Normalized Multi-band Drought Index Research Articles

A comprehensive investigation on the effect of UHI in Baripada city using geo‐spatial technique and MLTHP model

AbstractAccording to the Times of India, Baripada city recorded the highest temperature in the world on April 14, 2023, at 43.5°C. It is located in the south‐west direction of the Similipal Biosphere Reserve. Observing the third highest temperature in the world, we are interested in finding out the reasons affecting climate change. This paper investigates the reasons for the temperature change in Baripada city and its surroundings by considering factors like Normalized Difference Vegetation Index (NDVI), Normalized Multi‐band Drought Index (NMDI), Normalized Difference Water Index (NDWI), Normalized Difference Built‐up Index (NDBI), forest fires, rainfall patterns, wind patterns, and registration of motor vehicles. For this study, data has been collected from USGS (United States Geological Survey), NASA POWER, Forest Survey of India, ArcGIS Online, etc. The study of NDVI shows a significant relationship with the LST. Similarly, NMDI shows an abnormal change in the drought index from 0.092 in 1990 to 0.276 in 2022, and NDWI shows a continuous reduction of the water index of −0.382 in 1990 and −0.176 in 2010, followed by −0.212 in 2022. NDBI values show that the urban build‐up index is increased to an extent due to rapid urban settlements removing natural vegetation. The overall forest fire count found an abnormal rise in the Baripada region during 2011 and 2022 to 143 times. Focusing on all the above factors, an experimental study is finally carried out to confirm the rise in temperature using time‐series analysis and a multi‐layer locally tuned hidden layer perceptron (MLTHP) model to ensure the Times of India's report.

Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time‐series analysis based on Sentinel‐1 and Sentinel‐2

AbstractAssessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch‐network in managed broad‐leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time‐series from high spatial resolution imagery of Sentinel‐1 (Synthetic‐Aperture Radar, SAR) and Sentinel‐2 (multispectral) are analyzed in time‐series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel‐1 (n = 84) and Sentinel‐2 (n = 903) are calculated at patch‐level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel‐1 VH and Sentinel‐2 NMDI (Normalized Multi‐band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time‐series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time.

Meteorological drought assessment in northern Bangladesh: A machine learning-based approach considering remote sensing indices

Meteorological drought, driven by inadequate precipitation, has significant repercussions for water resources, agriculture, and human well-being. This study conducted an extensive assessment of meteorological drought in northern Bangladesh, employing remote sensing indices and machine learning techniques. The main aim was to evaluate meteorological drought occurrences in northern Bangladesh from 2010 to 2019, utilizing seven drought parameters and a machine learning model. Utilizing a Random Forest (RF) model, this study employed the Standardized Precipitation Index (SPI) as the dependent variable and seven remote sensing indices as independent variables. Through this methodology, the study assessed the significance of these indices generated by the model and integrated them, culminating in the creation of a meteorological drought distribution map spanning 2010 to 2019. This approach offers novel insights by probing the interplay and collective impacts of these indices, shedding light on previously unexplored aspects of regional drought patterns of northern Bangladesh. The major findings showed that precipitation strongly influenced both short-term and long-term meteorological drought episodes. Moreover, land surface-related indices, such as Evapotranspiration (ET) and Normalized Difference Water Index (NDWI), exhibited a more pronounced impact on short-term drought occurrences, while vegetation-related indices like Normalized Multi-band Drought Index (NMDI) and Normalized Difference Vegetation Index (NDVI) demonstrated greater influence over long-term drought events. During this timeframe, the Rajshahi division experienced frequent extreme and severe drought events. Moderate droughts and abnormally dry conditions were widespread. The Barind tract area consistently faced moderate to extreme droughts, with exceptions in 2011, 2014, and 2019. On average, over 5% of the region had extreme droughts, while more than 12% experienced severe droughts during this decade. Long-term drought indicators (SPI 6 and SPI 9) consistently showed higher frequencies of extreme and severe droughts compared to short-term indicators (SPI 1 and SPI 3), emphasizing the influence of prolonged rainfall deficits on extreme droughts and the relevance of longer time frames for severe drought dynamics. The RF model demonstrated strong performance with accuracy ranging from 81% to 95%. Low prediction errors (RMSE 6% to 31%) and high out-of-bag (OOB) accuracy ranging from 76% to 98% highlighted its accuracy. The F1 score consistently exceeded 76%, indicating high precision and recall. Cross-validation values ranged from 78% to 94%, affirming reliable generalization to new data. Incorporating the main findings, this study contributes valuable insights for the formulation of targeted drought mitigation strategies in northern Bangladesh. It is imperative to note that the scope of this study is confined to the northern region of Bangladesh, and generalizing these findings to other regions should be exercised with caution. Nevertheless, the research methodology and approach can serve as a model for future studies in related fields, advancing knowledge of how to assess droughts using remote sensing and machine learning methods.

