Agreement evaluation of AVHRR and MODIS 16‐day composite NDVI data sets

Satellite‐derived normalized difference vegetation index (NDVI) data have been used extensively to detect and monitor vegetation conditions at regional and global levels. A combination of NDVI data sets derived from AVHRR and MODIS can be used to construct a long NDVI time series that may also be extended to VIIRS. Comparative analysis of NDVI data derived from AVHRR and MODIS is critical to understanding the data continuity through the time series. In this study, the AVHRR and MODIS 16‐day composite NDVI products were compared using regression and agreement analysis methods. The analysis shows a high agreement between the AVHRR‐NDVI and MODIS‐NDVI observed from 2002 and 2003 for the conterminous United States, but the difference between the two data sets is appreciable. Twenty per cent of the total difference between the two data sets is due to systematic difference, with the remainder due to unsystematic difference. The systematic difference can be eliminated with a linear regression‐based transformation between two data sets, and the unsystematic difference can be reduced partially by applying spatial filters to the data. We conclude that the continuity of NDVI time series from AVHRR to MODIS is satisfactory, but a linear transformation between the two sets is recommended.     

An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data

Daily daytime Advanced Very High Resolution Radiometer (AVHRR) 4‐km global area coverage data have been processed to produce a Normalized Difference Vegetation Index (NDVI) 8‐km equal‐area dataset from July 1981 through December 2004 for all continents except Antarctica. New features of this dataset include bimonthly composites, NOAA‐9 descending node data from August 1994 to January 1995, volcanic stratospheric aerosol correction for 1982–1984 and 1991–1993, NDVI normalization using empirical mode decomposition/reconstruction to minimize varying solar zenith angle effects introduced by orbital drift, inclusion of data from NOAA‐16 for 2000–2003 and NOAA‐17 for 2003–2004, and a similar dynamic range with the MODIS NDVI. Two NDVI compositing intervals have been produced: a bimonthly global dataset and a 10‐day Africa‐only dataset. Post‐processing review corrected the majority of dropped scan lines, navigation errors, data drop outs, edge‐of‐orbit composite discontinuities, and other artefacts in the composite NDVI data. All data are available from the University of Maryland Global Land Cover Facility (http://glcf.umiacs.umd.edu/data/gimms/).     

Understanding precipitation patterns and land use interaction in Tibet using harmonic analysis of SPOT VGT‐S10 NDVI time series

Time series analysis of Normalized Difference Vegetation Index (NDVI) imagery is a powerful tool in studying land use and precipitation interaction in data‐scarce and inaccessible areas. The Fast Fourier Transform (FFT) was applied to the annual time series of 36 average dekadal NDVI images. The dekadal annual average pattern was calculated from 189 NDVI images from April 1998 to June 2003 acquired with the VEGETATION instruments of the SPOT‐4 and SPOT‐5 satellites in Tibet. It is shown that the first two harmonic terms of a Fourier series suffice to distinguish between land use classes. The results indicate that the highest biomass production occurs before the monsoon peak. Regression analysis with 15 meteorological stations has shown that the total amount of precipitation during the growing season shows the strongest relation with the sum of the amplitudes of the first two harmonic terms (R ²?=?0.72). Inter‐annual NDVI variation based on Fourier‐transformed time series was studied and it was shown that, early in the season, the expected NDVI behaviour of the up‐coming season could be forecast; if linked to food production this might provide a robust early warning system. The most important conclusion from this work is that harmonic time series analysis yields more reliable results than ordinary time series analysis.     

