Normalized Difference Moisture Index Research Articles

Improving Tree Cover Estimation for Sparse Trees Mixed with Herbaceous Vegetation in Drylands Using Texture Features of High-Resolution Imagery

Tree cover is a crucial vegetation structural parameter for simulating ecological, hydrological, and soil erosion processes on the Chinese Loess Plateau, especially after the implementation of the Grain for Green project in 1999. However, current tree cover products performed poorly across most of the Loess Plateau, which is characterized by grasslands with sparse trees. In this study, we first acquired high-accuracy samples of 0.5 m tree canopy and 30 m tree cover using a combination of unmanned aerial vehicle imagery and WorldView-2 (WV-2) imagery. The spectral and textural features derived from Landsat 8 and WV-2 were then used to estimate tree cover with a random forest model. Finally, the tree cover estimated using WV-2, Landsat 8, and their combination were compared, and the optimal tree cover estimates were also compared with current products and tree cover derived from canopy classification. The results show that (1) the normalized difference moisture index using Landsat 8 shortwave infrared and the standard deviation of correlation metric calculated by means of gray-level co-occurrence matrix using the WV-2 near-infrared band are the optimal spectral feature and textural feature for estimating tree cover, respectively. (2) The accuracy of tree cover estimated using only WV-2 is highest (RMSE = 7.44%), indicating that high-resolution textural features are more sensitive to tree cover than the Landsat spectral features (RMSE = 11.53%) on grasslands with sparse trees. (3) Textural features with a resolution higher than 8 m perform better than the combination of Landsat 8 and textural features, and the optimal resolution is 2 m (RMSE = 7.21%) for estimating tree cover, whereas the opposite is observed when the resolution of textural features is lower than 8 m. (4) The current global product seriously underestimates tree cover on the Loess Plateau, and the tree cover calculation using the canopy classification of high-resolution imagery performs worse than the method of directly using remote sensing features.

Mapping long-term Transformation of Wetlands and Annual Rainfall Variability in Madhubani District (1975-2022).

In addition to supporting a rich biodiversity, wetlands serve as ecosystems that connect aquatic and terrestrial settings. They also play a critical ecological role by providing essential supplies. According to the National Wetland Atlas (2010), Bihar has a total of 21,988 wetlands with a total area of 4,03,209 hectares. These wetlands exhibit great range of floral and faunal diversity with large number of endemic species. The present paper endeavors to map the natural wetlands of Madhubani district of North Bihar and assess their evolving hydro-physiographic characteristics. The study area is endowed with numerous small wetlands as it receives high average annual rainfall in Bihar. This study primarily emphasizes on identification of wetlands and their spatial-temporal assessment during the period 1975-2022 by utilizing datasets from various sources like topographical maps, satellite imageries, and other ancillary data. It uses geospatial tool like Arc Hydro and indices like Topographic Wetness Index (TWI) and Normalized Difference Wetness Index (NDWI) for identification and spatial-temporal assessment of wetlands during pre-monsoon and post-monsoon season. The study finds that despite being one of the rainiest districts of Bihar, the annual rainfall trends in Madhubani is decreasing. This has impacted upon the water quality, productivity, biodiversity and annual recharge of wetlands of Madhubani. Its entire geographic region has thus been significantly reduced as a result. Other important factors like accelerated anthropogenic intervention, land use changes and process of urbanization have contributed towards rapid transformation of floodplains of Madhubani District. These changes may affect future water potential of the region and thus threaten sustainability in the region.

