Index NDVI Research Articles

Cохранение и восстановление продуктивности нарушенных золотодобычей земель с использованием инновационного подхода

The article summarizes the long-term experience of research conducted on the development of nature-like technologies on toxic technogenic formations of placer gold mining using a comprehensive assessment of the state of environmental objects and an innovative approach. A large amount of waste from the processing of gold deposits located in productive areas withdrawn from the forest lands fund has a negative impact on the components of the ecosphere. In this regard, the purpose of the study was to create a nature-like technology that allows preserving and restoring the productivity of disturbed gold mining lands to ensure their environmental safety. The object of the study was a technogenic system within the boundaries of the infl uence of the ceased to exist mining enterprise “Kerbinsky mine” of the P. Osipenko district (P. Briakan) of the Khabarovsk Territory. The research uses generally accepted methods, as well as remote sensing of the Earth (remote sensing), GIS technologies and mathematical apparatus. Remote sensing made it possible to identify disturbed territories on various geo-substrates, on multispectral combined images and NDVI index image. Using the QGIS program, an analysis of the dynamics of the transformation of disturbed lands was carried out and their area was calculated. It has been established that toxic waste from the enrichment of gold deposits of the second hazard class is a source of pollution of the habitat. Pollutants, migrating from the waste of processing into the components of the biosphere, lead to their large-scale technogenic pollution. Thus, the upper horizon of technogenic soils accumulates the maximum amount of toxic elements (mg/kg): Cu – 454, Zn – 1241, Pb – 934, which is signifi cantly higher than the background values from 3 to 9 or more times. The calculations of the value of the vegetation index NDVI showed that the study area can be classifi ed as highly toxic, belonging to the I category of environmental risks. The NDVI value in the technogenic territory ranged from 0.034 to 0.063, which indicates a weak degree of restoration of forest vegetation. Based on the analysis of the results obtained, new technological solutions are proposed, confi rmed by Patents of the Russian Federation.

Effect of the net radiation proxies on maize and soya evapotranspiration estimation using machine learning methods.

Accurate evapotranspiration (ET) estimation is essential for water management in crops, but it is not an easy task. Empirical ET methodologies require precise net radiation (Rn) measurements to obtain accurate results. Nevertheless, Rn measurements are not easy to obtain from meteorological stations. Thus, this study explored the use of machine learning algorithms with two Rn substitutes, to estimate daily ET: the extraterrestrial solar radiation (Ra) and a modelled Rn (RnM). Support Vector Machine (SVM), Kernel Ridge (KR), Decision Tree (DT), Adaptive Boosting (AB), and Multilayer Perceptron (MLP) were applied to model FLUXNET Rn and ET observations. Adaptive Boosting produced the best field Rn measurements (RnO), yielding a Root Mean Square Error of about 16 % of the mean observed Rn. The resulting Rn (AB RnM) was used to model daily crops ET employing the above-mentioned machine learning methods with RnO, AB RnM, and Ra, in conjunction with meteorological variables and the NDVI index. The evaluated methods were suitable to estimate ET, yielding similar errors to those obtained with RnO, when contrasted with ET observations. These results demonstrate that AB and KR are applicable with rutinary meteorological and satellite data to estimate ET.

Using images from Landsat satellites to characterize the transformations of the ground cover at the Bovanenkovo–Baydaratskaya Bay section of the route of the Bovanenkovo–Ukhta gas pipeline

By the case of the Bovanenkovo–Baydaratskaya Bay section of the route of the two underground gas pipelines located on the Yamal Peninsula, the possibility of using images from Landsat 5 and Landsat 8 satellites to characterize the transformations of the ground cover associated with the laying of pipelines is shown. The studies were carried out using seven summer time images (July, August) for the period from 2009 to 2020 based on parameters characterizing the chlorophyll content in the vegetation cover – the vegetation index (NDVI), the moisture index (NDWI) and the albedo (Alb) of the surface. In the year of laying the pipelines, directly above them, the average values of these parameters sharply decrease, and after 5–7 years, the values of the NDVI and NDWI indices stabilize to the initial values. During the same period, the average values of Alb increase by 13–15% relative to the initial level. According to the analyzed parameters, signs of disturbance and restoration of the ground vegetation cover are not recorded at a distance of more than 70–90 m from the central axis of each of the pipelines. In general, by the end of the analyzed period, no dominant changes in the NDVI, NDWI and Alb parameters are observed, indicating the transformations of the biophysical properties of the surface, which increase the likelihood of degradation of permafrost and destabilization of the state of pipelines.

