NDVI e SAVI como ferramentas de monitoramento das modificações no uso e ocupação do solo no sudoeste paraense

Sensoriamento Remoto é um uma tecnologia que permite aquisição de informações sobre áreas ou objetos sem manter contato físico. Esse trabalho objetivou utilizar imagens de satélites passivos, por meio dos índices de cobertura vegetal, como o Índice de Vegetação por Diferença Normalizada (NVDI) e Índice de Vegetação Ajustado para o Solo (SAVI), nos anos de 2008 e 2018, para identificar as modificações sofridas em 10 anos da comunidade Comunidade Linha Gaúcha localizada no município de Novo progresso no estado do Pará. Para este trabalho, foram utilizados dados provenientes do IBAMA, como a localização espacial da Comunidade e imagens da plataforma United States Geological Survey (USGS), para os anos de 2008(Landsat 5 – TM) e 2018 (Landsat 8 – OLI). Por meio do método de NDVI e SAVI foi possível analisar a expansão urbana em torno da comunidade num raio de 50 km, assim como observar a intensa modificação no uso e ocupação do solo, estando este fato intimamente ligado à presença da rodovia Transamazônica, importante agente de crescimento na Amazônia.

Soil Moisture Assessment Using Soil Moisture Index (SMI) Method at the Bangelan Coffee Plantation, Malang Regency, East Java

Water is one of the limiting factors in the growth of coffee plants. If plants experience a lack of water, it can inhibit plant growth and, at a critical level, can lead to drought stress and plant damages. The available soil water to plants can be estimated from the level of soil moisture index. The monitoring of soil moisture status can be used in improving the management of coffee plantations. Soil Moisture Index (SMI) is a method that can be used to estimate the level of soil moisture using remote sensing technology using NDVI and LST values. The purpose of this study was to analyze the status and distribution of soil moisture at the coffee plantation; analyze the relationship between vegetation index and soil moisture; and analyzed the relationship between soil moisture status using the SMI method and soil moisture measured in coffee plantations. Results showed that the soil moisture index obtained from Landsat 8 OLI/TIRS image processing had an average value of 0.60. The average soil moisture index at the study site is 1.05. Soil moisture index from the Landsat 8 OLI/TIRS image has a significant positive effect on soil moisture at the study site (y = 7.4996x – 3.4789; R2 = 0.7146**). It is suggested that the SMI method can be used to estimate soil moisture in the coffee plantation.

Land cover classification using Landsat 8 OLI in West Langsa Sub district, Langsa City

Land cover information is needed by various parties as a consideration in controlling land cover changes. The latest land cover information can be obtained using remote sensing techniques in the form of image classification maps. This technique is very effective in monitoring land cover because of its ability to quickly, precisely, and easily provide spatial information on the earth’s surface. The purpose of this study was to classify land cover in West Langsa Sub district, Langsa City using Landsat 8 OLI (Operational Land Imager) imagery. The classification method used in this study is the maximum likelihood classification (MLC) method. There are several considerations of various factors in the MLC method, including the probability of a pixel to be classified into a certain type or class. The results of Landsat 8 OLI image classification in West Langsa Sub district resulted in 6 land cover classes, namely mangrove forests, settlements, rice fields, shrubs, ponds and bodies of water. The largest land cover class is ponds with an area of 1981.54 ha (38.71%) and the smallest land cover is rice fields with an area of 115.58 ha (2.26%) of the total land cover class. Classification accuracy is indicated by the overall accuracy and kappa accuracy of 91.15% and 82.75%, respectively. These results meet the requirements set by the USGS (Overall Accuracy > 85%) and indicate that the Landsat 8 OLI image classification map can be used for various purposes.

Post-Fire Vegetation Recovery in Elwasita Area of Al-Jabal Al-Akhdar-Libya - Based on Remote Sensing Information

Satellite-based remote sensing technologies and Geographical Information Systems (GIS) present operable and cost-effective solutions for mapping fires and observing post-fire regeneration. Elwasita wildfire, which occurred during April and May in 2013 in Libya, was selected as a study site. This study aims to monitor vegetation recovery and investigate the relationship between vegetation recovery and topographic factors by using multi-temporal spectral indices together with topographical factors. Landsat 8 (OLI and TIRS) images from different data were obtained which were for four years; April 2013, June 2014, July 2015, and July 2016, to assess the related fire severity using the widely-used Normalized Burn Ratio (NBR). Normalized difference Vegetation Index (NDVI) was used to determine vegetation regeneration dynamics for four consecutive years. Also, the state of damage, vegetation recovery and, damage dimensions about the burned area were capable of being effectively detected using the result of supervised classification of Landsat satellite images. In addition, aspect, slope, and altitude images derived from Digital Elevation Model (DEM) were used to determine the fire severity of the study area. The results have found that it could be possible to figure out the degree of vegetation recovery by calculating the NDVI and NBR using Landsat 8 OLI and TIRS images. Analysis showed that it mainly oriented towards the northwest (47%), north (29%), and northeast (12%). The statistical analysis showed that fire was concentrated on the incline by 76%, and the most affected areas are those between 200 m-450 m above sea level, with a percentage of 80%. It is expected that the information can be acquired by various satellite data and digital forests. This study serves as a window to an understanding of the process of fire severity and vegetation recovery that is vital in wildfire management systems.

Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible.

Uzaktan Algılama Yöntemleri Uygulanarak Namak Gölü'nün Su Yüzeyindeki Değişimin Su İndeksleri (AWEI, MNDWI, NDWI VE WRI) Aracılığıyla Belirlenmesi

Namak Gölü, İran’da bulunan Urmiye Gölü, Hazar Denizi ve diğer su kütlelerini oluşturan Paratetis denizinin bir kalıntısıdır. Göl, küçük bir tuz gölü olmanın yanı sıra deniz seviyesinden 790 metre yükseklikte yer almakta ve Kum (Qom) nehri tarafından beslenmektedir. Bununla birlikte, son yıllarda kuraklığın etkisiyle, azalan yüzey suyu ve artan tuzluluk oranı gölün kurumaya yüz tutmasına neden olmuştur. Bu çalışmada, 2001-2021 yılları arasında -belirlenen onar yıllık üç dönemde- Namak Gölü'nün mekânsal-zamansal değişimleri; Landsat 5-TM, Landsat 7-ETM+ ve Landsat 8-OLI görüntüleri kullanılarak hesaplanmıştır. Çalışmada, yüzey suyunun belirlenmesini sağlayan Normalleştirilmiş Fark Su İndeksi (NDWI), Modifiye Edilmiş Fark Su İndeksi (MNDWI), Su Oranı İndeksi (WRI) ve Landsat verilerinden yüzey suyunun çıkarılmasına imkân veren Otomatik Su Çıkarma İndeksi (AWEI) incelenmiştir. Sonuç olarak, 20 yıllık dönemde meydana gelen su yüzeyindeki değişiklikler alansal olarak (km²) karşılaştırılmış ve doğruluk oranı görece yüksek olan NDWI indeksinin, diğer indekslere göre yüzey suyunun belirlenmesinde daha faydalı bir yöntem olarak kullanılabileceği belirlenmiştir.