scholarly journals COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

2018 ◽  
Vol XLII-3 ◽  
pp. 1381-1387 ◽  
Author(s):  
M. Piragnolo ◽  
G. Lusiani ◽  
F. Pirotti
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Monitoring And Control ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.

scholarly journals MAPPING AND MONITORING WETLANDS USING SENTINEL-2 SATELLITE IMAGERY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 271-277 ◽  
Author(s):  
G. Kaplan ◽  
U. Avdan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Kappa Coefficient ◽  
Water Index ◽  
Object Based ◽  
The World ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Mapping and monitoring of wetlands as one of the world`s most valuable natural resource has gained importance with the developed of the remote sensing techniques. This paper presents the capabilities of Sentinel-2 successfully launched in June 2015 for mapping and monitoring wetlands. For this purpose, three different approaches were used, pixel-based, object-based and index-based classification. Additional, for more successful extraction of wetlands, a combination of object-based and index-based method was proposed. It was proposed the use of object-based classification for extraction of the wetlands boundaries and the use of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for classifying the contents within the wetlands boundaries. As a study area in this paper Sakarbasi spring in Eskisehir, Turkey was chosen. The results showed successful mapping and monitoring of wetlands with kappa coefficient of 0.95.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Application of Remote Sensing on El Niño Extreme Effect in Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)

2021 ◽  
Vol 6 (1) ◽  
pp. 46-56
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Remote Sensing ◽  
El Niño ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
El Nino ◽  
Extreme Temperature ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
The Impact

The years 1997/1998 and 2015/2016 saw the worst El Niño occurrence in human history. The occurrence of El Niño causes extreme temperature events which are higher than usual, drought and prolonged drought. The incident caused a decline in the ability of plants in carrying out the process of photosynthesis. This causes the carbon dioxide content to be higher than normal. Studies on the effects of El Niño and its degree of strength are still under-studied especially by researchers in the tropics. This study uses remote sensing technology that can provide spatial information. The first step of remote sensing data needs to go through the pre-process before building the NDVI (Normalized Difference Vegetation Index) and Normalized Difference Water Index (NDWI) maps. Next this study will identify the relationship between Oceanic Nino Index (ONI) with Application Remote Sensing in The Study Of El Niño Extreme Effect 1997/1998 and 2015/2016 On Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)NDWI and NDWI landscape indices. Next will make a comparison, statistical and spatial information space between NDWI and NDVI for each year 1997/1998 and 2015/2016. This study is very important in providing spatial information to those responsible in preparing measures in reducing the impact of El Niño.

scholarly journals Determination of Crop Health Monitoring in MPKV – Rahuri, Using Remote Sensing Approach

2021 ◽  
pp. 11-16
Author(s):  
Ankita P. Kamble ◽  
A. A. Atre ◽  
Payal A. Mahadule ◽  
C. B. Pande ◽  
N. S. Kute ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Health Monitoring ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Stress ◽  
Vegetation Indices ◽  
Remotely Sensed Data ◽  
Pests And Diseases ◽  
Crop Health ◽  
Sentinel 2

Pests and diseases cause major harm during crop development. Also plant stress affects crop quality and quantity. Recent developments in high resolution remotely sensed data has seen a great potential in mapping cropland areas infected by pests and diseases, as well as potential vulnerable areas over expansive areas. Crop health monitoring in this study was carried out using remote sensing techniques. The present study was carried out in MPKV, Rahuri, Ahmednagar District, Maharashtra. Vegetation indices like Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to classify the crops into healthy and dead or unhealthy one. Sentinel-2 image data from October 2019 to January 2020 processed in Arc GIS 10.1 were used for this study. Vegetation is a key component of the ecosystem and plays an important role in stabilizing the global environment. The result showed that the average vegetation cover was decreased in the month of November and healthy vegetation was found more in month of October as compared to December and January. This shows that NDVI and SAVI indices for Sentinel-2 images can be used for crop health monitoring.

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

2021 ◽  
Vol 4 (2) ◽  
pp. 81
Author(s):  
Annisa Rizky Kusuma ◽  
Fauzan Maulana Shodiq ◽  
Muhammad Faris Hazim ◽  
Dany Puguh Laksono
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ground Water ◽  
Water Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Water Level ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

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%.