Parcel-Level Mapping of Horticultural Crops in Mountain Areas Using Irregular Time Series and VHR Images Taking Qixia, China as An Example

Accurate and reliable horticultural crop classification results are an important part of agricultural management. At present, research on orchard classification based on optical images in complex mountain areas is vulnerable to the influence of cloudy weather, especially in the summer, which leads to a lack of key phenological characteristics. To solve this problem, a parcel-level orchard mapping experiment with an irregular time series was carried out in Qixia City, China. Firstly, the candidate parcels in the study area were extracted from VHR images with a spatial resolution of 0.55 m based on RCF and DABNet models. The F1 score and area-based intersection-over-union (IoU) of the parcel extraction results were calculated. When the boundary buffer radius was 1 m, the F1 score was 0.93. When the radius was 2 m, the F1 score was 0.96. The IoU was 0.872, which shows the effectiveness of the parcel extraction method. Then, based on Sentinel-2 data, the NDVI, EVI, and SAVI vegetation indexes were calculated to construct an irregular time series. A two-dimensional CNN model was used for classification. In order to verify the effectiveness of this method, the study also constructed regular time series for the study area and conducted classification experiments using the 2DCNN and LSTM as classifiers, respectively. Confusion matrices were constructed for the classification results, and the overall accuracy was calculated. The results show that the overall accuracy of the method based on irregular time series is 97.76%, with a kappa coefficient of 0.96, higher than the other experiments, which indicates that the classification method based on irregular time series is effective and can make full use of the fragmented spectral features. Finally, based on the candidate parcels and pixel-level classification results of the study area, the crop categories of the parcels were filled to achieve accurate parcel-level mapping of horticultural crops in complex mountain areas. This method can provide a theoretical reference for orchard crop mapping and serves the development of regional precision agriculture.

CHARACTERIZATION OF VEGETATION AND PREDICTION OF BIOMASS PRODUCTION IN SILAGE CORN CROP BASED ON SATELLITE IMAGES

The study used methods based on remote sensing to analyze the dynamics of a silage corn crop and predict biomass production. The corn crop, the Micado hybrid, was organized within the Didactic and Experimental Station of the "King Mihai I" University of Life Sciences in Timisoara, under the specific conditions of the 2021-2022 agricultural year. The soil in the crop plot was of chernozem type, and the crop was in a non-irrigated system. To obtain the satellite data, the Sentinel 2 system was used. To characterize the plot and the crop of silage corn, 14 sets of images were taken, in the interval March - July 2022. From the analysis of the images, the spectral data were obtained, and based on the consecrated formulas MSAVI, NDMI, NDVI and NBR indexes were calculated. The ANOVA test confirmed the reliability of the data and the presence of variance in the data set (F>Fcrit, p<0.001, for Alpha=0.001). The level of correlations identified between the calculated indices was between r=0.967 (NBR with NDVI) and r=0.996 (NDVI with MSAVI). The variation of the NBR index, in relation to the other indices, was described by linear equations, in terms of statistical safety (R2=0.965, p<0.001, in relation to MSAVI; R2=0.989, p<0.001 in relation to NDMI; R2=0.934, p<0.001 in relation to NDVI). The variation of indices in relation to time (t, days) during the study period was described by polynomial equations of the 3rd degree, in terms of statistical safety (R2=0.964 for MSAVI, R2=0.934 for NDMI, R2=0.941 for NDVI, and R2=0.961 for NBR). Regression analysis facilitated obtaining some equations, as models for predicting biomass production, under conditions of statistical safety. Based on the RMSEP parameter, it was found that the most reliable level of production prediction was obtained in the case of MSAVI and NDMI indices (RMSEP=0.05923).