Monitoring Spatiotemporal Vegetation Response to Drought Using Remote Sensing Data.

Environmental factors such as drought significantly influence vegetation growth, coverage, and ecosystem functions. Hence, monitoring the spatiotemporal vegetation responses to drought in a high temporal and adequate spatial resolution is essential, mainly at the local scale. This study was conducted to investigate the aspatial and spatial relationships between vegetation growth status and drought in the southeastern South Dakota, USA. For this purpose, Landsat 8 OLI images from the months of April through September for the years 2016-2021, with cloud cover of less than 10%, were acquired. After that, radiometric calibration and atmospheric correction were performed on all of the images. Some spectral indices were calculated using the Band Math toolbox in ENVI 5.3 (Environment for Visualizing Images v. 5.3). In the present study, the extracted spectral indices from Landsat 8 OLI images were the Normalized Difference Vegetation Index (NDVI) and the Normalized Multiband Drought Index (NMDI). The results showed that the NDVI values for the month of July in different years were at maximum value at mostly pixels. Based on the statistical criteria, the best regression models for explaining the relationship between NDVI and NMDISoil were polynomial order 2 for 2016 to 2019 and linear for 2021. The developed regression models accounted for 96.7, 95.7, 96.2, 88.4, and 32.2% of vegetation changes for 2016, 2017, 2018, 2019, and 2021, respectively. However, there was no defined trend between NDVI and NMDISoil observed in 2020. In addition, pixel-by-pixel analyses showed that drought significantly impacted vegetation coverage, and 69.6% of the pixels were negatively correlated with the NDVI. It was concluded that the Landsat satellite images have potential information for studying the relationships between vegetation growth status and drought, which is the primary step in site-specific management.

Remote Sensing Monitoring of Durum Wheat under No Tillage Practices by Means of Spectral Indices Interpretation: A Preliminary Study

Due to its advantages, remote sensing monitoring has been used in various applications and made noteworthy contributions to understanding soil and plant processes, as well as in the agriculture sector. The aim of the work is to compare the return of durum wheat crops in conservative agricultural practices in Mediterranean climate conditions by analysing the data from the Sentinel2 satellite through three spectral indices. The analysed spectral indices have different interpretations and therefore have been studied in different periods: (i) NDVI (normalized difference vegetation index) for the evaluation of the vegetative vigour from January to June; (ii) NDWI (normalized difference water index) for the moisture of covered soil from January to June and of bare soil after harvesting from June to August; and (iii) NMDI (normalized multi-band drought index) for the variability of bare soil moisture from June to August. With reference to the machines used in cultivation practices, a further purpose of the study is to investigate the effects of automatic guidance versus manual guidance on production yields and on the spectral indices considered. The first results show that the NDVI follows crop phenological stages by reaching the maximum values in correspondence with the stem elongation and booting stages. Additionally, the NDWI showed the same trend as the NDVI during the current crop. After harvesting, the NDWI showed higher values in the plots cultivated under conservation tillage practices. In the same period, the NMDI showed the same results as the NDWI and a positive correlation, confirming that tillage practices could imply a lower ability to retain water in drought time.

Future spatial modeling of vegetation in the Central Atlantic Forest Corridor, Brazil

Tropical forests stand out among forest domains, due to their wide diversity of flora and fauna. However, these areas concentrate a large number of the forest fires that occur annually on the planet. In this context, the present study performs spatial modeling of the normalized multi-band drought index (NMDI) in the Central Atlantic Forest Corridor during past (2001-2020) and future (2021-2040) periods to identify the relationship between drought and forest fires. Fire foci data, soil moisture, and data from the MOD09A1 product were used to obtain the NMDI. Rainfall and mean air temperature data from the past and for different future scenarios (SSP126 and SSP585) were also used. The autoregressive integrated moving average model (ARIMA) was used for modeling the NMDI. The results found indicate a recurrence of fire in the CAFC during the period of 2001–2020. Future data indicate reductions in rainfall and an increase in temperature in the CAFC area. The NMDI data indicate that the central region of the corridor is the driest and, among the priority areas for conservation, the Ilha do Lameirão Municipal ecological station has the lowest index values. Future modeling indicates the drought intensifying in the coming years in the corridor area. The CAFC is an extremely important area for the maintenance of Atlantic Forest remnants; however, this area continues to suffer disturbances and without the adoption of public policies, these disturbances could compromise the conservation of natural resources.