Normalised difference moisture index in water stress assessment of maize crops

Remote sensing is a promising technique for better management of water resources in agriculture through improvement of dynamic control and operational scheduling on irrigated croplands. The main goal of this study was to identify the possibilities of application of the normalised difference moisture index (NDMI) to water stress monitoring in maize crops, and to determine the relationship between the index and soil moisture content. The study was carried out in 2019–2021 in the experimental fields of disturbed maize, cultivated on dark-chestnut soils in the Southern Ukraine at the NAAS Institute of Climate-Smart Agriculture. The crop cultivation technology was common for the conditions of the steppe zone of Ukraine. Actual soil moisture content was determined by gravimetric method in the pre-sowing and post-harvest period. The NDMI values were calculated using cloudless aerospace images from the satellites Landsat 8, Sentinel-2, and MODIS with 250 m resolution. It was revealed that the seasonal NDMI dynamics perfectly reflected the water-supply conditions of the disturbed maize, and could be used for operational monitoring and scheduling of irrigation. The parameters of the water-supply conditions were determined in 2021, which was the wettest year of the study: the cumulative seasonal NDMI reached 1.71, while the highest water stress was recorded in the driest year, 2020, – the cumulative NDMI was 0.15. Additionally, there was a moderately strong negative correlation between NDMI and soil moisture content, and the coefficient of determination was 0.62. The linear regression models, developed to predict soil moisture content in the 0–100 cm layer depending on the NDMI values, had good fitting quality and reasonable accuracy, but they required further calibration and extension of the initial dataset to provide more robust and reliable results for practical implementation. Based on the results of the study, spatial NDMI could be considered a good and reliable tool for improving irrigation water management. Further studies should focus on the practical implementation of the NDMI-based model of moisture-content estimation, as well as on the possibilities of the index usage for mapping irrigated lands.

ASSESSMENT OF FOREST DISTURBANCES USING REMOTE SENSING: CASE OF KALIWA RIVER FOREST RESERVE (KRFR), PHILIPPINES

Abstract. The Kaliwa River Forest Reserve (KRFR) is located in the southwestern part of the Sierra Madre Mountain Range, Philippines. KRFR is one of the most important forest reserves in the country that is adjacent to urban areas and is very much affected by various forest disturbances. Forest disturbances can be caused by either anthropogenic or natural phenomena (e.g., typhoons, drought, and pathogen outbreaks) and may have negative effects on the forest structure and function. The study used the Normalized Difference Moisture Index (NDMI) in detecting forest disturbances for the period of 2010, 2015, and 2020. Results showed that the deforestation rate has significantly decreased from 6.22% in 2010 to 3.40% in 2020. The National Greening Program (NGP) and intensified forest protection activities of the Department of Environment and Natural Resources (DENR) may have contributed to the observed decreasing trend in deforestation rate. However, forest disturbance in the closed and open forest areas of KRFR gradually increased from 0.11% to 0.40%. Based on the accuracy assessment, the NDMI performed well in determining forest disturbances with mean values of 0.91 and 0.93 for completeness and correctness, respectively. The main contributor to forest disturbances in KRFR was mainly anthropogenic factors such as kaingin, illegal logging, urban sprawl, and upland farming. Outputs of this study can aid in the formulation of policies for better forest resource management, rehabilitation, and protection in the protected area.

Pixel-Based Spatio-Statistical Analysis of Landslide Probability in Humid and Seismically Active Areas of Himalaya and Hindukush

The Hindukush and Himalaya regions of Pakistan are chronically prone to several geological hazards such as landslides. Studying landslides in these regions is crucial for risk assessment and disaster management, as well as for determining the effects of adverse climatic conditions, infrastructure management, and increasing anthropogenic activities. High-relief mountains in these regions face severe challenges because of frequently occurring landslides and other natural hazards, especially during intensive rainfall seasons and seismic activity, which destroy infrastructure and cause injuries and deaths. Landslides in the Alpuri Valley (Hindukush) and the Neelum Valley (Himalaya) have been activated through high magnitude earthquakes, intensive rainfalls, snowfall, floods, and man-made activities. Landslide susceptibility mapping in these areas is essential for sustainable development as it enables proactive risk management, up-to-date decision-making, and effective responses to landslide hazards, ultimately safeguarding human lives, property, and the environment. In this study, the relative effect method was applied for landslide susceptibility modeling in both study areas to determine the capability to reduce the effects of landslides, and to improve the prediction accuracy of the method. The relative effect is a statistical model that has only been used for very limited time for landslide susceptibility with effective results. A total of 368 (Neelum Valley) and 89 (Alpuri Valley) landslide locations were identified, which were utilized to prepare the reliable landslide inventory using GIS. In order to evaluate the areas at risk for future landslides activities and determine their spatial relationship with landslide occurrences, the landslide inventory was developed with 17 landslide causative factors. These factors include slope gradient, slope aspect, geology, plan curvature, general curvature, profile curvature, elevation, stream power index, drainage density, terrain roughness index, distance from the roads, distance from the streams, distance from fault lines, normalized difference wetness index, land-use/land-cover, rainfall, and normalized difference vegetation index. Finally, the performance of the relative effect method was validated using the success and prediction curve rate. The AUC-validated result of the success rate curve in the Alpuri Valley is 74.75%, and 82.15% in the Neelum Valley, whereas, the AUC-validated result of the prediction rate curve of the model is 87.87% in the Alpuri Valley and 82.73% in the Neelum Valley. These results indicate the reliability of the model to produce a landslide susceptibility map, and apply it to other landslide areas. The model demonstrated a more effective result in the Alpuri Valley, having a smaller area. However, the results are also desirable and favorable in Neelum Valley, with it being a large area. It will assist in general landslide hazard management and mitigation, and further research studies related to future landslide susceptibility assessments in other parts of the region.