Preliminary Study on the Use of Digital Surface Models for Estimating Vegetation Cover Density in Mountainous Area

Digital surface model (DSM) has been widely available for mapping and was also sometimes used for mapping vegetation height. The authors conducted a preliminary study to evaluate the potential use of DSMs derived from ASTER, ALOS, and SRTM for estimating vegetation cover density in mountainous area. This study used NDVI and SAVI vegetation indices, in addition to forest cover density (FCD) model as references for evaluation. A DSM-based volume index (Volindex) concept is introduced, which is the product of the canopy height model (CHM) and the pixel area. CHM was derived from the value difference between the DSM and the reference DEM. The Volindex model was then compared with the NDVI, SAVI and FCD. We found that all DSM-based Volindex models are not accurate enough to represent the vegetation cover density, although the ALOS Palsar-based Volindex could reach 41.53% accuracy and was finally used to predict the vegetation cover density.

Spatial distribution vegetation density, land surface temperature, and land surface moisture of Banda Aceh, Indonesia after 17years of tsunami: a multitemporal analysis approaches.

Although some studies on vegetation cover in relation to the land surface temperature (LST) and land surface moisture (LSM) have been carried out, but not yet been investigated in relation to the pre-disaster and after-tsunami disasters. This paper investigates the spatial extent of vegetation cover density, land surface temperature, and land surface moisture of Banda Aceh from 2000 to 2020 to represent the pre-disaster phase, during the disaster, post-disaster, rehabilitation, and reconstruction stages, and current conditions using remote sensing data. NDVI, LST, and NDMI indexes were utilized, and the mapping process of multitemporal images of Banda Aceh was carried out. The results show that after the tsunami, the distribution of vegetation density varies, tending to decrease for densely vegetated areas before increasing more than 5years later. For medium categories, the vegetation density increased by 22% in 2020 and almost 26% in 2015. Meanwhile, dense vegetation grew by 19% and 13%, respectively, according to data for 2015 and 2020. In a similar outlook, the increasing vegetation density has demonstrated that the LST across Banda Aceh is dominated by 27-30°C, with 2005 data occupying the coldest area. The city's moderate moisture content covered 85.43% of its surface. The low to medium vegetation density, LST, and LSM indexes from 2000 to 2010 is shown by overlaying a combination of three spatial extent indexes. It is shown that Banda Aceh's medium-to-high density, medium LSM, and medium-to-high LST spatially. Banda Aceh's dynamic changes in vegetation, LST, and LSM over the past two decades have been proven in this research; the Banda Aceh's spatial variation in vegetation density, LST, and LSM is influenced by the tsunami.

Helical graphs to visualize the NDVI temporal variation of forest vegetation in an open source space

Global change caused by human activity (e.g., land fragmentation, deforestation, pollution, anthropization of natural landscapes) has several effects on the biomes of the Earth, leading to alterations in the functioning of ecological systems. In this context, remote sensing represents an important tool to assess ecosystem changes, as it allows to collect a huge amount of data at different temporal and spatial resolutions concerning various compartments of the Earth system (land, ocean, atmosphere, and cryosphere). This information can be used to estimate precipitation patterns, global temperatures, snow cover and aerosol concentrations. The aim of this work is to exploit this wide availability of data to display the ecosystem changes using a new visualization method: the helical graphs. The helical graphs represent the change of a variable over time, reporting on the y-axis its moving averages and on the x-axis its rates of change.These new charts were tested on the NDVI index retrieved from Google Earth Engine (https://earthengine.google.com/) to visualize trends on selected biomes of the Earth (tropical and boreal forests). The results show that the helical graphs are a useful tool to highlight trends that might not be easily detected in a time series. In conclusion, the helical graphs can have a lot of application in ecology, especially exploiting the wide amount of data available thanks to the remote sensing.

Analysis of the plant physiological effects of late, artificial corn smut infestation using remote sensing methods

In Hungary, corn is also infected by several important pathogens. In this experiment, we analysed the plant physiological effects of artificial late cron smut infestation using remote sensing methods under field conditions We examined the experimental area from which the data comes from with a DJI Phantom 4 Multispectral Drone NDVI and NDRE indices were calculated and analyzed in GIS programs. Individuals treated with a higher dose remained much greener than the untreated control. They also showed significant differences within the indices used.

