NDVI Threshold Method Research Articles

Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods

Land Surface Temperature (LST) measurements using remote sensing data are conducted using various methods, including the Single Channel Method (SCM) and the Multi-Channel Method (MCM). Emissivity, which significantly affects LST, is determined using methods such as the Correction-Based Emissivity Method (CBEM) and the Non-Linear Based Emissivity Method (NBEM). Given these factors, validating LST measurements is crucial for assessing the accuracy of data processing results. This study aims to compare LST data retrieved using SCM and MCM with various emissivity calculation methods and assess their accuracy compared to single measurements with an infrared thermometer, measurements using a grid plot, and measurements using a thermal camera with the assistance of an Unmanned Aerial Vehicle (UAV). The results show that SCMJM&S and SCMAC did not produce optimal accuracy (>2.5K) when using the emissivity estimates from CBEM and NBEM. In contrast, MCM that adopted MCMSko, MCMQin, and MCMMao generally produced high-accuracy LST with an average difference of <1 K from actual temperatures, specifically in shrub pixels. MCM with the NDVI threshold method (NDVITHM) showed excellent results in distinguishing between vegetation and non-vegetation objects. In conclusion, MCM provides more accurate LST than SCM. Nevertheless, selecting the appropriate method should consider heterogeneity, homogeneity, and regional physiography.

Changes in Onset of Vegetation Growth on Svalbard, 2000-2020.

The global temperature is increasing, and this is affecting the vegetation phenology in many parts of the world. The most prominent changes occur at northern latitudes such as our study area, which is Svalbard, located between 76°30'N and 80°50'N. A cloud-free time series of MODIS-NDVI data was processed. The dataset was interpolated to daily data during the 2000-2020 period with a 231.65 m pixel resolution. The onset of vegetation growth was mapped with a NDVI threshold method which corresponds well with a recent Sentinel-2 NDVI-based mapping of the onset of vegetation growth, which was in turn validated by a network of in-situ phenological data from time lapse cameras. The results show that the years 2000 and 2008 were extreme in terms of the late onset of vegetation growth. The year 2020 had the earliest onset of vegetation growth on Svalbard during the 21-year study. Each year since 2013 had an earlier or equally early timing in terms of the onset of the growth season compared with the 2000-2020 average. A linear trend of 0.57 days per year resulted in an earlier onset of growth of 12 days on average for the entire archipelago of Svalbard in 2020 compared to 2000.

Quantification of Alpine Grassland Fractional Vegetation Cover Retrieval Uncertainty Based on Multiscale Remote Sensing Data

Fractional vegetation cover (FVC) retrieval results of high spatial resolution satellite remote sensing images are usually upscaled as training and validation data (FVC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UIH</sub> ) for low spatial resolution satellite remote sensing images. However, few studies have focused on the impact of the spatial scale conversion on the evaluation of FVC retrieval accuracy. In this study, we first investigated the influence of spatial scale conversion on FVC retrieval accuracy based on FVC measured by unmanned aerial vehicle (FVC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UAV</sub> ) at three scales (Sentinel-2 MSI, Landsat-8 OLI, and MODIS). Then, the NDVI threshold method is proposed to further analyze the uncertainty caused by the underlying surface heterogeneity. The results showed that the use of FVC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UIH</sub> as training and validation data in the process of spatial scale conversion led to overestimation of FVC accuracy, and its influence on FVC retrieval cannot be ignored. In addition, the uncertainty of the underlying surface heterogeneity at the measured sites increased the uncertainty of the FVC retrieval, while these results could be optimized by detecting the underlying surface heterogeneity. Our results suggested that both spatial scale conversion and underlying surface heterogeneity would cause the inaccurate FVC retrieval, while the latter could be optimized by detecting the underlying surface heterogeneity. This study provided a reference for the improvement of multiscale FVC retrieval accuracy based on single-scale FVC-measured data.

