints defined by the noise characteristics of the reflectance data and knowledge of the spectral behavior of burned vegetation and spectrally confusing changes that are not associated with burning. Temporal constraints are applied capitalizing on the spectral persistence of fire-affected areas. Differences between mapped fire-affected areas and cumulative MODIS active fire detections are illustrated and discussed for each fire regime. The results reveal a coherent spatio-temporal mapping of fire-affected area and indicate that the algorithm shows potential for global application.     

Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas

Remote sensing in the optical band is a well-established tool for monitoring changes in forested areas, although it can suffer from limitations, especially where frequent cloud cover occurs. The increased availability of space-borne radar imagery offers additional means for assessing the state of forests and monitoring their dynamics. In this study, the potential of multi-temporal space-borne SAR data for monitoring vegetation recovery over burned areas next to the Mediterranean coast is investigated. In particular, the study considers a set of ERS-SAR images, C-band and VV polarization, taken over the Castel Fusano pinewood, located near Rome, Italy, devastated in summer 2000 by a fire that burned about 350 ha of the wood. Starting from the analysis of the information contained in the variations, both in burnt and unburnt areas, of the inter annual multitemporal backscattering signatures, the study presents two different approaches, one more qualitative, the other one more quantitative, for the retrieval of the biomass re-growth after the fire. In the quantitative case, the inversion procedure computes the biomass re-growth rate by means of simulations carried out with the Tor Vergata scattering model. The obtained results are satisfactory as they are in agreement with simultaneous analysis based on optical data and in-situ measurement campaigns.     

Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA

Multispectral satellite data have become a common tool used in the mapping of wildland fire effects. Fire severity, defined as the degree to which a site has been altered, is often the variable mapped. The Normalized Burn Ratio (NBR) used in an absolute difference change detection protocol (dNBR), has become the remote sensing method of choice for US Federal land management agencies to map fire severity due to wildland fire. However, absolute differenced vegetation indices are correlated to the pre-fire chlorophyll content of the vegetation occurring within the fire perimeter. Normalizing dNBR to produce a relativized dNBR (RdNBR) removes the biasing effect of the pre-fire condition. Employing RdNBR hypothetically allows creating categorical classifications using the same thresholds for fires occurring in similar vegetation types without acquiring additional calibration field data on each fire. In this paper we tested this hypothesis by developing thresholds on random training datasets, and then comparing accuracies for (1) fires that occurred within the same geographic region as the training dataset and in similar vegetation, and (2) fires from a different geographic region that is climatically and floristically similar to the training dataset region but supports more complex vegetation structure. We additionally compared map accuracies for three measures of fire severity: the composite burn index (CBI), percent change in tree canopy cover, and percent change in tree basal area. User's and producer's accuracies were highest for the most severe categories, ranging from 70.7% to 89.1%. Accuracies of the moderate fire severity category for measures describing effects only to trees (percent change in canopy cover and basal area) indicated that the classifications were generally not much better than random. Accuracies of the moderate category for the CBI classifications were somewhat better, averaging in the 50%-60% range. These results underscore the difficulty in isolating fire effects to individual vegetation strata when fire effects are mixed. We conclude that the models presented here and in Miller and Thode ([Miller, J.D. & Thode, A.E., (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109, 66-80.]) can produce fire severity classifications (using either CBI, or percent change in canopy cover or basal area) that are of similar accuracy in fires not used in the original calibration process, at least in conifer dominated vegetation types in Mediterranean-climate California.     

MODIS-informed greenness responses to daytime land surface temperature fluctuations and wildfire disturbances in the Alaskan Yukon River Basin

Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of Moderate Resolution Imaging Spectroradiometer (MODIS) data) acquired between 2000 and 2009. Our analyses indicate measureable effects on NDVI values from vegetation type, burn severity, post-fire time, and climatic variables. The NDVI observations from both fire scars and unburned areas across the Alaskan YRB showed a tendency of an earlier start to the growing season (GS); the annual variations in NDVI were significantly correlated to daytime land surface temperature (LST) fluctuations; and the rate of post-fire green-up depended mainly on burn severity and the time of post-fire succession. The higher average NDVI values for the study period in the fire scars than in the unburned areas between 1950 and 2000 suggest that wildfires enhance post-fire greenness due to an increase in post-fire evergreen and deciduous species components.     

Dambo mapping for environmental monitoring using Landsat TM and SAR imagery: Case study in the Zambian Copperbelt

The purpose of this study was to monitor the impact of mining in the Zambian Copperbelt, specifically using dambos as an environmental indicator for pollution. Data fusion using a Brovey transform was used for combining speckle filtered radar data with optical data to effectively map natural dambos and dambos that have degraded due to human impact. Comparative analysis of raw images and fusion product reveals that, whereas natural dambos show low values on Landsat reflective bands and low backscatter response in SAR imagery, degraded dambos have mixed spectral responses. Degraded dambos are difficult to identify in either optical or SAR images alone, but a fusion product highlights complimentary spectral information, making these environmental indicators uniquely identifiable.     

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

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

On the sensitivity of polarimetric SAR measurements to vegetation cover: the Coiba National Park,Panama

In this study, the sensitivity of multi-polarization synthetic aperture radar (SAR) features to vegetation cover is investigated over a test case of environmental importance: the Coiba National Park, Panama. Single-polarization intensity features and polarimetric features derived from the eigenvalue/eigenvector decomposition are analysed and their classification performance, evaluated against a reference land-cover map using a simple clustering algorithm, is contrasted with conventional optical features.

Experiments, undertaken using actual L-band full-polarimetric SAR and Landsat data, show that (a) polarimetric information plays a key role in improving the classification accuracy with some polarimetric features performing better than single-polarization and optical ones, (b) classification performance of radar features is significantly affected by incidence angles, and (c) a joint use of different radar features is expected to increase classification accuracy.     

A comparison of TerraSAR-X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta,Vietnam

Satellite-based multispectral imagery and/or synthetic aperture radar (SAR) data have been widely used for vegetation characterization, plant physiological parameter estimation, crop monitoring or even yield prediction. However, the potential use of satellite-based X-band SAR data for these purposes is not fully understood. A new generation of X-band radar satellite sensors offers high spatial resolution images with different polarizations and, therefore, constitutes a valuable information source. In this study, we utilized a TerraSAR-X satellite scene recorded during a short experimental phase when the sensor was running in full polarimetric ‘Quadpol’ mode. The radar backscatter signals were compared with a RapidEye reference data set to investigate the potential relationship of TerraSAR-X backscatter signals to multispectral vegetation indices and to quantify the benefits of TerraSAR-X Quadpol data over standard dual- or single-polarization modes. The satellite scenes used cover parts of the Mekong Delta, the rice bowl of Vietnam, one of the major rice exporters in the world and one of the regions most vulnerable to climate change. The use of radar imagery is especially advantageous over optical data in tropical regions because the availability of cloudless optical data sets may be limited to only a few days per year. We found no significant correlations between radar backscatter and optical vegetation indices in pixel-based comparisons. VV and cross-polarized images showed significant correlations with combined spectral indices, the modified chlorophyll absorption ratio index/second modified triangular vegetation index (MCARI/MTVI2) and transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI), when compared on an object basis. No correlations between radar backscattering at any polarization and the normalized difference vegetation index (NDVI) were observed.