Использование главных компонент для построения регрессионных моделей оценки средней районной урожайности озимой пшеницы для отдельных областей Центрально-Черноземного УГМС

The results of the forecast model development based on the principal component analysis for calculating the expected winter wheat yield for the regions of the Central Chernozem Administration for Hydrometeorology and Environmental Monitoring (AHEM) were shown. Satellite, meteorological and statistical data were used as input information. Satellite data included the NDVI and VCI indices, and meteorological data included average air temperature for one and three 10-day periods, total precipitation per one and three 10-day periods, average air humidity deficit for one and three 10-day periods, Selyaninov hydrothermal coefficient. The results of calculating the average winter wheat yield for the selected groups of separate regions on the territory of the Central Chernozem AHEM were presented. The calculations were based on the integration of satellite and ground-based information for the period from 2012 to 2021 and on the developed forecast models using the principal component analysis. Key words: satellite data, principal component analysis, orthogonal projections to latent structures, winter wheat, average regional yield

A Comparison of Different Machine Learning Algorithms in the Classification of Impervious Surfaces: Case Study of the Housing Estate Fort Bema in Warsaw (Poland)

The aim of this study is to extract impervious surfaces and show their spatial distribution, using different machine learning algorithms. For this purpose, geoprocessing and remote sensing techniques were used and three classification methods for digital images were compared, namely Support Vector Machines (SVM), Maximum Likelihood (ML) and Random Trees (RT) classifiers. The study area is one of the most prestigious and the largest housing estates in Warsaw (Poland), the Fort Bema housing complex, which is also an exemplary model for hydrological solutions. The study was prepared on the Geographic Information System platform (GIS) using aerial optical images, orthorectified and thus provided with a suitable coordinate system. The use of these data is therefore supported by the accuracy of the resulting infrared channel product with a pixel size of 0.25 m, making the results much more accurate compared to satellite imagery. The results of the SVM, ML and RT classifiers were compared using the confusion matrix, accuracy (Root Mean Square Error /RMSE/) and kappa index. This showed that the three algorithms were able to successfully discriminate between targets. Overall, the three classifiers had errors, but specifically for impervious surfaces, the highest accuracy was achieved with the SVM classifier (the highest percentage of overall accuracy), followed by ML and RT with 91.51%, 91.35% and 84.52% of the results, respectively. A comparison of the visual results and the confusion matrix shows that although visually the RT method appears to be the most detailed classification into pervious and impervious surfaces, the results were not always correct, e.g., water/shadow was detected as an impervious surface. The NDVI index was also mapped for the same spatial study area and its application in the evaluation of pervious surfaces was explained. The results obtained with the GIS platform, presented in this paper, provide a better understanding of how these advanced classifiers work, which in turn can provide insightful guidance for their selection and combination in real-world applications. The paper also provides an overview of the main works/studies dealing with impervious surface mapping, with different methods for their assessment (including the use of conventional remote sensing, NDVI, multisensory and cross-source data, ‘social sensing’ and classification methods such as SVM, ML and RT), as well as an overview of the research results.

Determining the Effect of Urbanization on Agricultural Lands Using Remote Sensing and Geographic Information System Techniques

Bu çalışmanın amacı, yıldan yıla tarımsal arazi varlığında önemli derecede daralma meydana gelen Türkiye’de; Konya ili Selçuklu ilçesinde, Uzaktan Algılama (UA) yöntemleri ve Coğrafi Bilgi Sistemleri (CBS) kullanılarak, kentleşmenin farklı yıllardaki alansal değişimi ve bu değişimin tarım arazileri üzerindeki etkisini belirlemeye yöneliktir. Çalışmada, Landsat 5-8 uydu görüntülerinde, CBS tabanlı olarak, NDVI ve NDBI indeksleri hesaplanmış; 2001, 2014 ve 2021 yıllarına ait, kentsel ve tarımsal alanlardaki değişimin görselleştirilmesi amacıyla dağılım haritaları hazırlanmıştır. Buna göre, 2001 yılında 79300.63 hektar olan tarımsal alanlarının, 2021 yılında 50167.97 hektara gerilediği, 2001 yılında 9265.19 hektar olan kentsel alanların 2021 yılında 15690.5 hektarlık alana yayıldığı tespit edilmiştir. Ayrıca çalışma alanına ait arazi yetenek sınıfları ve toprak dağılım haritaları oluşturularak, yerleşime uygun olmayan alanlar tespit edilmiştir. Sonuç olarak; sektörel anlamda bir tarafta kazanımlar var iken, diğer tarafta kayıplar söz konusudur. Bu nedenle kentsel alanlardaki genişlemenin, özellikle tarım alanlarının daralması anlamına geldiği, son 20 yıl içinde kentsel alanların % 3.35 oranında büyüdüğü, tarım alanlarında da % 15.18 oranında bir kayıp yaşandığı belirlenmiştir. Ayrıca alüvyal arazilerin ve tarımsal üretim yapılan arazilerin üzerinde yerleşim yerlerinin ve diğer yapay alanların yayıldığı tespit edilmiştir.