Using multisource remotely sensed data and cloud computing approaches to map non-native species in the semi-arid savannah rangelands of Mpumalanga, South Africa

ABSTRACT Semi-arid savannah rangelands are diverse environments (in terms of species) that play an important role in sustaining biodiversity and providing ecosystem services. However, the emergence of non-native species, as well as bush encroachment, are currently threatening these (semi-arid rangeland and grassland) ecosystems. The purpose of this study was therefore to map and quantify the spatial extents of non-native woody vegetation in the Kruger National Park and surrounding communal areas in Mpumalanga, South Africa. To achieve the study’s objectives, Sentinel-1 and Sentinel-2 remotely sensed data were combined and analysed using the random forest (RF) machine-learning algorithm in the Google Earth Engine (GEE) cloud computing platform. Specifically, spectral bands and selected spectral derivatives, e.g. enhanced vegetation index (EVI2), normalized difference moisture index (NDMI) and normalized difference phenology index (NDPI) were computed and used to map non-native woody vegetation. After optimizing the model combination, the classification outputs had an overall accuracy of 70%, with class accuracies such as producer’s accuracy (PA) and user’s accuracy (UA) ranging from 67% to 95%.It was shown in this study that using Sentinel-2 and Sentinel-1 data together led to better overall accuracy than using single sensor models when mapping semi-arid savannah rangelands. It was also found in this study that the overall classification accuracy of non-native (invasive) species using optical sensors was higher than in previous studies. On a free platform like GEE, it was possible to utilize advanced classification processes to fully exploit the informative content of Sentinel-1 and Sentinel-2 data.

Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.

Long-term dynamics of remote sensing indicators to monitor the dynamism of ecosystems in arid and semi-arid areas: contributions to sustainable resource management

Abstract Drought is expected to increase in water bodies due to climate change. Monitoring long-term changes in wetlands is crucial for identifying fluctuations and conserving biodiversity. In this study, we assessed the long-term variability of remote sensing indicators in 25 watershed areas in Algeria known for their significant biodiversity. We employed two statistical methods, namely linear regression and the Mann–Kendall (MK) test, to capture long-term fluctuations by integrating data from various sources, including Modis and Landsat satellite data. A time-series dataset spanning 22 years was developed, consisting of the following indicators: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), normalized difference moisture index (NDMI), and land surface temperature (LST). We evaluated the relationships between these variables. The results indicated that NDVI exhibited a stronger temporal response compared to EVI, NDWI, and NDMI. Additionally, negative associations between NDVI and LST confirmed the impact of drought and plant stress on vegetation in the study areas (R2 = 0.109–R2 = 0.5701). The NDMI results pointed to water stress in the water bodies, showing a significant decreasing trend. The results from the MK trend analysis underscored the importance of NDVI and highlighted its strong association with EVI, NDWI, and NDMI. Understanding the dynamics of vegetation and water stress has become crucial for ecosystem forecasts.

