Study on Monitoring Water Area in Irrigation Area by Local Space Self-correlation Index

Landsat remote sensing image is a widely used data source in water remote sensing. Normalized difference water index (NDWI), modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) are commonly used water extraction classifiers. In the process of their application, because the threshold varies with the location and time of the research object, how to select the threshold with the highest classification accuracy is a time-consuming and challenging task. The purpose of this study was to explore a method that can not only improve the accuracy of water extraction, but also provide a fixed threshold, and can meet the requirements of automatic water extraction. We introduced the local spatial auto correlation statistics and calculate the Getis-Ord Gi* index to have hot spot analysis. Comparative analysis showed that the accuracy of water classification had been greatly improved through hot spot analysis. AWEIsh classifier had the best classification accuracy under the condition of INVERSE_DISTANCE neighborhood rule and Z>1.96, and the accuracy changes least in different time, different location and different vegetation coverage images. Therefore, in the process of regional water extraction, hot spot analysis method was effective, which was helpful to improve the accuracy of water extraction.

Hasil Studi Pola Kebakaran Lahan Gambut melalui Citra Satelit Sentinel-2 dengan Pengimplementasian Machine Learning Metode Random Forest : Kajian Literatur

Kebakaran lahan gambut merupakan peristiwa yang sulit diprediksi perilakunya. Karakteristik tanah gambut yang kompleks dan faktor-faktor alam lain seperti arah angin, status vegetasi, dan kandungan air membuat kasus ini menjadi salah satu kasus menarik yang masih menjadi objek penelitian yang belum tuntas hingga saat ini. Ketika memasuki musim kemarau kondisi kadar air di dalam tanah gambut akan semakin berkurang, maka potensi terjadinya kebakaran akan semakin tinggi. Pada studi ini dilakukan analisis faktor penyebab kebakaran dengan area cakupan yang luas melalui satelit Sentinel-2. Citra satelit yang diperoleh nantinya akan diolah oleh machine learning untuk memprediksi penyebaran api. Hasil literatur yang telah dilakukan diperoleh bahwa Ground Water Level (GWL), kematangan gambut, suhu, curah hujan dan kelembaban, serta kerapatan vegetasi dapat diidentifikasi melalui perhitungan indeks. Indeks yang digunakan diantaranya indeks Differenced Normalized Difference Vegetation Index (dNDVI) dan Normalized Difference Water Index (NDWI) yang diolah dengan algoritma machine learning metode Random Forest memilki akurasi mencapai 96%.

Procjena opsega poplave daljinskim istraživanjima

U radu su, na primjeru poplave koja je u svibnju 2014. godine zadesila istočnu Hrvatsku, uspoređene tri metode kartiranja i procjene opsega poplavljenog područja: metoda analize refleksije s površine u blisko infracrvenom (IC) dijelu spektra (jednokanalna metoda) te metode vegetacijskog indeksa NDVI (Normalized Difference Vegetation Index) i vodenog indeksa NDWI (Normalized Difference Water Index). Metode kao ulazne podatke koriste snimke snimljene pasivnim senzorom ugrađenim na satelitsku platformu Landsat 8. Analizirane su četiri snimke; snimljene su prije (jedna snimka), tijekom (jedna snimka) i nakon poplave (dvije snimke). Procjena temeljena na jednokanalnoj metodi rezultirala je površinom manjom od površina procijenjenih primjenom višekanalnim metodama. Rezultati se mogu objasniti kompleksnošću spektralnog potpisa plitkih poplavnih voda s visokim udjelom suspendiranog nanosa koji će utjecati na refleksiju takvih površina u blisko IC dijelu spektra i klasificirati ih kao nevodene površine. S druge strane, kombiniranjem različitih spektralnih kanala u višekanalnim metodama kompenzira se utjecaj suspendiranog nanosa na refleksiju takvih voda te je klasifikacija na vodene i nevodene površine preciznija.

Apport des images LANDSAT à l’étude de l’évolution de l’occupation du sol dans la plaine de SAÏSS au MAROC, pour la période 1987-2018

Cet article concerne la plaine de Saïss au Maroc et porte sur l’évolution de l’occupation et de l'utilisation des sols pour la période allant de 1987 à 2018. Cette plaine s’avère très importante au niveau économique pour le pays. La méthodologie adoptée comporte successivement le calcul d’indices spectraux à partir d’images Landsat (NDVI : Normalized Difference Vegetation Index, NDWI : Normalized Difference Water Index et NDBI : Normalized Difference Built-up Index), puis l’utilisation de l’algorithme de vraisemblance afin de réaliser quatre classifications thématiques pour les années 1987, 2003, 2014 et 2018. La précision globale de ces classifications est déterminée à partir de la matrice de confusion, et varie entre 83 et 87% ; le coefficient kappa est, pour les quatre années, supérieur à 0,80. Entre 1987 et 2018, les surfaces correspondant aux terres irriguées, aux oliviers et au milieu urbain, ont progressé respectivement de 123%, 136% et 115%. À l’inverse, les forêts, les parcours et les terres arables ont vu leur surface diminuer respectivement de 10%, 6% et 29%.