Remote sensing for monitoring the impacts of agroforestry practices and precipitation changes in particle size export trends

Recent land use changes, the absence of soil protection between crop periods, and extreme precipitation events have been highlighted as major influential factors in the fluctuations of sediment export in the last decades at the catchment scale worldwide. In this regard, soil erosion and fine-particle export are two of the major concerns of soil nutrient loss and water-quality decrease (e.g., increasing turbidity and vector of chemicals). However, while rainfall effects have been well-monitored, recent land use changes and management need additional approaches to evaluate their effect. In Mediterranean mountainous environments, in addition to forest management, agricultural practices during different cropland stages likely increase sediment and particle-bound chemicals in the drainage system. Moreover, most catchments lack instrumentalization. Thus, there is a gap in the knowledge on the processes influencing the sediment exported in ungauged catchments. To evaluate the processes involved, remote sensing and seasonal sampling of suspended sediments were examined for 5 years in a representative agroforestry system in three sub-catchments (SBCs) with different proportions of land uses. Temporal trends of NDVI, EVI, MSAVI, SAVI, and NDWI indices were analyzed for monitoring the vegetation status. With this information, we attempt to evaluate the soil response in terms of particle size export to land use change, vegetation status, and precipitation distribution in fine-grained sediment-reaching streams. Our findings not only highlight the significant effect of heavy precipitation events and vegetation cover on the grain-size fraction of the exported sediment but also reveal the existence of more complex factors influencing the export dynamics. A silt-increasing trend due to the increase of individual heavy precipitations from 2017 onward despite the total precipitation amount not increasing was detected. It is shown that indices such as NDVI and NDMI help detect small changes in vegetation cover, while EVI, SAVI, and MSAVI are more robust for detecting general patterns in large vegetated areas and preventing the appearance of artefacts in the data. Results from this study suggest that land use changes combined with short-scale changing trends of rainfall likely explain most of the possible effects observed in terms of sediment export changes.

Mapping and Monitoring Forest Plantations in São Paulo State, Southeast Brazil, Using Fraction Images Derived from Multiannual Landsat Sensor Images

This article presents a method, based on orbital remote sensing, to map the extent of forest plantations in São Paulo State (Southeast Brazil). The proposed method uses the random forest machine learning algorithm available on the Google Earth Engine (GEE) cloud computing platform. We used 30 m annual mosaics derived from Landsat-5 Thematic Mapper (TM) images and from Landsat-8 Operational Land Imager (OLI) images for the 1985 to 1995 and 2013 to 2021 time periods, respectively. These time periods were selected based on the planted areas’ rotation, especially the eucalypt’s short rotation. To classify the forest plantations, green, red, NIR, and MIR spectral bands, NDVI, GNDVI, NDWI, and NBR spectral indices, and vegetation, shade, and soil fractions were used for both sensors. These indices and the fraction images have the advantage of reducing the volume of data to be analyzed and highlighting the forest plantations’ characteristics. In addition, we also generated one mosaic for each fraction image for the TM and OLI datasets by computing the maximum value through the period analyzed, facilitating the classification of areas occupied by forest plantations in the study area. The proposed method allowed us to classify the areas occupied by two forest plantation classes: eucalypt and pine. The results of the proposed method compared with the forest plantation areas extracted from the land use and land cover maps, provided by the MapBiomas product, presented the Kappa values of 0.54 and 0.69 for 1995 and 2020, respectively. In addition, two pilot areas were used to evaluate the classification maps and to monitor the phenological stages of eucalypt and pine plantations, showing the rotation cycle of these plantations. The results are very useful for planning and managing planted forests by commercial companies and can contribute to developing an automatic method to map forest plantations on regional and global scales.