A new braiding index to assess river regulation effects in multi‐thread channels: Insights from a highly regulated Himalayan river

AbstractRiver regulation by dams and embankments drastically reduce/alter flow, which affects the natural channel pattern. Existing braiding indices have seldom incorporated the effects of diurnal flow variations caused by hydropeaking, leading to over/underestimation of the braiding intensity. These indices consider only the visible wet channels, ignoring the existence of dry channels that are activated only episodically during phases of water release from hydropower dams. We have extracted the dry channels (those that are periodically wet) coursing across the channel belt of the highly regulated River Tista from Landsat images between 1977 and 2014, using Normalized Difference Wetness Index values. These were combined with existing wet channel widths and numbers to formulate the Regulated Braiding Index (RBi) for characterizing channel braiding in the Tista's course over the Himalayan foothill plains. Overall, the widths and numbers of wet channels decreased by 63% and 25%, respectively, during the regulated years (2003 and 2014) as compared to pre‐dam years (1977 and 1995) due to the collective operation of 14 upstream dams/barrages (having cumulative ~89 million m3 reservoir capacity), whose operations reduced the braiding intensity (eliciting lower RBi values). Further, the number of high braiding reaches decreased by half while low braiding stretches rose by 10% in comparison to the pre‐dam period due to reduced/regulated flow. Comparative analysis of derived RBi values with three existing braiding indices revealed that RBi values consistently decreased near barrages, bridges, and within embankment‐confined reaches, unlike the other indices, thus providing a better framework for assessing expected river regulation effects.

Delineation of Soil Management Zones and Validation through the Vigour of a Fodder Crop

In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties.

Use of Remote Sensing Data to Assess the Effects of the Floodplain Vegetation Burning in the Lower Dniester National Park (Odesa Region)

The aim of the study was to determine the burnt areas, response of the floodplain ecosystems to burning, and to determine the difference between them and the control sites using the remote sensing data. The impact of burning was assessed using the spectral indices: Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (GCI), Specific Leaf Area Vegetation Index (SLAVI), Normalized Difference Moisture Index (NDMI) and Moisture Stress Index (MSI). It was established that during the winter/spring period of 2020, fires in the territory of the Lower Dniester NNP covered almost 4453 hectares (33.1% of the floodplain ecosystems area). Simulation showed that the difference between the burned and control areas gradually decreased over the vegetation period. Maximal difference was observed at the beginning of the growing season (the quality of group selection was 43.2%), which indicates forming of relatively individual environmental conditions for the floodplain vegetation development. In spring, the difference was equal to 20.3%, due to development of <i>Phragmites australis</i>, the edifier of the plant complexes of wetland ecosystems, whose stems have developed a leaf blade. The summer period was characterized by further decrease in the group selection quality (19.9%), owing to equalization of environmental conditions for vegetation development. The autumn period was characterized by dry conditions and end of the growing season, which also contributes to the almost complete disappearance of differences between the plots (14.5%). Based on the analysis of similarity (ANOSIM), it was found that degree of difference between the burned plots was small, owing to local conditions and burning intensity, and amount of dry organic matter in certain areas. It was found that gradual decrease in differences during the vegetation season took place only at winter (February) burning. The influence of spring (March, April) burning resulted in significant variability of the similarity degree over the vegetation period.

ASSESSMENT OF FOREST VEGETATION POTENTIAL OF RECLAIMED AREAS AFTER ILMENITE MINING USING THE REMOTE EARTH SENSING METHOD

The mining of ilmenite has irreversible negative environmental impacts on the ecosystem of the area where mining companies operate. First of all, it leads to disturbance of the soil and vegetation layer, changes in the natural landscape, formation of depression sinkholes, which causes changes in water flow and water distribution in the mining area, lowering of groundwater levels, pollution of the atmosphere, soil and water bodies, and loss of species diversity of flora and fauna. In general, the mining process lasts for decades, during which time the territory is subject to irreversible changes and disturbances and requires high-quality restoration after the completion of ilmenite mining. The article suggests a methodology for assessing the forest vegetation potential of soils in areas disturbed by ilmenite mining using remote earth sensing (RES). Based on satellite images and spectral characteristics, we determined the parameters of soil type and moisture, as well as the vegetation and moisture index of the forest vegetation layer The results of the remote earth sensing were compared with the results of laboratory analyzes of soil samples from the territory operated by the branch of the Irshansk Mining and Processing Plant of PJSC UMCC. Normalized Difference Vegetation Index, Normalized Difference Moisture Index, soil type and moisture were calculated and identified using QGIS software from data obtained from free-access satellite images. The results showed that a combination of laboratory and remote sensing methods can be quite effective for studying areas disturbed by mining activities and the state of their recovery after reclamation.

Can Water-Detection Indices Be Reliable Proxies for Water Discharges in Mid-Sized Braided Rivers Using Coarse-Resolution Landsat Archives?

