scholarly journals Analyzing Rainfall and Greenness Vegetation Level on Forest/Land Fire Area in Jambi and Central Kalimantan Provinces using Remote Sensing Data

2021 ◽  
Vol 893 (1) ◽  
pp. 012067
Author(s):  
Khalifah Insan Nur Rahmi ◽  
Indah Prasasti ◽  
Jalu Tejo Nugroho ◽  
M. Rokhis Khomaruddin
Keyword(s):  
Confidence Level ◽  
Vegetation Index ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Forest Land ◽  
Infrared Band ◽  
Central Kalimantan ◽  
Previous Period ◽  
Vegetation Greenness ◽  
Dry Conditions

Abstract El-Nino, which occurred in 2019 in Indonesia, caused longer dry conditions than usual. Low rainfall and vegetation drought cause widespread forest/land fires. This study aims to know the relationship between drought conditions and forest/land fires from the parameters of rainfall and vegetation greenness level. The study located in Jambi and Central Kalimantan Provinces during the peak months of fires which is September 2019. To see fluctuations in the peak of fires, eight daily data were taken for this period. Extraction of rainfall information is derived from the Himawari-8 infrared band L1 image into L2 rainfall rate data. Vegetation greenness level information is derived from Terra/Aqua MODIS red and near-infrared band images into L2 Enhance Vegetation Index (EVI) data. Hotspot data comes from the images of Terra, Aqua MODIS, SNPP VIIRS, and NOAA20. Fire data was extracted from hotspot data and delineation of MODIS RGB image smoke. Rainfall fluctuation affects the number of forest/land fire hotspots. The decrease in rainfall was followed by an increase of hotspot numbers and vice versa. In Jambi Province, rainfall decreased in first to second period i.e. 40 to 0 mm was followed by an increase of hotspot number which dominated by high confidence level. In Central Kalimantan rainfall increased from third to fourth period i.e. 0 to 100-400 mm followed by the decreasing of hotspot number which dominated by medium confidence level. Meanwhile, the TKV variable had little effect on the number of hotspots but related with rainfall data. In Central Kalimantan Province, the driest TKV (0.1) on September 14-21, 2019, was influenced by low rainfall in the previous period which also has highest number of fire hotspots. In Jambi Province, the driest TKV happened on third period which also the result of lowest rainfall and highest number of fire hotspot in the previous period.

scholarly journals Forest and land fire smoke detection using GCOM-C data (case study: Pulang Pisau, Central Kalimantan)

2021 ◽  
Vol 893 (1) ◽  
pp. 012068
Author(s):  
K I N Rahmi ◽  
N Febrianti ◽  
I Prasasti
Keyword(s):  
Near Infrared ◽  
Thermal Infrared ◽  
Short Wave ◽  
Forest Land ◽  
Visual Interpretation ◽  
Infrared Band ◽  
Central Kalimantan ◽  
Fire Smoke ◽  
Shortwave Infrared

Abstract Forest/land fire give bad impact of heavy smoke on peatland area in Indonesia. Forest/land fire smoke need to be identified the distribution periodically. New satellite of GCOM-C has been launched to monitor climate condition and have visible, near infrared and thermal infrared. This study has objective to identify fire smoke from GCOM-C data. GCOM-C data has wavelength range from 0.38 to 12 μm it covers visible, near infrared, short-wave infrared and thermal infrared. It is relatively similar to MODIS or Himawari-8 images which could identify forest/land fire smoke. The methodology is visual interpretation to detect forest/land fire smoke using near infrared band (VN08), shortwave infrared band (SW03), and thermal bands (T01 and T02). Hotspot data is overlaid with GCOM-C image to represent the location of fire events. Combination of composite RGB image has been applied to detect forest/land fire smoke. GCOM-C image of VN8 bands and combination of thermal band in composite image could be used to detect fire smoke in Pulang Pisau, Central Kalimantan.

Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

2021 ◽  
Author(s):  
Georg Wohlfahrt ◽  
Albin Hammerle ◽  
Barbara Rainer ◽  
Florian Haas
Keyword(s):  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
Census Data ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Wavelength Region ◽  
Remote Sensing Data ◽  
Warning Signs ◽  
Mitigation Measures

<p>Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.</p><p>Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.</p><p>To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.</p>

scholarly journals Evaluation of Soil Degradation Based on High Resolution Remote Sensing Data

2005 ◽  
pp. 145-148
Author(s):  
Péter Burai ◽  
János Tamás
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Soil Salinity ◽  
Strong Correlation ◽  
Soil Degradation ◽  
Vegetation Index ◽  
Near Infrared ◽  
Salt Content ◽  
Remote Sensing Data ◽  
Test Site

Soil salinity is the main problem of soil degradation in the Grate Plain with cultivated area of 20% affected. Its influence is accelerated on the water managed and irrigated lands. Remote sensing can significantly contribute to detecting temporal changes of salt-related surface features. We have chosen a farm where intensive crop cultivation takes place as a test site as soil degradation can be intensive as a result of land use and irrigation. In order to evaluate soil salt content and biomass analysis, we gathered detailed data from an 100x250 m area. We analyzed the salinity property of the samples. In our research we used a TETRACAM ADC multispectral camera to take high resolution images (0,2-0,5 m) of low altitude (300-500 m). A Normalized Vegetation Index was computed from near infrared (750-950 nm) and red (620-750 nm) bands. This data was compared with the samples of investigated area. Analyzing the images, we evaluated image reliability, and the connection between the bands and the soil properties (pH, salt content). A strong correlation observed between NDVI and soil salinity (EC) makes the multispectral images suitable for construction of salinity map. A further strong correlation was determined between NDVI and yield.

scholarly journals NEON NIST data science evaluation challenge: methods and results of team FEM

2018 ◽  
Author(s):  
Michele Dalponte ◽  
Lorenzo Frizzera ◽  
Damiano Gianelle
Keyword(s):  
Data Science ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Region Growing ◽  
Support Vector ◽  
Infrared Band ◽  
Species Classification ◽  
Individual Tree ◽  
Data Alignment ◽  
Selection Of

An international data science challenge, called NEON NIST data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: 1) segmentation of tree crowns; 2) data alignment; and 3) tree species classification. In this paper the methods and results of team FEM in the NEON NIST data science evaluation challenge are presented. The individual tree crown (ITC) segmentation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the segmentation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the segmented ITCs and the ground measured trees was done using an Euclidean distance among the position, the height, and the crown radius of the ITCs and the ground trees. The classification (Task 3) was performed using a Support Vector Machine classifier applied to a selection of the hyperspectral bands. The selection of the bands was done using a Sequential Forward Floating Selection method and the Jeffries Matusita distance. The results in the three tasks were very promising: team FEM ranked first in Task 1 and 2, and second in Task 3. The segmentation results showed that the proposed approach segmented both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good, even if the accuracy was biased towards the most represented species.

scholarly journals Hyperspectral Characteristics and Scale Effects of Leaf and Canopy of Summer Maize under Continuous Water Stresses

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1180
Author(s):  
Meng Li ◽  
Ronghao Chu ◽  
Xiuzhu Sha ◽  
Feng Ni ◽  
Pengfei Xie ◽  
...  
Keyword(s):  
Scale Effect ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Scale Effects ◽  
Spectral Characteristics ◽  
Summer Maize ◽  
Infrared Band ◽  
Area Index ◽  
Closed Canopy