ASSESSING LAND USE AND LAND COVER CHANGE IMPACT ON FLASH FLOODS IN KASARANI CONSTITUENCY, KENYA

Kasarani Constituency in Nairobi County, Kenya is one of the areas that experienced frequent flash floods over the last decade. Anthropogenic activities such as the construction of roads, buildings, and industrialization are identified as key drivers of change in LULC. The aim of this study was to assess the impact of LULC change on flooding in the Kasarani constituency by comparing Spectral indices from Landsat images captured before, during, and after floods between 2013 and 2020. The value of NDVI and MSAVI indices declined during flood days indicating that the vegetation was covered with water while the NDMI value increased during flood days indicating a water-logged surface. The Digital Elevation Model of the study area was created to identify the low-lying areas and river channels that were identified as high flood risk areas. In order to reduce vulnerability to frequent floods, there is a need to identify safe areas for evacuation as well as raised areas for settlement.

The method of forest change detection using Sentinel-2 optical satellite imagery and Sentinel-1 radar imagery: A case study in Dak Nong Province, Vietnam

Abstract. Bao TQ, Thi NV, Doanh LS, Duan PV, Khang LN, Cuong NT, Hieu BT, Mai DTT, Tuyen DV. 2022. The method of forest change detection using Sentinel-2 optical satellite imagery and Sentinel-1 radar imagery: A case study in Dak Nong Province, Vietnam. Biodiversitas 23: 4800-4809. This study presents the findings of forest change detection in Dak Nong Province, Vietnam by combining the NDVI of Sentinel-2 satellite imagery and the backscatter (BKS) values of VH and VV polarizations from the first quarter of 2020 to the first quarter of 2021 and using the combination model of BKS and NDVI (CMB) to determine the NDVI and Backscatter Change Index (NBCI) between the two periods, which were processed and analyzed in Google Earth Engine. The verification results on 270 samples for all 3 indices show that the results of forest loss detection using NBCI index, NDVI index, and BKS index reach 93.3%, 83.3% and 73.3%, respectively; the results of identifying the areas with no forest change using NBCI index, NDVI index, and BKS index achieve 97.8%, 92.2% and 83.3%, respectively; the results of forest increase detection using NBCI index, NDVI index, and BKS index reach 90.0%, 85.6% and 68.9%, respectively. The NBCI index for Dak Nong Province indicated that during the study period, the the province lost 1,198.9 hectares forest area, whereas it gained 871.3 hectares of the same.

Landsat ETM+ Uydu Görüntüleri ile Metal Kaynaklı Bitki Stresinin Araştırılması

Remote sensing technology is used in many areas today, facilitating spatial analysis operations under difficult conditions. This technology offers solutions in different fields such as forest degradation, land classification, change analysis and mine detection. In particular, mine detection is of great importance for the economies of countries. Detecting mines with terrestrial measurement techniques in vegetated areas is a challenging situation, but mine sites can be easily detected, by using satellite images in a shorter time. When plants are exposed to heavy metals, anomalies occur and they cause reduction in the amount of chlorophyll. This can be observed as decrease in the reflectance values. In this study, it is aimed to detect the stress of the plants due to heavy metal by measuring the amount of the change in the reflectance values of plants in mining area and non-mining area. In this context, VIGS index is exploited on Landsat ETM+ satellite images belong to Cukuralan/Izmir and Kisladag/Usak regions for both gold mine and non-gold mine areas. Other vegetation indices (NDVI, GNDVI, BNDVI) were also computed on these sites when they were covered with vegetation. According to the evaluation results, it was seen that the difference between the values of the VIGS and NDVI indexes in the mine and not mine areas were higher, and therefore it was suggested that they could be used to determine the amount of anomaly in the wooded areas.