Characterizing post‐fire vegetation recovery of California chaparral using TM/ETM+ time‐series data

A time series of normalized difference vegetation index (NDVI) data derived from 11 TM/ETM+ images was used to examine the recovery characteristics of chaparral vegetation in a small watershed near Santa Barbara, California following a fire event in 1985. The NDVI recovery trajectory was compared to a generalized recovery trajectory of leaf area index (LAI) for the same region, which was established using a chronosequence approach and TM/ETM+ data. Post‐fire NDVI recovery trajectories were derived for the entire catchment and for individual vegetation types. Post‐fire NDVI spatial patterns on each image date were compared to the pre‐fire pattern to determine the extent to which the pre‐fire pattern was re‐established, and the rate of this recovery. Results indicated that the post‐fire recovery trajectory for the catchment area average NDVI was similar to the previously established regional LAI trajectory based on a chronosequence approach. The NDVI recovery was disrupted by drought stress and attained pre‐fire levels approximately 10 years after the fire. Individual vegetation types did not exhibit different rates of recovery and the recovery trajectories were only distinguished by the maximum post‐fire NDVI observed after 10 years. The post‐fire NDVI spatial pattern also showed a systematic return to pre‐fire conditions, but exhibited a more substantial disruption due to drought stress than was the case for the average NDVI recovery trajectory.     

A comparative analysis of the use of NOAA‐AVHRR NDVI and FWI data for forest fire risk assessment

Fires are a major hazard to forests in the Mediterranean region, where, on average, half a million hectares of forested areas are burned every year. The assessment of fire risk is therefore at the heart of fire prevention policies in the region. The estimation of forest fire risk often involves the integration of meteorological and other fuel‐related variables, leading to an index that assesses the different levels of risk. Two indices frequently used to estimate the level of fire risk are the Fire Weather Index (FWI) and the Normalized Difference Vegetation Index (NDVI). Although a correlation between the number of fires and the level of risk determined by these indices has been demonstrated in previous studies, the analyses focused on the changes in fire risk levels in areas where fires took place. The present study analyses the behaviour of the fire risk indices not only in areas where fires occurred but also in areas where fires did not take place. Specifically, the objective of this work was to compare the potential of the two indices to discriminate different levels of fire risk over large areas. Qualitative and quantitative methods were used to compare the statistical distributions of fire event frequencies with those of fire risk levels. The qualitative method highlights graphically the statistical difference between the values of the indices computed over burnt areas and the overall distribution of the values of the indices. The quantitative method, based on the use of the so‐called performance index, was used to evaluate and compare numerically the potential of the indices. The analyses were performed considering very extensive datasets of fire events, satellite data and meteorological data for Spain during a 10‐year period. Although the NDVI is assumed to describe the vegetation status as related to fire ignition, the results show conclusively an enhanced performance of the FWI over the NDVI in identifying areas at risk of fires.     

Monitoring Sahelian floodplains using Fourier analysis of MODIS time‐series data and artificial neural networks

Fourier analysis of Moderate Resolution Image Spectrometer (MODIS) time‐series data was applied to monitor the flooding extent of the Waza‐Logone floodplain, located in the north of Cameroon. Fourier transform (FT) enabled quantification of the temporal distribution of the MIR band and three different indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Enhanced Vegetation Index (EVI). The resulting amplitude, phase, and amplitude variance images for harmonics 0 to 3 were used as inputs for an artificial neural network (ANN) to differentiate between the different land cover/land use classes: flooded land, dry land, and irrigated rice cultivation. Different combinations of input variables were evaluated by calculating the Kappa Index of Agreement (KIA) of the resulting classification maps. The combinations MIR/NDVI and MIR/EVI resulted in the highest KIA values. When the ANN was trained on pixels from different years, a more robust classifier was obtained, which could consistently separate flooded land from dry land for each year.     

Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985–2006

Long‐term changes in the Normalized Difference Vegetation Index (NDVI) have been evaluated in several studies but results have not been conclusive due to differences in data processing as well as the length and time of the analysed period. In this research a newly developed 1 km Advanced Very High Resolution Radiometer (AVHRR) satellite data record for the period 1985–2006 was used to rigorously evaluate NDVI trends over Canada. Furthermore, climate and land cover change as potential causes of observed trends were evaluated in eight sample regions. The AVHRR record was generated using improved geolocation, cloud screening, correction for sun‐sensor viewing geometry, atmospheric correction, and compositing. Results from both AVHRR and Landsat revealed an increasing NDVI trend over northern regions where comparison was possible. Overall, 22% of the vegetated area in Canada was found to have a positive NDVI trend based on the Mann–Kendal test at the 95% confidence level. Of these, 40% were in northern ecozones. The mean absolute difference of NDVI measurements between AVHRR and Landsat data was <7%. When compared with results from other studies, similar trends were found over northern areas, while in southern regions the results were less consistent. Local assessment of potential causes of trends in each region revealed a stronger influence of climate in the north compared to the south. Southern regions with strong positive trends appeared to be most influenced by land cover change.     

Categorization of land‐cover change processes based on phenological indicators extracted from time series of vegetation index data

A straightforward method for categorizing temporal patterns of land‐cover change is presented. Two successive years of enhanced vegetation index (EVI) data derived from the Moderate Resolution Imagery Spectrometer (MODIS) were analysed. Five phenological indicators were extracted. Based on the inter‐annual difference of each of the five indicators, indices of change in phenology were calculated. An unsupervised classification of these five indices of change applied to pixels characterized by a high change magnitude led to the identification of seven categories of land‐cover change patterns. Thirty‐one per cent of the change pixels could clearly be explained by a difference in only one or two phenological indicators, e.g. a shift in the start of the growing season or an interruption of the growing season due to floods. The remaining change pixels were explained by a combination of more than two indices of change. The output of this analysis is an allocation of change pixels to broad categories of land‐cover change as a preliminary step for finer resolution analyses.     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

An automatic method for synthetically preprocessing multi‐source NOAA‐AVHRR series and FY‐1D 1A.5 data

Polar orbiting meteorological satellites have been used successfully in many fields in which applications, such as dynamic monitoring, need multi‐temporal remote sensing data imaged in the same region but at different times and with different sensors. The basis of these quantitative applications is automatic and synthetic preprocessing of different satellite data. In order to ensure the direct comparability of multi‐source satellite National Oceanic & Atmospheric Administration‐Advanced Very High Resolution Radiometer (NOAA‐AVHRR) series and FY‐1D 1A.5 data, based on its imaging mechanisms, the paper discusses radiant and geometric preprocessing methods used for visual and near‐infrared and thermal infrared channels. This is performed by analysing the technical parameters of the satellites and the sensors, theoretical numerical simulation and image data comparison. For synthetic radiance preprocessing, radiance calibration, spectral calibration for different sensors and calibration of the Sun elevation angles or the satellite zenith angles at the imaging time are included. Based on normalization of spatial resolution in scan‐line directions, rough geometric correction and precise registration are sequentially processed for geometric preprocessing with a higher precision. Combining the detailed steps introduced in the paper, automatic preprocessing of polar orbiting meteorological satellites can be realized.     

A ground‐validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula

An NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16‐day maximum value composite data from 2000 to 2005. To create the dataset, (1) the influence of the polar night and snow on the NDVI values was removed by replacing NDVI values in winter with a pixel‐specific NDVI value representing the NDVI outside the growing season when the pixel is free of snow; and (2) yearly NDVI time series were modelled for each pixel using a double logistic function defined by six parameters. Estimates of the onset of spring and the end of autumn were then mapped using the modelled dataset and compared with ground observations of the onset of leafing and the end of leaf fall in birch, respectively. Missing and poor‐quality data prevented estimates from being produced for all pixels in the study area. Applying a 5 km×5 km mean filter increased the number of modelled pixels without decreasing the accuracy of the predictions. The comparison shows good agreement between the modelled and observed dates (root mean square error = 12 days, n = 108 for spring; root mean square error = 10 days, n = 26, for autumn). Fennoscandia shows a range in the onset of spring of more than 2 months within a single year and locally the onset of spring varies with up to one month between years. The end of autumn varies by one and a half months across the region. While continued validation with ground data is needed, this new dataset facilitates the detailed monitoring of vegetation activity in Fennoscandia and the Kola peninsula.     