Time-Series of Cloud-Free Sentinel-2 NDVI Data Used in Mapping the Onset of Growth of Central Spitsbergen, Svalbard

The Arctic is a region that is expected to experience a high increase in temperature. Changes in the timing of phenological phases, such as the onset of growth (as observed by remote sensing), is a sensitive bio-indicator of climate change. In this paper, the study area was the central part of Spitsbergen, Svalbard, located between 77.28°N and 78.44°N. The goals of this study were: (1) to prepare, analyze and present a cloud-free time-series of daily Sentinel-2 NDVI datasets for the 2016 to 2019 seasons, and (2) to demonstrate the use of the dataset in mapping the onset of growth. Due to a short and intense period with greening-up and frequent cloud cover, all the cloud-free Sentinel-2 data were used. The onset of growth was then mapped by a NDVI threshold method, which showed significant correlation (r2 = 0.47, n = 38, p &lt; 0.0001) with ground-based phenocam observation of the onset of growth in seven vegetation types. However, large bias was found between the Sentinel-2 NDVI-based mapped onset of growth and the phenocam-based onset of growth in a moss tundra, which indicates that the data in these vegetation types must be interpreted with care. In 2018, the onset of growth was about 10 days earlier compared to 2017.

Study on inversion of coal seam temperature in mining area --Pingshuo mining area of Shanxi Province

Landsat 8 is widely used in the extraction of surface temperature, but the data of surface temperature and abnormal area in Pingshuo mining area is vacant based on Landsat 8 in recent years, and there is no standard optimal algorithm to follow. In order to explore the possibility of underground coal fire in Pingshuo mining area of Shanxi Province in the future, based on the Landsat 8 satellite data, the temperature inversion method is used to observe the temperature distribution of the mining area, and three commonly used algorithms of temperature inversion processing are used to compare and analyze the SC algorithm as the best data processing method. The artificial threshold method and NDVI threshold method are used to extract the temperature anomaly area and vegetation coverage area, and calculate the area and proportion of coal fire potential area. According to a series of the data and result charts analysis, it shows that: the highest vegetation index of Pingshuo mining area is 0.79, the vegetation coverage is low, and the surface temperature is more than 41.44 ℃, which may lead to the spontaneous combustion of underground coal mines. However, the area prone to underground coal fires is small and controllable. According to the area of potential coal fires in the mining area, the local relevant departments can take relevant measures to prevent coal fire through the distribution map of potential coal fires.

Sugarcane canopy detection using high spatial resolution UAS images and digital surface model

The use of an unmanned aerial system (UAS) equipped with multispectral cameras is a potential approach to acquire canopy reflectance to make various correlations with desired crop parameters. However, the acquired reflectance data are mixed with unwanted data, such as reflectance from soil, which significantly affects some commonly used vegetation indices, such as the NDVI. This study compares the performance of three methods for detecting the canopy area of 3-month-old sugarcane crops. These methods extract the canopy areas using 5 NDVI thresholds (0.2, 0.3, 0.4, 0.5, and 0.6), a principal component analysis (PCA) threshold, and a digital surface model (DSM) threshold. The performance assessment will deliberately consider the quality percentage (QP) of each method to correctly detect the canopy area of short sugarcane crops in 10 selected images. The results show that filtration by the PCA threshold method provides the best result with a QP of 65.89-78.72%. The NDVI threshold method at levels of 0.3 and 0.4 follow with QPs of 58.42-68.81% and 40.80-70.81%, respectively, and the lowest accuracy is obtained by the DSM threshold method, which has QPs of 14.80-30.78%.

Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method

Predicting land surface energy budgets requires precise information of land surface emissivity (LSE) and land surface temperature (LST). LST is one of the essential climate variables as well as an important parameter in the physics of land surface processes at local and global scales, while LSE is an indicator of the material composition. Despite the fact that there are numerous publications on methods and algorithms for computing LST and LSE using remotely sensed data, accurate prediction of these variables is still a challenging task. Among the existing approaches for calculating LSE and LST, particular attention has been paid to the normalised difference vegetation index threshold method (NDVITHM), especially for agriculture and forest ecosystems. To apply NDVITHM, knowledge of the proportion of vegetation cover (PV) is essential. The objective of this study is to investigate the effect of the prediction accuracy of the PV on the estimation of LSE and LST when using NDVITHM. In August 2015, a field campaign was carried out in mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany, coinciding with a Landsat-8 overpass. The PV was measured in the field for 37 plots. Four different vegetation indices, as well as artificial neural network approaches, were used to estimate PV and to compute LSE and LST. The results showed that the prediction accuracy of PV improved using an artificial neural network (R2CV = 0.64, RMSECV = 0.05) over classic vegetation indices (R2CV = 0.42, RMSECV = 0.06). The results of this study also revealed that variation in the accuracy of the estimated PV affected calculation results of the LSE. In addition, our findings revealed that, though LST depends on LSE, other parameters should also be taken into account when predicting LST, as more accurate LSE results did not increase the prediction accuracy of LST.

A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data

Land surface temperature (LST) retrieval from satellite imagery is one of the most practical ways to consistently monitor urban thermal environment. Given the heterogeneous nature of urban landscape, an implicit assumption should be considered in remotely sensed LST determinations that a mixed urban land cover aggregation is the combination of its constituent components. Currently, the common LST retrieval method which utilize emissivity measures estimated by NDVI threshold method (NDVITHM), including mono window (MW), single channel (SC), and split window algorithms (SW), does not take into account heterogeneity of pixels. While in this study, a new approach, the mixture analysis of emissivity (MAoE), is proposed to calculate temperature by estimating pixel emissivity from mixed land cover classes. We conduct a comparison of six approaches by the combinations of three LST retrieval algorithms with NDVITHM and MAoE respectively. The differences among strategies are characterized and analyzed by comparing LST estimates from Landsat 8 thermal images. The LST gradients derived from transect analysis are found consistently similar for combinations of two LST algorithms (MW and SC) and the two emissivity estimation algorithms (MAoE and NDVITHM). LSTs derived from SW algorithms using band 10 have the highest mean values, while the SC algorithms have moderate mean values and the MW algorithms have the lowest values. Standard deviations of estimated LST from MAoE are smaller compared with NDVITHM methods, SC retrieval algorithm with MAoE has the smallest standard deviation, and NDVITHM temperature estimation could be more impacted by different land use land cover types.

Thermal Activity Monitoring of an Active Volcano Using Landsat 8/OLI-TIRS Sensor Images: A Case Study at the Aso Volcanic Area in Southwest Japan

Thermal remote sensing is currently an emerging technique for monitoring active volcanoes around the world. The study area, the Aso volcano, is currently the most active and has erupted almost every year since 2012. For the first time, Landsat 8 TIRS thermal data were used in this study area to evaluate and monitor the recent thermal status of this volcano, situated in Southwest Japan, from 2013 to 2016 using four sets of images. The total heat discharged rate (HDR), radiative heat flux (RHF), land surface temperature (LST), and land cover (LC) were evaluated, and the relationship between them was determined, to understand the thermal status of the study area. We used the NDVI (normalized difference vegetation index) for land cover, the NDVI-threshold method for emissivity, the split-window algorithm for LST, and the Stefan–Boltzmann equation for radiative heat flux estimation in this study. The total heat discharge rate was computed using a relationship coefficient of RHF and HDR here. The highest HDR was obtained in 2013, at about 4715 MW, and was the lowest in 2016, at about 3819 MW. The total heat loss showed a declining trend, overall, from 2013 to 2016. The highest pixel RHF was in 2013 and the lowest was in 2014; after that, it increased gradually until 2016, coinciding with the LST of this study area. LC showed that, with decreasing heat loss, the vegetated coverage increased and bare land or mixed land decreased, and vice versa. From the spatial distribution of RHF, we saw that, within the Nakadake craters of the Aso volcano, Crater 1 was the most active part of this volcano throughout the study period, and Crater 3 was the most active after 2014. We inferred that the applied methods using the continuous Landsat 8 TIRS data showed an effective and efficient method of monitoring the thermal status of this active volcano.

Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs

The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVITHM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is significantly larger in the derived LST products than the corresponding radiant temperature images.

Spatial and Temporal Variability in the Onset of the Growing Season on Svalbard, Arctic Norway — Measured by MODIS-NDVI Satellite Data

The Arctic is among the regions with the most rapid changes in climate and has the expected highest increase in temperature. Changes in the timing of phenological phases, such as onset of the growing season observed from remote sensing, are among the most sensitive bio-indicators of climate change. The study area here is the High Arctic archipelago of Svalbard, located between 76°30ʹ and 80°50ʹN. The goal of this study was to use MODIS Terra data (the MOD09Q1 and MOD09A1 surface reflectance products, both with 8-day temporal composites) to map the onset of the growing season on Svalbard for the 2000–2013 period interpreted from field observations. Due to a short and intense period with greening-up and frequent cloud cover, all the cloud free data is needed, which requires reliable cloud masks. We used a combination of three cloud removing methods (State QA values, own algorithms, and manual removal). This worked well, but is time-consuming as it requires manual interpretation of cloud cover. The onset of the growing season was then mapped by a NDVI threshold method, which showed high correlation (r2 = 0.60, n = 25, p &lt; 0.001) with field observations of flowering of Salix polaris (polar willow). However, large bias was found between NDVI-based mapped onset and field observations in bryophyte-dominated areas, which indicates that the results in these parts must be interpreted with care. On average for the 14-year period, the onset of the growing season occurs after July 1st in 68.4% of the vegetated areas of Svalbard. The mapping revealed large variability between years. The years 2000 and 2008 were extreme in terms of late onset of the growing season, and 2002 and 2013 had early onset. Overall, no clear trend in onset of the growing season for the 2000–2013 period was found.

Assessment of spatial relationship between land surface temperature and landuse/cover retrieval from multi-temporal remote sensing data in South Karkheh Sub-basin, Iran

Land surface temperature (LST) is a required input data for modeling in climatological, hydrological, agricultural, and change detection studies. In the current study, remotely sensed thermal infrared data were applied to assess LST in south Karkheh sub-basin, Iran. This research deals with the extraction of LST and land surface emissivity (LSE) and the relationship between vegetation abundance using Landsat 5 TM and Landsat 7 ETM+ for October 1998 and 2002 over the studied area. The landuse/land cover (LULC) map was derived by using maximum likelihood classifier. The variety of LSE was investigated and extracted on the basis of NDVI threshold method, as well as the Plank equation was used to derive LST over the study region. The relationship between LST and different LULC is determined by zonal GIS and regression analysis. The results illustrate that the emissivity lies in the range of 0.860–0.992 and 0.855–0.992 in 1998 and 2002, respectively. The maximum value of LSE equal to ∼0.99 was observed over the dense vegetation, while the minimum LSE value equal to ∼0.880 was found over the sand dune. LST and NDVI analysis exhibits a strong inverse correlation except over water bodies. The strongest coefficient correlation was found over wetland (−0.76 and R2 = 0.78).