Natural hydrogen emanations in Namibia: Field acquisition and vegetation indexes from multispectral satellite image analysis

Natural hydrogen exploration is now active in various places of the world. Onshore, correlation between natural H2 generation and the presence of iron rich rocks especially from Archean and Neoproterozoic cratons have been observed. Emanations and accumulations of H2 have already been confirmed in such geological settings in Australia, South Africa and Brazil. The geological similitude and the presence of numerous sub circular depressions that are a good proxy for hydrogen emanations suggest that hydrogen resources may also exist in Namibia. We present here the results of a data acquisition campaign which allowed us to confirm the presence of natural hydrogen in this country in the vicinity of Neoproterozoic Banded Iron Formation. The H2 content in the soil, as in Brazil, is variable within the depressions in time and space and is particularly time sensitive across the day. Comparison of the H2 signal versus time within these two regions shows a similar behavior of the soils with an increase of the H2 flow at the middle of the day. In addition, these new data allow us to better constrain the morphological characteristics of such H2-emiting depressions. By using satellite images and digital elevation model we propose a new proxy to differentiate potentially H2-emiting features from other type of depressions such as Salt Pan. The Landsat multispectral images and their processing through NDVI and SAVI indexes, that highlight a ring of healthy vegetation around the sub circular area with scarce vegetation already observed appear able to discriminate between H2 emitting structures and other soft depressions.

Analisis Probability Distribution Function Terhadap Perubahan Tutupan Lahan Dan Fraksi Awan Di Indonesia

Land use exceeds the standard of suitability and availability over Indonesia, which causes uncontrolled land acquisitions every year. Changes in land use can be controlled by monitoring changes in land cover and the effect of land changes on hydrological cycle components such as cloud fraction. The land cover and cloud fraction distribution were identified using the NDVI index and cloud fraction parameters. The NDVI index value in Indonesia is 0.73 to 0.81, representing land cover in Indonesia, which is relatively high. Meanwhile, the cloud fraction showed the lowest value between 0.6 to 0.7 in summer and experienced the highest cloud cover at the peak of the rainy season. The analysis of the distribution of both land cover and cloud cover shows the consistency of value stability in the rainy season starting from November to May from 2003 to 2016. The correlation value based on the spatial analysis between the NDVI anomaly and the cloud fraction parameter anomaly has a value range of around -0. 8 to 0.8. The correlation between the NDVI anomaly and the cloud parameter anomaly has a negative correlation in Indonesia, including Sumatra, Kalimantan, Java and Bali, Sulawesi, Nusa Tenggara, Maluku, and Papua. Sumatra, Kalimantan, and Papua islands have a prominent role with a negative correlation between the NDVI anomaly and the cloud fraction parameter anomaly. It is feared to be caused by changes in land use to deforestation of natural area conservation areas into oil palm plantations and mining.

Exploring built environment factors on e-bike travel behavior in urban China: A case study of Jinan.

E-bike, characterized as a low-carbon and health-beneficial active travel mode, is gradually becoming popular in China. Although built environment factors are considered to be key parameters that can facilitate or hinder active transportation, such as cycling or walking, few studies have explored the impact of built environment on e-bikes. To fill this gap, this study was the first to explore the relationship between e-bike usage and built environment factors based on population level travel survey in central Jinan, China. Both macro and micro levels of built environment were measured using multi-source data. We employed ordinary least squares (OLS) and geographically weighted regression (GWR) models to explore the aggregation patterns of e-bike trips. Besides, the local Moran's I was employed to classify the aggregation patterns of e-bike trips into four types. The results from OLS model showed that eye-level greenery, building floor area, road density and public service POI were positive significantly related to e-bike trips, while open sky index and NDVI had negative association with e-bike trips. The usage of GWR model provided more subtle results, which revealed significant spatial heterogeneity on the impacts of different built environment parameters. Road density and public service POI posed positive effects on e-bike travel while NDVI and open sky index were found mainly pose negative impacts on e-bike travel. Moreover, we found similar coefficient distribution patterns of eye-level greenery, building floor area and distance to bus stop. Therefore, tailored planning interventions and policies can be developed to facilitate e-bike travel and promote individual's health level.