The scale effect problem is one of the most challenging issues in remote sensing studies. However, the research on the methodology and theory of the scale effect is scarcely applied in practice. To this end, in this study, 3 years of field experimental data of continuous water stresses on summer maize were used for this purpose. Furthermore, the Prospect and Sail models were employed to investigate the scale effects of reflectance characteristics and vegetation indexes. The results indicated that the spectral characteristics of canopy and leaf of summer maize were similar under continuous water stresses at various stages. The reflectance at the canopy level was distinct from that at the leaf level, considering the soil background differences. From leaf to canopy scales, with the increase in the leaf area index (LAI), the spectral reflectance of all treatments in the visible band decreased, but increased in the near-infrared band, and the reflectance was saturated when LAI increased to 5. The reflectance difference caused by LAI variation was enlarged as the drought stress intensified in the short-wave infrared band. The spectral reflectance in the near-infrared band was susceptible to leaf inclination angle (LIA) variation and changed significantly, especially in the closed canopy. With the increase in LAI, the difference vegetation index (DVI) and normalized difference vegetation index (NDVI) values under each treatment showed a gradually increasing trend. With the increase in LIA, the DVI value decreased gradually, and the DVI value under the saturated canopy was significantly higher than that under the unclosed canopy. However, the NDVI values of all treatments did not change with LIA, mostly under the closed canopy. Overall, the results demonstrated that LAI had a more significant influence on canopy reflectance than LIA. In addition, NDVI was not able to capture the LAI and LIA information when the canopy was closed, but DVI performed better.

scholarly journals Assessing drought from satellite data to support agricultural production

2020 ◽  
Vol 4 (1) ◽  
pp. First
Author(s):  
Van Tran Thi ◽  
Toi Nguyen Duong Lam ◽  
Huynh Phan Thi Diem ◽  
Ha Nguyen Ngan ◽  
Bao Ha Duong Xuan
Keyword(s):  
Surface Temperature ◽  
Social Life ◽  
Vegetation Index ◽  
Near Infrared ◽  
Global Climate ◽  
Landsat 8 ◽  
Agricultural Crop ◽  
Infrared Band ◽  
Drought Assessment ◽  
Optical Satellite Images

Drought is one of the disasters causing the problems to the economy and social life of people, especially where agriculture is the main source of income. The paper presents the results of studying the application of optical satellite images to investigate the drought situation for the southern part of Binh Phuoc province for perennial cropland, the main agricultural crop of the province. The image used is Landsat 8 of the dry season month 2015. The method of drought assessment is based on the relationship of surface temperature, and the Normalization Difference Vegetation Index (NDVI) integrated into the Temperature-Vegetation Dryness Index TVDI. In particular, the NDVI index is determined from the red and near-infrared bands, and the surface temperature is determined from the thermal infrared band of Landsat 8 images. The results show that the whole area of southern Binh Phuoc has drought area accounting for 54.9% of the total area, of which the majority is mild drought level 38.3%, high and serious level is 16.7%. About the area of perennial land has drought area accounted for 33.76% of the total area, of which Dong Xoai town has the highest percentage of drought-affected areas compare to other districts. The results of the study aimed to identify drought areas with different levels so that managers can promptly take measures to protect agricultural crops and to ensure people's livelihoods in the global climate change trend seriously affecting the localities today.

scholarly journals CCD CBERS and ASTER data in dasometric characterization of Pinus radiata D. Don (North-western Spain)

CERNE ◽  
2013 ◽  
Vol 19 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Eva Sevillano-Marco ◽  
Alfonso Fernández-Manso ◽  
Carmen Quintano ◽  
Marcela Poulain
Keyword(s):  
Remote Sensing ◽  
Pinus Radiata ◽  
Near Infrared ◽  
Thermal Emission ◽  
Correlation Coefficients ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Infrared Band ◽  
Western Spain

A Chinese-Brazilian Earth Resources Satellite (CBERS) and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes coupled with ancillary georeferenced data and field survey were employed to examine the potential of the remote sensing data in stand basal area, volume and aboveground biomass assessment over large areas of Pinus radiata D. Don plantations in Northwestern Spain. Statistical analysis proved that the near infrared band and the shade fraction image showed significant correlation coefficients with all stand variables considered. Predictive models were accordingly selected and utilized to undertake the spatial distribution of stand variables in radiata stands delimited by the National Forestry Map. The study reinforces the potentiality of remote sensing techniques in a cost-effective assessment of forest systems.