Time-series analysis of NDVI from AVHRR data over the Hindu Kush–Himalayan region for the period 1982–2006

The Hindu Kush–Himalayan (HKH) region with its surrounding mountains in central Asia is a region that has been warming at an alarming rate and is sensitive to climate change due to its heterogeneous terrain and high altitude. In a region where research is limited due to the paucity of field-based biophysical observations, analysis of remotely sensed data such as the normalized difference vegetation index (NDVI) can provide invaluable information on spatio-temporal patterns in linkages among land use, climate and vegetative phenological cycles, and trends in vegetative cover. In this study, NDVI data with 8 km spatial resolution for each 15 day composite period from 1982 to 2006 were analysed using a seasonal trend analysis technique, where the first step determines the annual mean and seasonal NDVI patterns across the HKH region. The second step analyses the non-parametric trends in magnitude and timing of the annual mean and seasonal NDVI cycle. The seasonal vegetation cycles were compared for the first and last ten years of the time series and were also analysed across areas undergoing significant change. Results indicated an overall greening trend in NDVI magnitude in most areas, particularly over open shrubland, grassland and cropland. Trends in the annual seasonal timing of NDVI indicated an earlier green-up for most parts of this region. Results also confirmed deforestation trends observed in a few states in northeastern India and Myanmar (Shan state) within the HKH region.     

Detecting the onset of snow‐melt using SSM/I data and the self‐organizing map

In this paper, we present an algorithm to estimate the onset of seasonal snow‐melt using space‐borne microwave radiometer data. We have earlier developed a simple model called a Channel Difference Algorithm (CDA) to estimate the beginning of the snow‐melt. The new algorithm, the SOM Detection Algorithm (SDA), is based on the use of an artificial neural network system a called Self‐Organizing Map (SOM). The purpose of this research is to develop a robust and simple algorithm feasible for operative use. The algorithm is tested using SSM/I data with hydrological predictions as reference data. The reference data covers two winters, 1997 and 1998, and is for the boreal forest zone in Finland. The results are promising. The SDA is able to estimate the beginning of the final snow‐melt well, especially if the snow water equivalent exhibits large values. Using low‐pass filtering for the SDA estimated time series, the estimation can be improved.     

Estimate of net primary productivity of aquatic vegetation of the Amazon floodplain using Radarsat and JERS‐1

Synthetic aperture radar images, combined with field measurements, were used to estimate net primary productivity (NPP) of aquatic vegetation in the lower Amazon. Input data for a NPP model are (i) the total biomass of aquatic vegetation, determined by radar imagery and field measurements and (ii) the area occupied by aquatic vegetation, determined from radar imagery. After correction for monthly biomass losses, the NPP of one growth cycle of aquatic vegetation was calculated in the image domain. The total net primary productivity of Hymenachne amplexicaules, the dominant aquatic vegetation in the area, was on average 19×10¹¹ g C yr^?1 for the entire area. Spatially, lower values of produced organic carbon (<900 g C m^?2 yr^?1) are confined to regions where the plants developed only in the beginning of the rising phase of the hydrological cycle. In general, values are higher (>5000 g C m^?2 yr^?1) in areas closer to the Amazon River where the availability and influence of nutrient‐rich water is greater.     

Spatial patterns of the NDVI–rainfall relationship at the seasonal and interannual time scales in an African savanna