PENENTUAN PEMETAAN KADAR AIR TANAH OPTIMAL PADA LAHAN PERKEBUNAN KELAPA SAWIT (STUDI KASUS: KEBUN CIKASUNGKA, PT PERKEBUNAN NUSANTARA VIII, CIMULANG, BOGOR)

Soil water content is one of the dynamics of water conditions that can be a limiting factor in achieving optimal oil palm productivity. The extensive oil palm plantations make it difficult to acquire soil moisture content data. Soil water content conditions can be estimated by remote sensing technology through the vegetation and drought index, and also can be mapped using an interpolation approach. This study aimed to map the optimal soil water content in oil palm plantations based on Normalized Difference Vegetation Index (NDVI), Normalized Multi-band Drought Index (NMDI), and Interpolation. The results showed that the highest soil moisture was found on 0-3% slope gradient and the lowest on 8-15% slope gradient. Vegetation density based on NDVI analysis result was quite high, while the level of dryness based on NMDI analysis result was quite humid. Mapping of soil water content based on NDVI and NMDI had a very weak correlation with soil water content so both indices are less responsive in describing soil moisture conditions in the field. Interpolated water content value had a fairly strong relationship with the soil water content and had the highest suitability, so the interpolation approach can be recommended for mapping soil water content in an oil palm plantation area.

Soil Moisture Retrieval over a Vegetation-Covered Area Using ALOS-2 L-Band Synthetic Aperture Radar Data

Soil moisture (SM) plays a significant part in regional hydrological and meteorological systems throughout Earth. It is considered an indispensable state variable in earth science. The high sensitivity of microwave remote sensing to soil moisture, and its ability to function under all weather conditions at all hours of the day, has led to its wide application in SM retrieval. The aim of this study is to evaluate the ability of ALOS-2 data to estimate SM in areas with high vegetation coverage. Through the water cloud model (WCM), the article simulates the scene coupling between active microwave images and optical data. Subsequently, we use a genetic algorithm to optimize back propagation (GA-BP) neural network technology to retrieve SM. The vegetation descriptors of the WCM, derived from optical images, were the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized multi-band drought index (NMDI). In the vegetation-covered area, 240 field soil samples were collected simultaneously with the ALOS-2 SAR overpass. Soil samples at two depths (0–10 cm, 20–30 cm) were collected at each sampling site. The backscattering of the ALOS-2 with the copolarization was found to be more sensitive to SM than the crosspolarization. In addition, the sensitivity of the soil backscattering coefficient to SM at a depth of 0–10 cm was higher than at a depth of 20–30 cm. At a 0–10 cm depth, the best results were the mean square error (MAE) of 2.248 vol%, the root mean square error (RMSE) of 3.146 vol%, and the mean absolute percentage error (MAPE) of 0.056 vol%, when the vegetation is described as by the NDVI. At a 20–30 cm depth, the best results were an MAE of 2.333 vol%, an RMSE of 2.882 vol%, a MAPE of 0.067 vol%, with the NMDI as the vegetation description. The use of the GA-BP NNs method for SM inversion presented in this paper is novel. Moreover, the results revealed that ALOS-2 data is a valuable source for SM estimation, and ALOS-2 L-band data was sensitive to SM even under vegetation cover.

Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

NMDI application for monitoring different vegetation covers in the Atlantic Forest biome, Brazil

Climate change due to global warming has significant impacts on the Atlantic forest biome. Many forest fires are caused by extreme drought events, which present an uncertain future for their vegetation, and their associated risks are sensitive mainly at the local scale. In this context, the present study evaluated and correlated the normalized multi-band drought index (NMDI) with biophysical variables in the monthly period from 2001 to 2019 in 12 land cover classes. The Auto Regressive Integrated Moving Average (ARIMA) model was applied to the NMDI series and its ability to simulate data from the observed time series (2001–2019) and the future (2020–2030). The results showed a decrease in the NMDI values for the period considered dry in the State of Rio de Janeiro (SRJ), mainly for the classes of pasture and savannah, which presented greater sources of heat. The non-parametric analysis was performed using the Mann-Kendall test for all biophysical variables. The variables soil moisture and NMDI showed negative trends (Z = −1.68 and Z = −0.76), whereas gross primary productivity (GPP) showed a positive trend (Z = 1.89). The generated and validated ARIMA modeling simulated NMDI well and the Willmott coefficient (d) was approximately 1.0 for the study period. The 10-year projection (2020–2030) from NMDI for SRJ pointed to a change in class from wet to dry in the mixed forest area (D) and cultivated land (L). The ARIMA model can represent the drought index in the seasonality of the series for the different classes of vegetation. These results showed that the applicability of NMDI in predicting fire risk conditions would be adequate in other areas of tropical forests, standing out mainly for being a drought index that can be used in future modeling.

Fire danger assessment using geospatial modelling in Mekong delta, Vietnam: Effects on wetland resources

The Mekong Delta (MD), Vietnam is a large tropical wetland important for commodity provision, water balance regulation, and biodiversity conservation. However, the delta is under severe fire risk due to prolonged drought and rising temperatures largely emanating from climatic change. Our study applied MODIS Terra land surface reflectance (MOD09A1), MODIS Terra land surface temperature (MOD11A2), and ASTER digital elevation model (DEM) to develop fire danger models to assess spatial patterns in fire danger in the MD. Four variables: Potential Surface Temperature (PST), Modified Normalized Difference Fire Index (MNDFI), Perpendicular Moisture Index (PMI) and Normalized Multiband Drought Index (NMDI), were extracted from the satellite products to develop the models using Analytical Hierarchical Process (AHP) and weighted overlay methods to generate fire danger maps during the 2019 fire season. The models were validated using MODIS Terra and Aqua thermal anomaly product (MOD14), with overall accuracy of 84.13%. The result demonstrated that the model accurately estimates fire risk over the MD. The study also found that forests, especially Melaleuca wetland forests in U Minh wetlands, and agricultural land were under severe fire risk. The implication is that measures such as proper management of forests and agricultural activities for mitigating forest fires and integrated fire and water strategies for either fire risk mitigation or biodiversity conservation are necessary for sustainable management of the MD wetland.

Estimation of Surface Soil Moisture Based on Improved Multi-index Models and Surface Energy Balance System

Soil moisture is a vital resource that plays a critical role in arid and semi-arid areas. In the present study, a new approach was adopted to estimate surface soil moisture based on multi-index models using reflective and thermal indices as well as surface energy balance system–Iran (SEBS–Iran) in pastures and farmlands in Qom province, Iran in 2016–2017. To select the best model based on remote sensing (RS) indices, 12 models were designed and after analysis, the best ones were selected. Afterward, the results of the SEBS–Iran algorithm and the improved multi-index model [normalized multi-band drought index (NMDI), normalized difference vegetation index (NDVI), land surface temperature (LST) and the temperature vegetation dryness index (TVDI)] were calibrated with field data in the two studied fields (pastures and farmlands). The findings indicated that the multi-index model NMDI–TDVI–LST–NDVI (R = 0.95) and SEBS–Iran (R = 0.93) both had significant correlations with measured soil moisture. Regarding both models in farmlands and pastures, the SEBS–Iran regression model was closer to the line of fit, and R2 in the two fields was 0.95 and 0.96, respectively. Compared to SEBS–Iran, the multi-index model showed lower coefficient of determination in pastures (0.71) due to the higher accuracy of SEBS–Iran in areas with lower vegetation density. Generally, both methods were found to be suitable for soil moisture estimation. The multi-index model can be used to estimate soil moisture in densely vegetated areas on a large scale due to its simplicity and good accuracy. Moreover, the highly accurate SEBS–Iran model can be used even in sparsely vegetated areas.

Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images

AbstractThe Aral Sea was one of the largest lakes in the world, but almost 60,000 km2 of the waterbody has dried up due to water withdrawal for irrigation. Afforestation on the desiccated seafloor could be important in preventing soil flation, dust storms, and negative impact on human health. In this study, we aimed to delineate potential vegetation establishment areas on the dried Aral Sea bed using remote‐sensed data in support of the decision‐making related to afforestation. Various indices such as normalized difference vegetation index (NDVI), topsoil grain size index (TGSI), soil salinity index (SSI), and normalized multiband drought index (NMDI) were calculated from the LANDSAT‐8 OLI satellite imagery. As an indicator of vegetation existence, NDVI was classified into three groups and set as a base for classifying other indices by performing statistical analyses. Based on the decision tree method, indices were combined and the potential vegetation establishment area was detected. Higher NDVI was identified in the southeast than the west of the study area. The results of statistical analyses showed that TGSI had a positive correlation with NDVI, while SSI and NMDI had a negative correlation. Overall, the potential vegetation area comprised 7,295.21 km2 (61.34%) of the 'unsuitable' area, 2,818.64 km2 (23.7%) of the 'intermediate' area, 1,612.15 km2 (13.56%) of the 'suitable' area, and 166.42 km2 (1.4%) of the 'very suitable' area. The developed map enables to identify dried seafloor area suitable for vegetation establishment thus contributing to planning the land rehabilitation efforts and preventing further land degradation.

A global regression method for thermal sharpening of urban land surface temperatures from MODIS and Landsat

ABSTRACTLand surface temperatures (LST) in urban landscapes are typically more heterogeneous than can be monitored by the spatial resolution of satellite-based thermal infrared sensors. Thermal sharpening (TS) methods permit the disaggregation of LST based on finer-grained multispectral information, but there is continued debate over which spectral indices are most appropriate for urban TS, and how they should be configured in a predictive regression framework. In this study, we evaluate the stability of various TS kernels with respect to LST at different spatial (Landsat 8) and diurnal (MODIS) scales, and present a new TS method, global regression for urban thermal sharpening (SGRUTS), based on these findings. Of the spectral indices examined, the normalized difference built-up index (NDBI) and the normalized multi-band drought index (NMDI) were the most spatially stable for Landsat 8 and MODIS overall. Kernel performance varied diurnally, with the index-based impervious surface index (IBI) and broadband α selected for 1030 h, NDBI and NMDI selected for 1330 h, and IBI and NMDI selected for 2230 h and 130 h, respectively. Over a range of field-validated metrics, the SGRUTS scheme comprising a two-factor interaction between NDBI and NMDI was competitive with the best alternative TS models compared. This SGRUTS model is essentially a refinement of the Enhanced Physical Method for urban applications in terms of kernel selection and configuration, and has interpretative advantages over more complex statistical schemes.

Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha−1) to 4.8 (100 kg ha−1), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R2 = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model.

Assessing the role of SWIR band in detecting agricultural crop stress: a case study of Raichur district, Karnataka, India.

The potential consequence of global climatic change caused the rise in temperature and precipitation decline, the aftermath of which has led to phenomenon like drought. As the agriculture is mainly dependent on the timely availability of water, delayed arrival of rainfall or decrease in the intensity of precipitation highly affects the growth of crops. If such situation persists for a longer period, the soil moisture content will be exploited completely and maturity of the crop will be stunted, finally adversely affecting the annual crop yield. In the present study, the capability of shortwave-infrared (SWIR) channels in detecting the agricultural crop stress in Raichur district of Karnataka state, India, using spectral vegetation indices namely Global Vegetation Moisture Index (GVMI) and Normalized Multi-band Drought Index (NMDI) has been analyzed using multi-temporal MODIS data. The vegetation health analysis by utilizing both indices were carried out from year 2002 to 2012 for the Kharif season, and it was seen that the year 2002 suffered major agricultural drought where the lower GVMI and NMDI values were covering the majority of the crop areas and in the year 2010 agricultural crop production was observed to be good. The average values of GVMI and NMDI for the years 2002 and 2010 were plotted with the average NDVI values of all months of both years and the results revealed that NDVI values were in concurrence with both NMDI and GVMI.

