Normalized Difference Moisture Index Research Articles

Pearsonema plica in red foxes (Vulpes vulpes) from semi-arid areas of the Iberian Peninsula.

The nematode Pearsonema plica is a parasite infecting the urinary bladder of carnivores, with a described prevalence ranging from 1 to 90%. This parasite needs earthworms as intermediate host to complete its life cycle, being the red fox (Vulpes vulpes) a definitive host. The objective of this study was to analyse the prevalence and intensity of P. plica in the red fox population from the Region of Murcia (SE Spain), an area with semi-arid Mediterranean climate. The urinary bladder, kidneys and ureters of 167 red foxes were collected at necropsy, opened and observed to detect adult parasites. The influence of host variables (sex, age and body condition using Kidney Fat Index) and environmental variables (Normalized Difference Vegetation Index, Normalized Difference Moisture Index, Bare Soil Index, temperature, radiation, evapotranspiration, precipitation, Corine Land Cover categories and distance to urban areas) were evaluated using a Generalised Linear Model. Moran index was used to evaluate the parasite spatial aggregation. The prevalence found was very low (2.4%; median abundance 0 nematodes per fox; median intensity 7.5 nematodes per parasitized fox), which contrast with those described in other red fox populations in Europe. Environmental variables had a significant influence on the occurrence of P. plica, being NDMI, mean summer precipitation, percentage of forest and agricultural areas positively associated with P. plica abundance. The south-eastern Iberian Peninsula has a semi-arid climate that hinders the development of the life cycle of this nematode, which justifies its occurrence in specific areas where there are the suitable environmental conditions for the presence of earthworms. However, although semi-arid Mediterranean areas do not seem to be favourable carnivores to be parasitized by P. plica, we cannot underestimate the risk that exists in those areas where, either naturally or by human activity, there are environmental factors that favor the presence of this nematode.

Thermal Response to Land‐Use Land‐Cover Patterns: An Experimental Study in Famagusta, Cyprus

AbstractThe transformation of land‐use land‐cover (LULC), as induced by human and natural processes, drives the biogeochemistry of Earth and influences the complex interaction between many factors. The most significant outcomes of these interactions are the thermal responses and changes in land‐surface temperature (LST). The consequences of these factors can be seen in both global and regional scale changes in climate—remote sensing methods are widely used in constraining these changes and, thus, there is an increasing shift toward LULC studies. This study uses a hybrid method to investigate the thermal responses to LULC patterns by analyzing LST. This method combined the normalized difference vegetation index, normalized difference built‐up index, normalized difference barrenness index, and normalized difference moisture index, and is then applied to a study area in Famagusta, Cyprus. The results of this study can help to guide future development and land‐use optimization, in Famagusta or any other area in which the method is applied. The study results show that agricultural intensification contributes to increased LST ( = 313.3), whereas wetland ( = 307.5) and forested land decrease the LST. Positive correlations are recorded between increased LST, the normalized difference built‐up index (r = 0.702, p < 0.01), and two digital elevation models (r = 0.372, r = 0.364, p < 0.01), while negative correlations existed between LST and the indices of normalized difference vegetation (r = −0.082, p < 0.01), barrenness (r = −0.711, p < 0.01), and moisture (r = −0.702, p < 0.01).

Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine

An analysis of the landscape spatial structure and diversity in the mountain ranges of Northeast Siberia is essential to assess how tundra and boreal landscapes may respond to climate change and anthropogenic impacts in the vast mountainous permafrost of the Arctic regions. In addition, a precise landscape map is required for knowledge-based territorial planning and management. In this article, we aimed to explore and enhanced methods to analyse and map the permafrost landscape in Orulgan Ridge. The Google Earth Engine cloud platform was used to generate vegetation cover maps based on multi-fusion classification of Sentinel 2 MSI and Landsat 8 OLI time series data. Phenological features based on the monthly median values of time series Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Moisture Index (NDMI) were used to recognize geobotanical units according to the hierarchical concept of permafrost landscapes by the Support Vector Machine (SVM) classifier. In addition, geomorphological variables of megarelief (mountains and river valleys) were identified using the GIS-based terrain analysis and landform classification of the ASTER GDEM scenes mosaic. The resulting environmental variables made it possible to categorize nine classes of mountain permafrost landscapes. The result obtained was compared with previous permafrost landscape maps, which revealed a significant difference in distribution and spatial structure of intrazonal valleys and mountain tundra landscapes. Analysis of the landscape structure revealed a significant distribution of classes of mountain Larix-sparse forests and tundra. Landscape diversity was described by six longitudinal and latitudinal landscape hypsometric profiles. River valleys allow boreal–taiga landscapes to move up to high-mountainous regions. The features of the landscape structure and diversity of the ridge are noted, which, along with the specific spatial organization of vegetation and relief, can be of key importance for environmental monitoring and the study of regional variability of climatic changes.