The use of water detection (WD) indices to infer daily discharge (Qd) has a great potential to enrich needed hydrological data for understanding fluvial processes driving the morphological changes of braided rivers. However, no consensus has been reached on which one stands out for use in mid-sized braided rivers. In this study, we compared the physical characteristics of three most commonly used WD indices, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Moisture Index (NDMI), for two mid-sized braided reach segments in the Qinghai-Tibet Plateau, China, that have different morphological structures. Relying on the Google Earth Engine web interface, we calculated the total mean water width (WWt) based on the detected surface-water areas (As) and braiding index (BI), as well as the mean values (m) of these indices over about four decades at the braided corridor scale (cs) (mNDWIcs, mMNDWIcs, and mNDMIcs). We then examined different responses of these indices to water and non-water features and their best threshold values for characterizing channel structures. Our analyses demonstrated that (1) NDWI and MNDWI perform well for detecting braided channel structures with the threshold of zero; (2) WWt is generally better correlated to Qd in a linear style than WD indices do, particularly when calculated from MNDWI; and (3) among WD indices calculated at the braided corridor scale, mMNDWIcs shows a better relationship with Qd than mNDMIcs does. Finally, we provided mechanisms that may explain these differences in terms of photometric discrepancies in calculating WWt and WD indices and the impact of image resolution on their calculations.

Predicting the Distribution of Mangrove Forests through Random Forest Algorithm by Combining Spectral Features and DEM Data

Using satellite data and machine learning-based classification methods for mangrove forest identification has gained popularity due to their effectiveness in producing high accuracy. Therefore, this research developed a random forest classification model using various dataset combinations representing spectral and topographical aspects to identify mangrove forests in Segara Anakan, Cilacap. We used the Sentinel-2 imagery acquired in 2022. Two types of digital elevation model (DEM) data were utilized, namely the National Digital Elevation Model (DEMNAS) and Multi-Error-Removed Improved-Terrain DEM (MERIT DEM). Another critical dataset used is the Normalized Difference Moisture Index (NDMI), derived from the ratio between near-infrared (NIR) and shortwave-infrared (SWIR) bands. The classification method used is the random forest algorithm on six different dataset combinations, including IMAGE, IMAGE+NDMI, IMAGE+DEMNAS, IMAGE+MERIT DEM, IMAGE+NDMI+DEMNAS, and IMAGE+NDMI+ MERIT DEM. The results showed that the combination of Image+DEMNAS and Image+NDMI+DEMNAS datasets was able to identify the mangrove forests more optimally. Incorporating DEM data alongside IMAGE and NDMI datasets resulted in a remarkable level of accuracy in mangrove forest mapping, exceeding 90%. DEM data has a very important role in increasing the classification accuracy of mangrove forests using the random forest algorithm.

Evaluating crop water stress through satellite-derived crop water stress index (CWSI) in Marathwada region using Google Earth Engine

Accurate information of crop water requirements is essential for optimal crop growth and yield. Assessing this information at the appropriate time, particularly during the vegetative and reproductive stages when water demand is highest, is crucial for successful crop production. Our study cantered on the drought-prone Marathwada region, specifically targeting the years 2015 to 2020, encompassing the challenging drought year of 2015 and the favourable year of 2020. The crop water stress was detected using crop water stress (CWSI) index and compared with normalized difference vegetation index (NDVI) and normalized difference wetness index (NDWI) derived from satellite data. Our findings reveal a negative correlation between the CWSI and satellite derived vegetation indices NDVI and NDWI. Notably, the NDWI index exhibits stronger alignment with CWSI compared to NDVI. The correlation demonstrates particular robustness during drought or deficient rainfall years such as 2015, 2017, and 2019, while weaker correlations are observed in 2016, 2018, and 2020. Moreover, these correlations display variations across different areas within distinct rainfall zones.

Use of remote sensing techniques to assess water storage variations and flood-related inflows for the Hawizeh wetland.