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

2021 ◽  
Vol 912 (1) ◽  
pp. 012089
Author(s):  
B Slamet ◽  
O K H Syahputra ◽  
H Kurniawan ◽  
M Saraan ◽  
M M Harahap
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Health Condition ◽  
Land Conversion ◽  
Water Index ◽  
The North ◽  
Normalized Difference Water Index ◽  
North Sumatra ◽  
Sentinel 2

Abstract 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.

scholarly journals Assessing damages of agricultural land due to flooding in a lagoon region based on remote sensing and GIS: case study of the Quang Dien district, Thua Thien Hue province, central Vietnam

2020 ◽  
Vol 12 (2) ◽  
pp. 100-107
Author(s):  
Ngoc Bich NGUYEN ◽  
Ngu Huu NGUYEN ◽  
Duc Thanh TRAN ◽  
Phuong Thi TRAN ◽  
Tung Gia PHAM ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Viet Nam ◽  
Enhanced Vegetation Index ◽  
Water Index ◽  
Land Surface Water ◽  
Central Vietnam ◽  
Sentinel 2

This study aims to create a flood extent map with Sentinel imagery and to evaluate impacts on agricultural land in the lagoon region of central Vietnam. In this study, remote sensing images, obtained from 2017 to 2019, were used to simultaneously map the land cover status of a flood in the Quang Dien district. This study highlights flooded areas from Sentinel-2 images by calculating some indicators such as the Land Surface Water Index (LSWI) and the Enhanced Vegetation Index (EVI). Comparisons between the floodplain samples (GPS point-based) and flood mapping results, with the ground-truth data, indicate that the overall accuracy and Kappa coefficients were 97.9% and 0.62 respectively for 2017; the values for 2019 were 95.7% and 0.77 for the same coefficients. Land use maps overlying the flood-affected maps show that approximately 11% of the agriculture land area was affected by floods in 2019 comparison to a 10% in 2017. Wet rice was the most affected crop with the flooded area accounting for more than 70% of the district under each flood event. The most affected communes are: Quang An, Quang Phuoc and Quang Thanh. This study provides valuable information for flood disaster planning, mitigation and recovery activities in Vietnam. Mục tiêu của nghiên cứu là lập bản đồ phân bố ngập lụt với hình ảnh vệ tinh Sentinel và đánh giá ảnh hưởng ngập lụt đến sử dụng đất nông nghiệp ở vùng đầm phá miền Trung, Việt Nam. Trong nghiên cứu này, ảnh viễn thám thu nhận giai đoạn 2017- 2019 được sử dụng để xây dựng bản đồ hiện trạng sử dụng đất tại thời điểm bị ngập nước trên địa bàn huyện Quảng Điền. Nghiên cứu đã xác định được vùng ngập lụt ở huyện Quảng Điền bằng phương pháp phân loại chỉ số mặt nước (Land Surface Water Index – LSWI) và chỉ số khác biệt thực vật (Enhanced Vegetation Index-EVI) từ ảnh Sentinel-2. Xác định vùng nước lũ bị che khuất bởi mây bằng mô hình số hóa độ cao (DEM). Kết quả phân loại vùng ngập lụt được so sánh với giá trị tham chiếu mặt đất cho thấy độ chính xác tổng thể và hệ số Kappa đạt được trong năm 2017 là 97,9% và 0,62; trong khi năm 2019 đạt 95,7% và 0.77. Bản đồ sử dụng đất chồng lên bản đồ lũ lụt cho thấy khoảng 11% diện tích đất nông nghiệp bị ảnh hưởng bởi lũ lụt năm 2019 so với 10% năm 2017. Cây lúa nước là cây trồng bị ảnh hưởng nặng nề nhất, với diện tích bị ngập lụt chiếm hơn 70% diện tích lúa của huyện. Các xã bị ngập lớn là xã Quảng An, Quảng Phước và Quảng Thành. Nghiên cứu này cung cấp thông tin có giá trị cho các hoạt động lập kế hoạch, giảm nhẹ và phục hồi thiên tai lũ lụt ở Việt Nam.

scholarly journals Use of aerial image in the estimation of volume and biomass of Eucalyptus sp. forest stand

2020 ◽  
pp. 286-294
Author(s):  
Thallita R. S. Mendes ◽  
Eder P. Miguel ◽  
Pedro G. A. Vasconcelos ◽  
Marco B. X. Valadão ◽  
Alba V. Rezende ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Basal Area ◽  
Aerial Image ◽  
Biomass Estimation ◽  
Fit Statistics ◽  
Remote Sensing Techniques ◽  
The Individual