Estimation of changes in the Dead Sea surface water area through multiple water indices and geospatial techniques

<p>The study calculated the changes in the Dead Sea surface area from 1984 to 2019. The satellite images of 1985, 1990, 2000, 2010, and 2019 were classified by applying four different methods to estimate the changes in the Dead Sea surface water area. The methods included normalized difference water index (NDWI), modified normalized differences water index (MNDWI), automated water extraction Index (AWEI), and ISO cluster unsupervised classification. The results revealed a decrease of 76.63 sq. km area that accounts for an average of 11.27% sea area. The statistical model predicted that the Dead Sea surface area will shrink by half within the next 143 years, and the sea will be completely dried by 2305 if appropriate measures are not taken by decision-makers to avoid further reduction of the surface area.</p>

Dynamics of Lower-Barun Glacier and Glacial Lake and its GLOF Susceptibility Using Geospatial Analysis and Modelling

Shrinkage of some of the glaciers has direct impacts on the formation and expansion of glacial lakes. Sudden glacial lake outburst floods (GLOFs) are a major threat to lives and livelihoods downstream as they can cause catastrophic damage. In this study, we present the dynamics of the Lower-Barun glacier and glacial lakes and their GLOF susceptibility. We used multi temporal Landsat and Sentinel satellite imagery and extracted the lake outlines using the Normalized Difference Water Index (NDWI) with manual post-correction while the glacier outline was digitized manually. Multi-criteria decision-based method was used to assess the GLOF susceptibility. For the estimation of peak discharge and failure time, an empirical model developed by Froelich (1995) was used. The surface area of the Lower-Barun glacial lake was increased by 86% in the last 40 yrs (from 1979 to 2018), with a mean increase of 0.0432 km2/yr. The shrinkage in the glacier area is around 0.49 km2/yr and has shrunk by 8% in the last four decades. The retreat of the Lower-Barun glacier was 0.20% per year in the last four decades. The susceptibility index was 0.94, which suggests that the lake is very highly susceptible to the GLOF. The peak discharge of 5768 m3/s is produced when the breach depth is 20 m and the entire water volume is released. Likewise, in the case of 15 m breach depth, the peak discharge of 4038 m3/s is formed. Breach depth scenario of 10 m, peak discharge of 2442 m3/s is produced and in case of breach depth of 5 m produces the peak discharge of 1034 m3/s. If GLOF occurs, it can exert disastrous impacts on the livelihood and infrastructure in the downstream. So, it is necessary to examine such lakes regularly and mitigation measures to lower the GLOF susceptibility should be emphasized.

Drought Periods Identification in Ecuador between 2001 and 2018 Using SPEI and MODIS Data

Drought is a natural phenomenon in which the precipitation amount is below normal in a specific region over a long period. The main objective of this study is to identify periods of drought in Ecuador between 2001 and 2018 using the Standardized Precipitation Evapotranspiration Index (SPEI) and the Normalized Difference Water Index (NDWI) derived from MODIS data. Firstly, the SPEI at a six-month scale and the Runs theory were used to identify periods of drought. Secondly, the NDWI from MOD09A1 MODIS product was used to identify the areas affected by drought.

Damage assessment of chilli thrips using high resolution multispectral satellite data

Remote sensing technology offers an effective, rapid and reliable tool for assessing pest severity in vegetation. Ground based hyperspectral radiometry studies revealed significant difference in the reflectance spectra between healthy and thrip damaged vegetation. Space borne multispectral reflectance from Sentinel 2A satellite data of chilli thrip infested canopy has significant differences in red region (Band 4 – 664.6 nm), NIR region (Bands 5, 6, 7, 8 & 8A having central wavelengths at 704.1, 740.5, 782.8 & 832.8 nm, respectively) and SWIR region (Bands 11 & 12 having central wavelengths at 1613.7 and 2202.4 nm). In this study, an attempt was made to discriminate healthy and pest affected chilli crop in the multispectral satellite imagery using several multispectral vegetation indices. Of these, land surface water index, LSWI (p=0.018) and normalized difference water index, NDWI (p=0.001) were found significant. These indices were used to classify chilli fields in the satellite imagery into severe, moderate and healthy classes. Superior performance of LSWI over NDWI with overall accuracy of 93.80 and Kappa Coefficient of 0.89 was observed. Moran's Index was used to study the spatial distribution of chilli thrips and observed strong clustering (I= 0.9073, p=0.0001).

Analysis of vegetation cover and built-up areas in the Percut watershed landscape, North Sumatra Province using sentinel-2 imagery

Changes in land cover have an impact on the health condition of a watershed. This research was conducted by utilizing Sentinel-2 imagery for the recording period 2020 and 2021. Three indices were used in this study, namely, the Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). NDBI analysis indicates there is an increase in the built-up area of 2,092.62 hectares which means land conversion. NDWI classification shows an increase in the wetness area of 308.58 hectares, mainly occurring in the downstream part of the watershed, located to the north. There is an increase in the area of non-vegetated areas reaching 288.96 hectares in the Percut watershed based on the results of the NDVI analysis.

Migratory Birds Monitoring of India’s Largest Shallow Saline Ramsar Site with Big Geospatial Data Using Google Earth Engine for Restoration

Globally, saline lakes occupying 23% by area 44% by volume among all the lakes might desiccate by 2025 due to agricultural diversion, illegal encroachment, pollution, and invasive species. India&rsquo;s largest saline lake, Sambhar is currently shrinking at the rate of 4.23% due to illegal saltpan en-croachment. This research article aims to identify the trend of migratory birds and monthly wetland status. Birds survey was conducted for 2019, 2020 and 2021 and combined with literature data of 1994, 2003, and 2013 for visiting trend, feeding habit, migratory and resident ratio, and ecological diversity index analysis. Normalized Difference Water Index was scripted in Google Earth Engine. Results state that it has been suitable for 97 species. Highest NDWI values for the was whole study period was 0.71 in 2021 and lowest 0.008 in 2019 which is highly fluctuating. The decreasing trend of migratory birds coupled with decreasing water level indicates the dubious status for its existence. If the causal factors are not checked, it might completely desiccate by 2059 as per its future prediction. Certain steps are suggested that might help conservation. Least, the cost of restoration might exceed the revenue generation.