Rainfall Variability and Tidal Inundation Influences on Mangrove Greenness in Karimunjawa National Park, Indonesia

Mangroves, which are vulnerable to natural threats and human activities on small islands in the tropics, play an essential role as carbon sinks, helping to mitigate climate change. In this study, we discussed the effect of natural factors on mangrove sustainability by analyzing the impact of rainfall, land surface temperature (LST), and tidal inundation on the greenness of mangroves in Karimunjawa National Park (KNP), Indonesia. We used Sentinel-2 image data to obtain the normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) during the dry season to determine the effect of inundation on mangrove greenness and soil moisture. The tidal inundation area was calculated using topographic data from the KNP and tidal observations from the area adjacent to it. Unmanned autonomous vehicles and topographic data were used to estimate mangrove canopy height. We also calculated mangrove greenness phenology and compared it to rainfall from satellite data from 2019–2021. Results show that the intertidal area is dominated by taller mangroves and has higher NDVI and NDMI values than non-intertidal areas. We also observed that mangroves in intertidal areas are mostly evergreen, and optimum greenness in KNP occurs from February to October, with maximum greenness in July. Cross-correlation analysis suggests that high rainfall affects NDVI, with peak greenness occurring three months after high rainfall. The LST and NDVI cross-correlation showed no time lag. This suggests that LST was not the main factor controlling mangrove greenness, suggesting tides and rainfall influence mangrove greenness. The mangroves are also vulnerable to climate variability and change, which limits rainfall. However, sea-level rise due to climate change might positively impact mangrove greenness.

Spatiotemporal Variation of Land Surface Temperature in Henan Province of China from 2003 to 2021

Land surface temperature (LST) is a key parameter closely related to various land surface processes and surface-atmosphere interactions. Analysis of spatiotemporal variation of time-series LST may provide useful information to understand eco-climatic characteristics. In this study, the spatiotemporal pattern of LST and its trend characteristics in Henan Province were examined based on MODIS LST products from 2003 to 2021. In addition, the influences of land cover types, Nighttime Light data (NTL) and Normalized Difference Moisture Index (NDMI) on LST variation were analyzed. The results indicated that: (i) The LST showed slight and rapid decreasing trend for 2004–2010 and 2018–2020, respectively, whereas an obvious increasing and slight increasing trend occurred for 2010–2013 and 2014–2018. In terms of spatial pattern, high-temperature, and sub-high-temperature were mainly distributed in the central part of the province with higher level of industrialization and urbanization at the annual, spring, summer, and daytime scales. While in fall, winter, and the nighttime, the spatial distribution of LST exhibited decreased trend from the southern part to the northern part of the province, the largest Standard Deviation (STD) was observed in summer. (ii) The interannual variation rate of LST was 0.08 °C/Y. The increasing trend mainly occurred in urban and built-up areas. At the seasonal scales, the rising rate decreased sequentially in the order of fall, winter, spring, and summer. In addition, the rising rate in the daytime was higher than that in the nighttime. (iii) LST increased along with the expansion of urban and built-up lands, except in winter. At the annual scales, 84.69% of areas with NTL data exhibited a positive correlation with LST, and NDMI in the western part with high elevation presented a significantly positive correlation to LST, while a significantly negative correlation occurred in urban and built-up areas. The cooling effect of NDMI on LST in the daytime was greater than that in the nighttime. In cropland areas, LST showed a non-significant correlation with NDMI at the annual scale, and a significantly negative correlation with NDMI in spring, summer, and fall. The influence mechanism of cropland on the variation of LST at different timescales needs to be further explored. These findings might provide some hints to understand climate change and its causes in the province.