Seasonal evaluation of downscaled land surface temperature: A case study in a humid tropical city

The present study evaluates the seasonal variation of estimated error in downscaled land surface temperatures (LST) over a heterogeneous urban land. Thermal sharpening (TsHARP) downscaling algorithm has been used with a separate combination of four selected remote sensing indices. This study assesses the capability of TsHARP technique over mixed land use/land covers (LULC) by analyzing the correlation between LST and remote sensing indices, namely, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized multi-band drought index (NMDI) and by determining the root mean square error (RMSE) and mean error (ME) produced by downscaled LST. Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images have been used for pre-monsoon, monsoon, post-monsoon, and winter seasons in 2014 covering the whole Raipur City, India. The RMSE of the downscaled LST decreases from 120 to 480 m spatial resolution in all the four seasons. It is concluded that NDBI is the most effective LULC index having the least error produced in TsHARP downscaling technique, irrespective of any season. Post-monsoon season reflects the most successful result followed by monsoon season. Even in the monsoon season of high vegetation coverage, NDBI presents a lower range of downscaled error compared to NDVI. This indicates better performance of NDBI in detecting the spatial and temporal distribution of mixed urban land.

Calculation of Salinity and Soil Moisture indices in south of Iraq - Using Satellite Image Data

A band rationing method is applied to calculate the salinity index (SI) and Normalized Multi-Band Drought Index (NMDI) as pre-processing to take Agriculture decision in these areas is presented. To separate the land from other features that exist in the scene, the classical classification method (Maximum likelihood classification) is used by classified the study area to multi classes (Healthy vegetation (HV), Grasslands (GL), Water (W), Urban (U), Bare Soil (BS)). A Landsat 8 satellite image of an area in the south of Iraq are used, where the land cover is classified according to indicator ranges for each (SI) and (NMDI).

Developing the Forest Fire Danger Index for the Country Kazakhstan by Using Geospatial Techniques

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Kazakhstan forests are more prone to fires due to the presence of coniferous forests and loss was enormous. There is a need of forest fire danger indices to estimate the potential fire danger so that fire officials effectively controls the fires. Global forest fire danger indices require daily meteorological stations data as well as ground investigation data. But, there are less number of meteorological stations are available in Kazakhstan, hence, the satellite derived parameters were used to develop the fire danger index in this study. In this study, Static forest fire probability index was developed by using the SRTM DEM and MODIS TERRA and AQUA Land cover type product (MCD12Q1). Dynamic forest fire probability index was calculated by using the MODIS TERRA Land Surface Temperature (MOD11A1) and Surface reflectance (MOD09GA). Dynamic forest fire probability index has been developed from the parameters, i.e. LST, Normalized Multi-band Drought Index (NMDI), Visible Atmospheric Resistant Index (VARI) and Modified Normalized Difference Fire Index (MNDFI). Finally, Fire danger index was developed by adding both the static and dynamic probability indices and Fire hotspot data (MCD14) has been used for the validation of the index. Accuracy was ranging from 77.78% to 90.32% and the overall accuracy was 84.14%. Developed Fire danger index was in operational, calculating by using MODIS Near Real Time datasets and uploading and updating every day in the website.

Mapping spatiotemporal dynamics of maize in China from 2005 to 2017 through designing leaf moisture based indicator from Normalized Multi-band Drought Index

Maize agriculture is experiencing substantial changes in the spatiotemporal pattern of planting areas in the most populous country-China. However, there is no spatially explicit and continuous information at national scale. Mapping maize at national scale is challenging due to intra-class variability of Vegetation Indices (VIs) temporal profile. This study coped with this challenge through combined utilizations of the EVI with two bands (EVI2) and Normalized Multi-band Drought Index (NMDI) time series datasets. A novel Maize mapping algorithm was proposed through Exploring Leaf moisture variation during flowering Stage (MELS). An indicator, the Ratio of Cumulative Positive slope to Negative slope (RCPN) during flowering stage, was developed based on NMDI and utilized as the unique metric for maize mapping. The capability of the MELS method was verified using the 8-day composite MODerate resolution Imaging Spectroradiometer (MODIS) datasets in China from 2005 to 2017. The derived maize map was consistent with the agricultural census data (r2 = 0.8875 in 2015) and 2020 ground truth observations (overall accuracy = 91.49%). Validation with Landsat-interpreted images in the test regions further confirmed its fairly good accuracy, with overall accuracy of 87.91% and kappa coefficient of 0.8577. We first generated annual maize maps from 2005 to 2017 in China. Maize planting areas increased continuously 100,130 km2 (by 33.20%) during the period 2005–2015 and decreased 10,424 km2 (by 2.60%) from 2015 to 2017. The increase of cropping intensity, replacement of paddy rice and other non-maize dryland crops areas accounted for 36.48%, 34.23% and 29.29% of the dramatic increased maize areas from 2005 to 2015, respectively.