VSDI: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing

In this article, a new index, the visible and shortwave infrared drought index (VSDI), is proposed for monitoring both soil and vegetation moisture using optical spectral bands. VSDI is defined as , where ρ represents the reflectance of shortwave infrared (SWIR) red and blue channels, respectively. VSDI is theoretically based on the difference between moisture-sensitive bands (SWIR and red) and moisture reference band (blue), and is expected to be efficient for agricultural drought monitoring over different land-cover types during the plant-growing season. The fractional water index (FWI) derived from 49 Mesonet stations over nine climate divisions (CDs) across Oklahoma are used as ground truth data and VSDI is compared with three other drought indices. The results show that VSDI generally presents the highest correlation with FWI among the four indices, either for whole sites or for individual CDs. The NDVI threshold method is applied to demonstrate the satisfactory performance of VSDI over different land-cover types. A time-lag analysis is also conducted and suggests that VSDI can be used as a real-time drought indicator with a time lag of less than 8 days. The VSDI drought maps are produced and compared with the US Drought Monitor (USDM) maps. A good agreement has been observed between the two products, and finer spatial information is also found in VSDI. In conclusion, VSDI appears to be a real-time drought indicator that is applicable over different land-cover types and is suitable for drought monitoring through the plant-growing season.

Land surface emissivity retrieval from airborne sensor over urban areas

In this paper we compare three different methodologies to retrieve land surface emissivity (LSE) over urban areas: the NDVI thresholds method, the temperature and emissivity separation (TES) algorithm and the temperature independent spectral indices (TISI) algorithm. The methodologies were applied to the Airborne Hyperspectral Scanner (AHS) imagery acquired during the Dual-use European Security IR Experiment 2008 (DESIREX 2008) experimental campaign over the city of Madrid (Spain). The images have a spatial resolution of 4m. The retrieved values are compared to in situ data measured during the campaign at 4 sites. Results show a good performance of the TISI and the TES algorithms over urban surfaces, while the NDVI threshold method does not appear to be capable to distinguish between different artificial surfaces. The land surface temperature (LST) was retrieved with a split window algorithm, using the three different LSE products. Differences of up to 3K were realized in some surfaces when different LSE maps were used. Over artificial urban surfaces TES and TISI showed better agreement with in-situ data than NDVI. Finally, the TES is the algorithm that best reproduces the LST over an urban area, without the requirement of high temporal resolution of the sensor.

Technology of Wetland Information Extraction in Farming-pastoral Areas: A Case Study of Dacangxiang in Duolun County

In the farming-pastoral area,the land types are complex and the mixed pixel phenomenon is serious,so the automatic extraction of wetland information is difficult.In view of the characteristics and difficulties of wetland remote sensing information extraction in these regions,the authors selected the TM remote sensing data covering Dacangxiang area in Duolun County to study a new method of wetland automatic extraction.Firstly,the NDVI threshold method was used to extract water body and then water types were extracted using water body shape features such as area(s),perimeter(p) and shape index(k).Afterwards,the linear spectral mixture model(LSMM)was chosen to extract wetland information after water removal.Finally,the meadow component which was decomposed by LSMM was amended by SPOT-5 satellite image to further extract high coverage,medium coverage and low coverage meadow.The results show that the overall accuracy of the wetland information extraction is 85.3%,and the Kappa coefficient is 0.79.Compared with the results of the traditional maximum likelihood method,the overall accuracy is improved by 19.9% and the Kappa coefficient is improved by 0.22.This method can provide a reference of remote sensing information extraction of wetland for other regions with mixed spectral characteristics.

MODIS-NDVI-based mapping of the length of the growing season in northern Fennoscandia

Northern Fennoscandia is an ecologically heterogeneous region in the arctic/alpine-boreal transition area. Phenology data on birch from 13 stations and 16-day MODIS-NDVI composite satellite data with 250m resolution for the period 2000 to 2006 were used to map the growing season. A new combined pixel-specific NDVI threshold and decision rule-based mapping method was developed to determine the onset and end of the growing season. A moderately high correlation was found between NDVI data and birch phenology data. The earliest onset of the growing season is found in the narrow strip of lowland between the mountains and the sea along the coast of northern Norway. The onset follows a clear gradient from lowland to mountain corresponding to the decreasing temperature gradient. In autumn, the yellowing of the vegetation shows a more heterogeneous pattern. The length of the growing season is between 100 and 130 days in 55% of the study area.