Monitoring of Rice Growth Phases Using Multi-Temporal Sentinel-2 Satellite Image

Rice is the primary source of nutrition food of more than half of the world’s population, and it is hugely important in the global economic growth, food security, water use, and climate change. The need for satellite systems to monitor rice crops and assist in rice crop management is gaining in popularity. The European Space Agency’s (ESA) launched Sentinel-2 A + B twin platform’s which enhanced the temporal, spatial, and spectral resolution, opening the way for their widely use in crop monitoring. Aside from the technical features of the Sentinel-2 A and B constellation, the easily accessible type of information they generate as well as the appropriate support software have been significant improvements for rice crop monitoring. In this study, the spectral reflectance has been analysed to find how far their potential in determining rice growth phases. The highest spectrum in reflectance was observed in the near infrared (NIR) region (842 nm). Because of the structure of mesophyll cells tissues and the inner backscatter of air spaces, moisture content, and air–water abstraction layers within the leaves, the reflectance in the NIR region seems to be much larger than in the visible band. The multi-temporal vegetation index namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI) have derived from ten Sentinel-2 images cover the entire rice season. These indices have been tested to determine the rice growth phases over the rice season. The spatial distribution of each tested indices is displayed in the map output. The maps are then analysed and compared to determine the potential of each index in determining rice growth phases. It was discovered in this study that there was a quadratic correlation between all of the tested indices and rice age. The Normalized Difference Vegetation Index (NDVI) is the most accurate vegetation index for estimating rice growth phases, followed by SAVI and NDMI.

Estimating Biophysical Parameters of Native Grasslands Using Spectral Data Derived from Close Range Hyperspectral and Satellite Data

Estimating biophysical parameters of native grassland enables management changes that affect ecological processes and economic benefits. Although multiple hyperspectral studies were focused on native grasslands, just a few compare data at different scales and among ecoregions. In this study, we compared data collected at different spectral and spatial scales and among Canadian Prairie ecoregions. Field observations indicate that the Fescue Ecoregion grasslands has specific dominant species, while the Moist-Mixed and Mixed Ecoregions share similar dominant species, which is important in determining parameters such as leaf area index (LAI) and canopy height. Hyperspectral measurements showed a specific signature for the Fescue Ecoregion, due to denser canopies, while the Moist-Mixed and Mixed Ecoregions showed similar spectral characteristics to each other. The correlation between biophysical parameters and spectral indices reveals the importance of LAI, since it was significantly correlated with all spectral indices analyzed. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and the Plant Senescence Reflectance Index (PSRI) showed significant correlations with biophysical parameters. The comparison results indicated the PSRI being overestimated at all sites (satellite data) and NDVI underestimated at all sites. Finally, the satellite-derived LAI showed a significant positive relationship with the field-measured LAI.

Long-Term Effects of Fire Severity and Climatic Factors on Post-Forest-Fire Vegetation Recovery

As a major disturbance to forest ecosystems, wildfires pose a serious threat to the ecological environment. Monitoring post-fire vegetation recovery is critical to quantifying the effects of wildfire on ecosystems and conducting forest resource management. Most previous studies have analyzed short-term (less than five years) post-fire recovery and limited the driving factors to temperature and precipitation. The lack of long-term and multi-faceted observational analyses has limited our understanding of the long-term effects of fire on vegetation recovery. This study utilized multi-source remote sensing data for a long time series analysis of post-fire vegetation recovery in China based on Google Earth Engine (GEE) cloud computing platform. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Burn Ratio (NBR), and Normalized Difference Moisture Index (NDMI) were selected to quantify the low, moderate, and high severity of burned areas. Ridge Regression Model (RRM) was used to analyze the relationship between 15 driving factors and the vegetation regeneration process. The results show that it took at least 7–10 years for the vegetation index to recover to the pre-fire level after a forest fire. The recovery rate of high severity combustion areas was the fastest within the first two years. From the results of Ridge Regression, it came out that the overall fitting degree of the model with NDVI as the dependent variable was superior than that with EVI. The four variables of temperature, precipitation, soil temperature, and soil moisture were able to explain the change in more detail in vegetation indices. Our study enriches the research cases of global forest fires and vegetation recovery, provides a scientific basis for the sustainable development of forest ecosystems in China, and provides insight into environmental issues and resource management.