Climate change is predicted to affect both the mean annual rainfall and its seasonal distribution over the African continent. Understanding their respective influences on primary production, an ecosystem's key feature, is therefore a major challenge for rangeland ecologists. We have investigated the change in intra‐ and interannual Normalized Difference Vegetation Index (NDVI) in relation to rainfall in Hwange National Park, Zimbabwe. Two distinct NDVI time series were built using NOAA/AVHRR data for the period 1982–2002. Long‐term monthly means described the change in seasonal NDVI, whereas annually integrated NDVI related to year‐to‐year fluctuations. The rainfall–NDVI relationship was stronger along the seasonal course [with a lag of 1 month, Kendall's tau (τ) = 0.879] than when studied interannually (τ = 0.476). Principal component analysis (PCA) demonstrated that spatial patterns of the NDVI fluctuations differed when studied interannually or during the seasonal course. Field features such as topography or vegetation composition influenced seasonal NDVI values whereas only rainfall distribution played a role at the interannual time scale. Our results show that rainfall controls on primary production and their mitigation differ between time scales, and these findings bring insights on the future response of savannas to climate change.     

Observations of volcanic ash clouds in the 10-12 μm window using AVHRR/2 data

Abstract

Volcanic aerosols are always present in the atmosphere, but because of the nature of volcanic activity their abundance varies greatly with time. The problem of detecting and monitoring volcanic ash clouds using radiance measurements from the AVHRR/2 (Advanced Very High Resolution Radiometer) instrument is discussed and some results are presented for the Galunggung eruptions of July 1982. It is shown that during the first few hours of an explosive eruption AVHRR/2 thermal channel measurements can be used to detect and discriminate volcanic clouds. Once the eruption cloud has spread and thinned out however, the problem of detection is difficult because of the similarity between dispersed volcanic cloud and semi-transparent cirrus. In these cases, if the volcanic cloud consists of liquid H₂SO₄ droplets, then it is possible to discriminate them from water/ice clouds because of the reverse absorption effect in channel-4 and channel-5. Some evidence is presented showing this effect. It is proposed that the temperature difference image be used operationally to warn of the presence of volcanic clouds.     

Evaluation and improvement of the MODIS land surface temperature/emissivity products using ground‐based measurements at a semi‐desert site on the western Tibetan Plateau

Current MODerate‐resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST, surface skin temperature)/emissivity products are evaluated and improvements are investigated. The ground‐based measurements of LST at Gaize (32.30° N, 84.06° E, 4420 m) on the western Tibetan Plateau from January 2001 to December 2002 agree well (mean and standard deviation of differences of 0.27 K and 0.84 K) with the 1‐km Version 004 (V4) Terra MODIS LST product (MOD11A1) generated by the split‐window algorithm. Spectral emissivities measured from surface soil samples collected at and around the Gaize site are in close agreement with the landcover‐based emissivities in bands 31 and 32 used by the split‐window algorithm. The LSTs in the V4 MODIS LST/emissivity products (MYD11B1 for Aqua and MOD11B1 for Terra) from the day/night LST algorithm are higher by 1–1.7 K (standard deviation around 0.6 K) in comparisons to the 5‐km grid aggregated values of the LSTs in the 1‐km products, which is consistent with the results of a comparison of emissivities. On average, the emissivity in MYD11B1 (MOD11B1) is 0.0107 (0.0167) less than the ground‐based measurements, which is equivalent to a 0.64 K (1.25 K) overestimation of LST around the average value of 285 K. Knowledge obtained from the evaluation of MODIS LST/emissivity retrievals provides useful information for the improvement of the MODIS LST day/night algorithm. Improved performance of the refined (V5) day/night algorithm was demonstrated with the Terra MODIS data in May–June 2004.     

Terra and Aqua MODIS inter‐comparison of three reflective solar bands using AVHRR onboard the NOAA‐KLM satellites