Assessing forest stands is crucial for managing and planning the use of these resources. Forest inventory is the instrument that provides information about the stand situation, which can be costly and time consuming. In order to facilitate and reduce the time spent obtaining these data, the main objective of this work was to evaluate the accuracy of volume and biomass estimates per unit area with data from remote sensing. Forty sample units were allocated and georeferenced, in which all trees with diameter at breast height (DBH) ≥ 5 cm were inventoried. Sequentially, the cubage was performed in order to obtain individual biomass, volume, and adjustment of the individual models. With data from georeferenced images of the study area, the vegetation indices MSAVI (Modified Soil-Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index) were obtained. The volume and biomass estimation using remote sensing variables were carried out through the adjustment of sigmoidal models by regression analysis, which used a combination of the average values of the vegetation indices and the basal area of the plot/hectares as an independent variable. The fit statistics and the accuracy of the tested models presented consistent results to estimate forest production. The results showwd that indices derived from remote sensing techniques associated with forest variables information could accurately estimate the volume and biomass of Eucalyptus spp. plantations.

scholarly journals Integration of Sentinel-2 Derived Spectral Indices and In-situ Forest Inventory to Predict Forest Biomass

2021 ◽  
Vol 64 (2) ◽  
pp. 119-130
Author(s):  
Areeba Binte Imran ◽  
Samia Ahmed ◽  
Waqar Ahmed ◽  
Muhammad Zia-ur-Rehman ◽  
Arif Iqbal ◽  
...  
Keyword(s):  
Linear Regression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Biomass ◽  
Above Ground Biomass ◽  
Stepwise Linear Regression ◽  
Water Index ◽  
Global Environmental ◽  
Normalized Difference Water Index ◽  
Sentinel 2

  Forest biomass estimation is the central part of sustainable forest management to assess carbon stocks and carbon emissions from forest ecosystem. Sentinel-2 is state-of-art sensor with refined spatial and recurrent temporal resolution data. The present study explored the potential of Sentinel-2 derived vegetation indices for above ground biomass prediction using four regression models (linear, exponential, power and logarithmic). Sentinel-2 indices includes Global environmental monitoring index, transformed normalized difference vegetation index, normalized difference water index, normalized difference infrared index and red-edge normalized difference vegetation index. The performances of Sentinel-2 indices were assessed by simple single variable (index) based regression for GEMI, TNDVI, NDII, NDWI and RENDVI versus AGB values. Further, stepwise linear regression was also developed in which used all indices entered into stepwise selection and the best index was selected in the final model. Results showed that linear model of all indices performance best compared to the rest three models and R2 values 0.12, 0.39, 0.46, 0.44 and 0.37 for Global environmental monitoring index, transformed normalized. Vegetation index, normalized difference water index, infrared index and red-edge vegetation index, respectively. Normalized difference water index was considered the best index among five computed indices in simple linear as well as in stepwise linear regression, whereas rest of the indices were removed because they were not significant under the stepwise criteria. Further, the accuracy of normalized difference water index model was determined by root mean square error and final prediction model has 28.27 t/ha error for both simple linear and stepwise linear regression. Therefore, normalized difference water index was selected for biomass mapping and resultant biomass showed up to 339 t/ha in the study area. The resultant biomass map also showed consistency with global datasets which include global forest canopy height and global forest tree cover change maps. The study suggest that Sentinel-2 product has great potential to estimate above ground  biomass with accuracy and can be used for large scale mapping in combination with national forest inventory for carbon emission accounting.    

scholarly journals A Sentinel based agriculture monitoring scheme for the control of the CAP and food security

2021 ◽  
Author(s):  
Antonios Koutroumpas ◽  
Vasileios Sitokonstantinou ◽  
Thanassis Drivas ◽  
Alkiviadis Koukos ◽  
Vassilia Karathanassi ◽  
...  
Keyword(s):  
Decision Making ◽  
Food Security ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Parameter Tuning ◽  
Rice Paddy ◽  
Global Food Security ◽  
Water Index

Effective and efficient control of the agrarian obligations imposed by the Common Agricultural Policy (CAP) and the high-level decision making for national and global food security, requires systematic and timely monitoring of the agricultural land. In this study we focus on rice paddy monitoring in South Korea to ultimately deliver food security related information. Food security monitoring demands knowledge at large scales to allow for decision making at the highest level. In this work, we monitor the growth of rice using the TIMESAT solution on a time-series of Normalized Difference Vegetation Index (NDVI), extracting useful metrics with reference to the phenological phases of the crop, but also biomass and yield indicators. TIMESAT requires user provided parameters to define the start and the end of season to then compute the relevant metrics. In order to automate this procedure, the vegetation indices Normalized Difference Water Index (NDWI) and Plant Senescence Reflectance Index (PSRI) are used to develop a data based parameter tuning for TIMESAT.

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