GOOGLE EARTH ENGINE BASED TEMPORAL ANALYSIS OF INDICES USED FOR FOREST FIRE STUDY IN MIZORAM, INDIA

Abstract. Forest fires would be a global disaster if they were not addressed seriously? From 2015 to 2021, the number of forest fires in India nearly tripled. According to FSI, the North Eastern Himalayas, one of UNESCO's 36 Biodiversity Hotspots, account for 36% of forest fires in India. This state is dominated by tribal population which practices shifting agriculture. It’s 76.01% of total forest cover is highly prone to Forest fires. Despite this, there hasn't been any time-series research on forest fires in this region. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burnt Ratio (NBR), Aerosol Free Vegetation Index (AFRI 1600), and Land Surface Temperature may all be linked to forest fires (LST). For Mizoram, India, random samples were taken every 16 days using Landsat 8 satellite data across different land cover types, including dense forest, sparse forest, farmland, and bare land etc. The study was conducted on a bimonthly basis from January 2016 to June 2021. The findings of this work show that an automated temporal analysis utilizing GEE may be used successfully over a wide range of land cover types, providing critical data for future monitoring of such threats.

The Potential of Agricultural Land Drought Using Normalized Difference Drought Index in Ciampel Subdistrict Karawang Regency

Karawang Regency, known as the National Rice Reserve, is experiencing drought on farmland. The Regional Disaster Management Agency (BPBD) of Karawang Regency noted that drought in 14 villages spread across three sub-districts in Karawang Regency has developed in 2019, such as Ciampel sub-district. Rice production decreased in 2015-2019 by 19 percent. The purpose of this study is to analyze the drought area of agricultural land using the Normalized Difference Drought Index (NDDI) and analyze the relationship between agricultural land drought and rainfall in Ciampel Sub-District, Karawang Regency in 2015 and 2019. The study used Landsat 8 OLI/TIRS in August-September 2015 and 2019. Agricultural land drought using the NDDI method is the ratio between the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Wetness Index (NDWI). The results showed a map of the distribution of agricultural land drought in Ciampel Sub-district, Karawang Regency during 2015 and 2019 with three classes of agricultural land drought (dry, rather dry, normal). The total area of agricultural drought in August 2015 was 11,166 hectares and as of September 2019 was 3,119 hectares. While as of September 2015, it was 3,086 hectares, and in 2019 was 3,158 hectares. The drought that hit Ciampel Sub-District in September 2019 hit almost all areas and dry areas in the middle eastern part of the Ciampel Sub-District. The drought, which is included in the classification of dry that hit irrigated rice field, was 20.19 %. Meanwhile, the rainfed rice field was 32.79%, and in dryland was 24.83%.

A Remote Sensing-Based Analysis of the Impact of Syrian Crisis on Agricultural Land Abandonment in Yarmouk River Basin.

In this study, we implemented a remote sensing-based approach for monitoring abandoned agricultural land in the Yarmouk River Basin (YRB) in Southern Syria and Northern Jordan during the Syrian crisis. A time series analysis for the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) was conducted using 1650 multi-temporal images from Landsat-5 and Landsat-8 between 1986 and 2021. We analyzed the agricultural phenological profiles and investigated the impact of the Syrian crisis on agricultural activities in YRB. The analysis was performed using JavaScript commands in Google Earth Engine. The results confirmed the impact of the Syrian crisis on agricultural land use. The phenological characteristics of NDVI and NDMI during the crisis (2013–2021) were compared to the phenological profiles for the period before the crisis (1986–2010). The NDVI and NDMI profiles had smooth, bell-shaped, and single beak NDVI and NDMI values during the period of crisis in comparison to those irregular phenological profiles for the period before the crisis or during the de-escalation/reconciliation period in the study area. The maximum average NDVI and NDMI values was found in March during the crisis, indicating the progress of natural vegetation and fallow land, while they fluctuated between March and April before the crisis or during the de-escalation/reconciliation period, indicating regular agricultural and cultivation practices.