Cross‐sensor inter‐comparison is important to assess calibration quality and consistency and ensure continuity of observational datasets. This study conducts an inter‐comparison of Terra and Aqua MODIS (the MODerate Resolution Imaging Spectroradiometer) to examine the overall calibration consistency of the reflective solar bands. Observations obtained from AVHRR (the Advanced Very High Resolution Radiometer) onboard the NOAA‐KLM series of satellites are used as a transfer radiometer to examine three MODIS bands at 0.65 (visible), 0.85 (near‐IR) and 1.64 µm (far near‐IR) that match spectrally with AVHRR channels. Coincident events are sampled at a frequency of about once per month with each containing at least 3000 pixel‐by‐pixel matched data points. Multiple AVHRR sensors on‐board NOAA‐15 to 18 satellites are used to check the repeatability of the Terra/Aqua MODIS inter‐comparison results. The same approach applied in previous studies is used with defined criteria to generate coincident and co‐located near nadir MODIS and AVHRR pixel pairs matched in footprint. Terra and Aqua MODIS to AVHRR reflectance ratios are derived from matched pixel pairs with the same AVHRR used as a transfer radiometer. The ratio differences between Terra and Aqua MODIS/AVHRR give an indication of the calibration biases between the two MODIS instruments. Effects due to pixel footprint mismatch, band spectral differences and surface and atmospheric bi‐directional reflectance distributions (BRDFs) are discussed. Trending results from 2002 to 2006 show that Terra and Aqua MODIS reflectances agree with each other within 2% for the three reflective solar bands.     

Evaluation of the B‐method for determining actual evapotranspiration in a boreal forest from MODIS data

Boreal forests occupy about 11% of the terrestrial surface and represent an important contribution to global energy balance. The ground measurement of daily evapotranspiration (LE_d) is very difficult due to the limitations on experiments. The objective of this paper is to present and explore the applicability of the B‐method for monitoring actual LE_d in these ecosystems. The method shown in this paper allows us to determine the surface fluxes over boreal forests on a daily basis from instantaneous information registered in a conventional meteorological tower, as well as the canopy temperature (T _c) retrieved by satellite. Images collected by the MODIS (moderate resolution imaging spectroradiometer) on board EOS‐Terra have been used for this study. The parameters of the model were calibrated from the SIFLEX‐2002 (Solar Induced Fluorescence Experiment 2002) campaign dataset in a northern boreal forest in Finland. A study of these parameters was made on an hourly basis in order to make the method applicable, not only at midday but within an interval of 7 h around it. This is an important advance with respect to the original formulation of this approach since the overpass time of satellites can be very variable. The comparison between T _c ground measured with a thermal infrared radiometer, and T _c retrieved from land surface temperature (LST) MODIS data, showed an estimation error of ±1.4°C for viewing angles from 5 to 60°. A complete sensitivity analysis was carried out and an estimation error of about ±35%, corresponding to the interval 10.00–11.00 h UTC, was shown as the lowest in LE_d retrieval. Finally, the method was validated over the study site using 21 MODIS images for 2002 and 2003. The results were compared with eddy‐correlation ground measurements. An accuracy of ±1.0 mm/day and an overestimation of 0.3 mm/day were shown in the LE_d retrieval.     

River bed identification for check‐dam engineering using SPOT‐5 image in the HongShiMao watershed of the Loess Plateau,China

The Loess Plateau located in the upper and middle reaches of the Yellow River is the most serious soil erosion region in China. Check‐dams (also known as silt trappers) have been demonstrated to be an effective soil and water conservation measure in the region and a traditional workflow for check‐dam planning and engineering is time‐consuming and cannot meet the requirement of efficiently locating optimal check‐dam sites. Fine resolution satellite imagery and analysis can play a key role in screening and determining special river bed segments that can be candidates for check‐dam sites. In this research, HongShiMao watershed of the Loess Plateau was selected as our case study area. Based on a detailed analysis of spectral characteristics of a fused SPOT‐5 imagery for dominant land covers and geomorphological features of a constructed digital elevation model, the river bed of key channels within the watershed was automatically identified. Then we selected four check‐dam sites on the river bed and four orientations of a check‐dam site to explore locational and directional profiles. Such profile information is most useful for locating optimal check‐dam sites in a cost‐effective manner and reducing associated expenditures surrounding check‐dam constructions. This remote sensing application demonstrates the latest spatial information technologies such as fine resolution satellite imagery and 3D geospatial visualisation hold promises for changing traditional workflows and advancing scientific decision making of environmental conservation projects.