High spatial and temporal resolution remote sensing data are becoming readily available. This has made the use of remote sensing to monitor and quantify spatiotemporal changes in surface waters feasible and efficient. In this paper, remote sensing techniques based on spectral indices were used to assess the changes in submerged areas and water storage in the Hawizeh marsh (south of Iraq) during the 2019 flood. Two water indices, the Normalized Difference Water Index (NDWI) and Normalized Difference Moisture Index (NDMI), were used for this purpose. Water indices have been frequently utilized to detect water bodies because of their particular spectral properties in the visible and infrared spectrum. Non-measured flood-related flows into the marsh have also been estimated by applying the water balance approach. The accuracy assessment of the water areas extracted by the remote sensing indices showed an acceptable degree of reliability, which reflected positively on the water inflow calculations. As the Hawizeh is a transboundary marsh shared by Iraq and Iran, remote sensing techniques allowed for the estimation of difficult-to-measure inflows from the Iranian side. The results of the water balance revealed that the inflows from the Iranian side to the marsh during the 5 months of the flood made up approximately 41.2% of the total water volume entering the marsh. The study demonstrated the feasibility of using uncomplicated water extraction methods that depend on remote sensing data to monitor hydrological changes in the Hawizeh wetland that lack sufficient data.

An Integrated Approach between Multispectral Satellite Images and Geophysical and Morpho-Topographic Surveys for the Detection of Water Stress Associated with Coastal Dune Erosion

Coastal erosion occurs due to different processes involving physical and ecological systems. One of these factors is the degree of water stress experienced by dune vegetation. While healthy dune vegetation can help to stabilize the dune systems, water-stressed vegetation can instead enhance dune erosion. In this study, remote sensing techniques were used to monitor the water stress affecting the dune vegetation in dune systems along the alluvial plain of the Chiatona coast (Apulia, Southern Italy) located on the Ionian Arc. Multispectral satellite data from Landsat 8/9 and Sentinel-2 were used to assess the water stress at different spatial scales over a 4-year monitoring period from 2019 to 2023. The normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were used to identify dune surfaces that were experiencing water stress. Furthermore, a terrestrial laser scanner and LiDAR data were acquired at different temporal ranges in areas affected by water stress to highlight coastal changes in areas associated with unhealthy dune vegetation. A large drop in NDVI values was observed in May 2020 due to the occurrence of coastal fires in some parts of the Chiatona coast. Geoelectrical surveys were conducted to investigate if coastal fires were capable of saline groundwater contamination, potentially enhancing dune erosion in these areas. The joint analysis of remote sensing, topographical, and geoelectric data showed that water stress reduced the amount of healthy dune vegetation, triggering dune deflation processes that resulted in increased coastal erosion rates, while also leading to the saline contamination of groundwater.

MONITORING COASTAL AREAS USING NDWI FROM LANDSAT IMAGE DATA FROM 1985 BASED ON CLOUD COMPUTATION GOOGLE EARTH ENGINE AND APPS

Abstract. The coastal area is an area that has a dense population with a lot of human activities that occur there. Due to environmental changes and human activities, changes often occur in coastal areas ranging from erosion and sedimentation. Changes must continuously be monitored to plan countermeasures due to the occurring phenomena. This study aims to create a website-based application to monitor coastal areas. This study will use Landsat data 5,7,8, and 9 to see changes in coastal areas. The analysis can be provided from 1985 until recent data by integrating four Landsat satellites. The NDWI index (Normalized Difference Wetness Index) analyzes changes occurring in coastal areas and differentiates between water and land area. The analysis is not only in the form of changes that occur in coastal areas but also in time series analysis, and trends that occur at a point can be analyzed using land trend analysis. The resulting website based on Cloud Computation in Google Earth Engine can be seen at the link https://bit.ly/MonitoringPesisir. This website can automatically update, and users can choose the location to monitor. This research is expected to be used by policymakers to monitor and plan the development and regulation of coastal areas.