Influences of different environmental covariates on county-scale soil type identification using remote sensing images

The scientific selection of environmental covariates is an important factor that affects the accuracy of digital soil mapping (DSM).An investigation of the influences of different environmental covariates on soil type identification is vital to understand soil development and predictive soil mapping. In this study, the soil groups in the typical plain and hilly areas of the Jiaodong Peninsula in China were taken as the objects of investigation. Based on five environmental characteristics and remotely sensed indexes (texture, terrain, biology, major spectral components and moisture), 21 covariates sourced from Landsat 8 remote sensing images and a digital elevation map were utilized for the predictive mapping of soils. The identification accuracies of the soil groups were acquired through maximum likelihood classification, and the relevant environmental factors that are important for soil mapping and the influence of different environmental covariates on soil mapping were explored. The results show that texture features had the greatest influence on DSM, followed by terrain, major spectral components, moisture and biological factors. The single environmental covariate with the greatest impact on DSM was the texture entropy parameter. For areas containing plain and hilly landforms, the entropy, elevation, slope, normalized difference vegetation index (NDVI), third principal component (PC3) and normalized difference moisture index (NDMI) indicators compose the optimal covariate combination for soil identification. Clarifying the impacts of different environmental covariates on DSM can improve the soil classification applicability and accuracy. The environmental covariates discussed in this study would be useful in similar environmental areas and would provide a reference for DSM.

An Approach for Monitoring and Classifying Marshlands Using Multispectral Remote Sensing Imagery in Arid and Semi-Arid Regions

Marshlands in arid and semi-arid areas are considered constantly changing environments due to unsecured water supplies as a result of high evapotranspiration and limited and highly variable rainfall. Classification of marshlands in these regions and mapping of their land cover is not an easy task and maps need to be upgraded frequently. Satellites provide enormous amounts of information and data for the continuous monitoring of changes. The aim of this paper is to introduce an approach using multispectral satellite imagery that was adopted to classify and monitor the Al Hammar Marsh (Iraq) over several years and to suggest a relationship between the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and the Normalized Difference Water Index (NDWI), using Landsat 8 data with a resolution of 30 m × 30 m, validated with Sentinel-2 datasets at 10 m × 10 m. Six land cover classes were used: (1) open water, (2) dry area, (3) dense vegetation, (4) medium-density vegetation, (5) sparse vegetation, and (6) wet soil. Three indices, NDWI, NDMI, and NDVI, were chosen for the automatic classification of each pixel and the creation of a time series of land cover maps. The proposed method can efficiently classify and monitor marshlands and can be used to study different marshlands by adjusting the thresholds for NDVI, NDMI, and NDWI. Overall, the correlation for all classes (R) between Landsat 8 and Sentinel-2 is about 0.78. Thus, this approach will help to preserve marshes through improved water management.

Differentiation of Six Grassland/Forage Types in Three Canadian Ecoregions Based on Spectral Characteristics

Differentiation of grassland/forage types and accurate estimates of their location and extent are important for understanding their ecological processes and for applying appropriate management practices. We are aiming to reveal the different spectral characteristics of six grassland/forage land covers in three ecoregions located in the Canadian Prairies, based on field data and satellite images. Three spectral indices representing productivity (Normalized Difference Vegetation Index (NDVI)), moisture content (Normalized Difference Moisture Index (NDMI)), and plant photosynthetic activity (Plant Senescence Reflectance Index (PSRI)) were used for comparison of means, comparison of coefficient of variation (CV), and analysis of variance (ANOVA). The results indicated that different grassland types show distinguishable spectral characteristics in the Moist-Mixed and Mixed Ecoregions, while it was not possible to differentiate the classes in the Fescue Ecoregion. To further investigate the within-sites and between-sites heterogeneity, we calculated the CV in a 3 × 3 window and placed them in comparative triangles to demonstrate their potential separability. Results indicated that the triangles based on the CV offered greater class separability in the Fescue Ecoregion and in the Mixed Ecoregion.

Utilization of NDMI Method in Landsat 8 Satellite Imagery for Analysis of Multi-Hazard Susceptibility

Liquefaction and landslide can occur during earthquakes caused by changes in soil saturation levels so that the soil loses strength due to loss of tension between grains. One of the determinations of soil moisture data using satellite imagery analysis is Landsat. Landsat has provided moderate, global, synoptic spatial resolution and repeated earth's soil surface coverage. This paper discusses the multi-hazard susceptibility using Landsat-8 satellite imagery with a combination of NDMI (Normalized Difference Moisture Index) ratio bands in Sunurraya Village and Simpang Saga Village, South OKU Regency, South Sumatra. The combination of NDMI bands determines the spread of soil saturation levels and differences in moisture in vegetation conditions. Other supporting data are soil's physical properties, including water content, density, hydrometer analysis, and Atterberg limits analysis. Overlay of NDMI data analysis and soil test analysis shows the level of liquidation insecurity in the research area on a regional and local scale

GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria

Forest fires are among the most major causes of global ecosystem degradation. The integration of spatial information from various sources using statistical analyses in the GIS environment is an original tool in managing the spread of forest fires, which is one of the most significant natural hazards in the western region of Syria. Moreover, the western region of Syria is characterized by a significant lack of data to assess forest fire susceptibility as one of the most significant consequences of the current war. This study aimed to conduct a performance comparison of frequency ratio (FR) and analytic hierarchy process (AHP) techniques in delineating the spatial distribution of forest fire susceptibility in the Al-Draikich region, located in the western region of Syria. An inventory map of historical forest fire events was produced by spatially digitizing 32 fire incidents during the summers of 2019, 2020, and 2021. The forest fire events were divided into a training dataset with 70% (22 events) and a test dataset with 30% (10 events). Subsequently, FR and AHP techniques were used to associate the training data set with the 13 driving factors: slope, aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Topographic Wetness Index (TWI), rainfall, temperature, wind speed, TWI, and distance to settlements, rivers and roads. The accuracy of the maps resulting from the modeling process was checked using the validation dataset and receiver operating characteristics (ROC) curves with the area under the curve (AUC). The FR method with AUC = 0.864 achieved the highest value compared to the AHP method with AUC = 0.838. The outcomes of this assessment provide constructive spatial insights for adopting forest management strategies in the study area, especially in light of the consequences of the current war.

ASSESSMENT OF MODEL GRASS PLOTS OF THE CITY OF KYIV IN ECO-CONDITIONS OF ANTHROPOGENIC LOAD

Urban heat islands (UHI) and rapid urbanisation create new health risks for residents of urban territories. The authors consider lawns as an eco-stabilising factor of the urban environment and an element of ensuring the environmental safety of the capital. The purpose of this study was to determine the qualitative state of lawns to the factors of the urban ecosystem and their thermoregulatory function. To determine the impact of anthropogenic factors on research objects, the study assessed the amount of emissions into the atmospheric air from stationary and mobile sources, satellite maps of the intensity of nitrogen dioxide emissions (NO2 ) and sulphur dioxide (SO2 ) in the city’s air basin, heat distribution, and normalised difference moisture index (NDMI). To assess the condition and quality of lawns, the methods of assessing the grasslands of A.A. Laptev (1983) and an unmanned aerial vehicle (UAV) were used. It was established that the results of the evaluation of lawn coenoses using the method of O.O. Laptev and UAV are correlated (r=0.87). Lawn areas that were rated as in “satisfactory condition” are characterised by a higher temperature on the heat map and lower NDMI values. Indicators of lawn cover that were rated as in “good condition” correlate with surface temperature and NDMI. The difference in surface temperatures over the area of the experimental sites reaches 5°C and depends on the objects surrounding the perimeter. It was established that lawn areas adjust the thermal mode of the urban system in the warm period of the year, and reduce the temperature of the asphalt surface by about 0.5°C, and together with flower and woody plants – by about 0.9°C. The study results improve the understanding of the physiological effects of heat stress on lawn areas, which allows developing practical strategies for managing urban green spaces with limited water resources and anthropogenic loads

Yield loss assessment of grapes using composite drought index derived from landsat OLI and TIRS datasets

Drought is one of the most challenging natural phenomena and occurs due to inadequate and erratic precipitation (snowfall and rainfall) as well as the effects of high temperature on agronomical and horticultural crops. Among horticultural crops, grapes suffer extensively from inadequate berry formation due to drought. Therefore, developing a comprehensive drought monitoring tool with a drought assessment system that incorporates multiple agrometeorological variables into a single drought indicator is critical for supporting growers. In this context, the objective of this research was to assess drought severity using the composite drought index (CDI) and determine the variations in grape yields in drought-affected vineyards. In this study, the CDI was developed for the years 2016–2020 by comprising five indices: the vegetation condition index (VCI), temperature condition index (TCI), deviation of NDVI from the long-term mean (NDVI DEV), normalized difference moisture index (NDMI) and precipitation condition index (PCI). Moreover, the quantitative principal component analysis (PCA) approach was used to assign a weight for each input parameter, and then the weights of all the indices were combined into one composite drought index. Finally, Bayesian regularized artificial neural networks (BRANNs) were used to evaluate the yield variation in each affected vineyard. The results were validated with the standard precipitation index (SPI) from 2016 to 2020. Moderate to severe drought was detected with the CDI across Kabul Province during 2016 and 2018. The correlation coefficient between the CDI and SPI-1 in the active growth stages (April to October) was relatively high, at 0.64. A validation was performed with the correlating yield losses, and losses of 3.4 ton/ha and 4.7 ton/ha were found in 2016 and 2018, respectively, under severe drought conditions. The findings suggested that the CDI can be used for vineyard drought detections and agricultural organization support to more precisely aid farmers during severe drought conditions in grape-growing regions based on satellite remote sensing datasets.

Multi-Species Inference of Exotic Annual and Native Perennial Grasses in Rangelands of the Western United States Using Harmonized Landsat and Sentinel-2 Data

The invasion of exotic annual grass (EAG), e.g., cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), into rangeland ecosystems of the western United States is a broad-scale problem that affects wildlife habitats, increases wildfire frequency, and adds to land management costs. However, identifying individual species of EAG abundance from remote sensing, particularly at early stages of invasion or growth, can be problematic because of overlapping controls and similar phenological characteristics among native and other exotic vegetation. Subsequently, refining and developing tools capable of quantifying the abundance and phenology of annual and perennial grass species would be beneficial to help inform conservation and management efforts at local to regional scales. Here, we deploy an enhanced version of the U.S. Geological Survey Rangeland Exotic Plant Monitoring System to develop timely and accurate maps of annual (2016–2020) and intra-annual (May 2021 and July 2021) abundances of exotic annual and perennial grass species throughout the rangelands of the western United States. This monitoring system leverages field observations and remote-sensing data with artificial intelligence/machine learning to rapidly produce annual and early season estimates of species abundances at a 30-m spatial resolution. We introduce a fully automated and multi-task deep-learning framework to simultaneously predict and generate weekly, near-seamless composites of Harmonized Landsat Sentinel-2 spectral data. These data, along with auxiliary datasets and time series metrics, are incorporated into an ensemble of independent XGBoost models. This study demonstrates that inclusion of the Normalized Difference Vegetation Index and Normalized Difference Wetness Index time-series data generated from our deep-learning framework enables near real-time and accurate mapping of EAG (Median Absolute Error (MdAE): 3.22, 2.72, and 0.02; and correlation coefficient (r): 0.82, 0.81, and 0.73; respectively for EAG, cheatgrass, and medusahead) and native perennial grass abundance (MdAE: 2.51, r:0.72 for Sandberg bluegrass (Poa secunda)). Our approach and the resulting data provide insights into rangeland grass dynamics, which will be useful for applications, such as fire and drought monitoring, habitat suitability mapping, as well as land-cover and land-change modelling. Spatially explicit, timely, and accurate species-specific abundance datasets provide invaluable information to land managers.

Bamboo Forest Mapping in China Using the Dense Landsat 8 Image Archive and Google Earth Engine

It is of great significance to understand the extent and distribution of bamboo for its valuable ecological services and economic benefits. However, it is challenging to map bamboo using remote sensing images over a large area because of the similarity between bamboo and other vegetation types, the availability of clear optical images, huge workload of image processing, and sample collection. In this study, we use the Landsat 8 times series images archive to map bamboo forests in China via the Google Earth engine. Several spectral indices were calculated and used as classification features, including the normalized difference vegetation index (NDVI), the normalized difference moisture index (NDMI) and textural features of the gray-level co-occurrence matrix (GLCM). We found that the bamboo forest covered an area of 709.92 × 104 hectares, with the provinces of Fujian, Jiangxi, and Zhejiang containing the largest area concentrations. The bamboo forest map was accurate and reliable with an average producer’s accuracy of 89.97%, user’s accuracy of 78.45% and kappa coefficient of 0.7789. In addition, bamboo was mainly distributed in forests with an elevation of 300–1200 m above sea level, average annual precipitation of 1200–1500 mm and average day land surface temperature of 19–25 °C. The NDMI is particularly useful in differentiating bamboo from other vegetation because of the clear difference in canopy moisture content, whilst NDVI and elevation are also helpful to improve the bamboo classification accuracy. The bamboo forest map will be helpful for bamboo forest industry planning and could be used for evaluating the ecological service of the bamboo forest.