Coastline Change Analysis using Landsat-8 on Rebana Metropolitan Area, West Java

The Rebana metropolitan area is an industrial area development covering Cirebon-Patimban-Kertajati to support accelerated economic growth, especially in the West Java region. In order to minimize failures in supporting the development targets of the Rebana metropolitan area, it is necessary to carry out spatial planning with consideration of the hazard aspects in the development area. One of the aspects that need to be studied further is the dynamics of the coastal area such as coastline changes that occur due to erosion or sedimentation. Analysis of erosion and sedimentation is essential for further studies to be carried out regarding the factors that cause erosion and sedimentation and how to protect and prevent them in the future. This research utilizes remote sensing techniques and integrated geographic information systems (GIS) to identify coastline changes from 2017-2022 in the coastal areas of Subang, Indramayu, and Cirebon districts which are included in the Rebana metropolitan area. In this study, Landsat-8 satellite imagery is used to identify and quantify Spatio-temporal changes that occur in the coastal area of the Rebana metropolitan area in 6 years. The Normalized Difference Wetness Index (NDWI) algorithm is used to separate wet and dry objects which are interpreted as bodies of water and land. Thresholding the results of the NDWI algorithm along with the on-screen digitization approach was carried out for coastline extraction. The calculation of coastline changes is carried out using the Digital Shoreline Analysis System (DSAS) so that the rate of change of coastline in the study area is obtained. During the study period, the results showed that the average erosion rate in the Rebana metropolitan area was -3.66 m/year and the average sedimentation rate was +6.28 m/year, where the greatest erosion rate occurred in Kapetakan sub-district, Cirebon Regency with an average rate -7.63 m/year, and the greatest sedimentation rate occurs in Pusakanagara sub-district, Subang Regency with an average rate +24.64 m/year. Karangampel Sub-district in Indramayu Regency has 79.89%, the widest erosion area of its total coastline length, and Balongan Subdistrict, Indramayu Regency has the widest sedimentation, 91.37% of the total coastline length. The land use that is most affected by erosion and accretion in the coastal area of the Rebana metropolitan area is fishpond.

Wildfire likelihood in Canadian treed peatlands based on remote-sensing time-series of surface conditions

While peatland fires are less common than upland fires, long periods of moisture deficit can increase fuel availability and lead to greater fire risks in peatlands. Peatland fire likelihood is crucial as these ecosystems serve as natural fire breaks under normal hydrological conditions but can easily turn into fuel sources under drought conditions. Furthermore, peatland fires have significant implications for atmospheric pollution, releasing fine particulates, carbon, and mercury emissions. Current frameworks for estimating wildfire likelihood predominantly rely on fuel-moisture indices developed for upland environments, which cannot adequately capture the hydrological and fuel-moisture conditions specific to peatlands. Additionally, common fuel-moisture indices are reliant on station-derived meteorological data, which are scarce in remote areas and therefore imprecise over large portions of sub-Arctic Canada and the boreal forest.Remote sensing data represents a proxy for near real-time surface conditions for remote locations, such as moisture and vegetation, where a time series of remotely-sensed metrics provides information about patterns and trends in local conditions over longer periods. With this in mind, we used SAR (Sentinel-1), multispectral (Sentinel-2) and human infrastructure data as a predictor of wildfire probability in treed peatlands of Canada. We developed a fire probability model based on metrics drawn from a seasonally decomposed time series of SAR backscatter and common multispectral indices of greenness, soil moisture and vegetation moisture. Sample sites were derived from an existing treed peatland map, then the Canadian Forest Service National Burned Area Composite dataset was used to identify fire events within those areas. This study presents a robust and versatile framework for training fire probability models that leverage remote sensing data and temporally-invariant ancillary predictors. Fire probability in peatland locations, given current conditions, was predicted through a model algorithm called extreme gradient boosting. To obtain fire probabilities over a four-week timeframe, the output of the binary classifier was calibrated, transforming its raw scores into more accurate probabilities. This approach showcases the effectiveness of machine learning methods in harnessing seasonally-decomposed remotely sensed time-series to improve fire prediction.The most important predictors of fire likelihood in treed peatlands were found to be multispectral moisture-related indices (Tasseled cap Wetness and Normalized Difference Moisture Index) and the distance to human infrastructure, which may indicate an overrepresentation of human-caused fires in the predictions. Using subsets of predictors, different iterations of the model demonstrate that incorporating SAR backscatter data can enhance the accuracy of fire likelihood predictions. However, models relying solely on SAR data exhibited lower performance, indicating limited applicability of SAR backscatter in treed peatlands without more advanced filtering and a correction method.The fire likelihood model presented here adds seasonally-detrended time series metrics from remote sensing observations to inform fire risk, independent from meteorological data. Such models could be implemented for a near-real time assessment of fire conditions that could complement the existing framework for fire risk predictions in Canada.