scholarly journals Cartographie De La Salinite A La Surface Du Sol Dans Une Zone Du Prerif. Cas De La Region De L’ouergha

2016 ◽  
Vol 12 (3) ◽  
pp. 197
Author(s):  
Mohamed Sadiki ◽  
Amal Markhi ◽  
Hicham Elbelrhiti ◽  
Souad Mrabet
Keyword(s):  
Soil Salinity ◽  
Near Infrared ◽  
Salt Water ◽  
Spectral Response ◽  
Surface Condition ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Soil Salinization ◽  
Landsat Tm ◽  
Infrared Band

The soil and groundwater salinization phenomenon in semi-arid to arid climate is considered as a real threat to safety and food quality. There are several factors that present soil salinity, some factors are purely climatic (temperature, rainfall levels, lack of drainage, composition of the rock) or human-induced (using salt water to irrigation). The aim of the work is to take stock of the surface condition at a specified scale of soil salinity by taking satellite images Landsat TM 2009 and ASTER 2003 with 15 m and 30 m of resolution respectively. This study allows us to detect the potential of remote sensing data to see a set of thematic maps that distinguish, evaluate and locate their extended saline soils on the surface of the study area. The methods of satellite image processing are for understanding of soil salinization process, assess their extensive and locate areas vulnerable to soil and water salinization. Evaluation of the results of applying this method on Landsat TM gave an accuracy of 87%. This study also allows us to highlight spectral indices that again demonstrate the natural origin, related to the lithology of groundwater salinity in the study area. These various indices largely exploit the difference spectral response of vegetation and soils in the red band (R) and near infrared band (PIR) which is related to the density of green vegetation the NDSI and NDVI which allows a very good distinction between areas of salinity and vegetation area.

scholarly journals Mangrove Vegetation Health Assessment Based on Remote Sensing Indices for Tanjung Piai, Malay Peninsular

2019 ◽  
Vol 12 (2) ◽  
pp. 26-40
Author(s):  
Sheriza Mohd Razali ◽  
Ahmad Ainuddin Nuruddin ◽  
Marryanna Lion
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Health Assessment ◽  
Stem Density ◽  
Infrared Band ◽  
Green Vegetation ◽  
Conservation Activity ◽  
Ground Truthing

Abstract Mangroves critically require conservation activity due to human encroachment and environmental unsustainability. The forests must be conserving through monitoring activities with an application of remote sensing satellites. Recent high-resolution multispectral satellite was used to produce Normalized Difference Vegetation Index (NDVI) and Tasselled Cap transformation (TC) indices mapping for the area. Satellite Pour l’Observation de la Terre (SPOT) SPOT-6 was employed for ground truthing. The area was only a part of mangrove forest area of Tanjung Piai which estimated about 106 ha. Although, the relationship between the spectral indices and dendrometry parameters was weak, we found a very significant between NDVI (mean) and stem density (y=10.529x + 12.773) with R2=0.1579. The sites with NDVI calculated varied from 0.10 to 0.26 (P1 and P2), under the environmental stress due to sand deposition found was regard as unhealthy vegetation areas. Whereas, site P5 with NDVI (mean) 0.67 is due to far distance from risk wave’s zone, therefore having young/growing trees with large lush green cover was regard as healthy vegetation area. High greenness indicated in TC means, the bands respond to a combination of high absorption of chlorophyll in the visible bands and the high reflectance of leaf structures in the near-infrared band, which is characteristic of healthy green vegetation. Overall, our study showed our tested WV-2 image combined with ground data provided valuable information of mangrove health assessment for Tanjung Piai, Johor, Malay Peninsula.

scholarly journals HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN

2020 ◽  
Vol 16 (2) ◽  
pp. 143
Author(s):  
Khalifah Insan Nur Rahmi ◽  
Sayidah Sulma ◽  
Indah Prasasti
Keyword(s):  
Remote Sensing ◽  
Forest Fires ◽  
Near Infrared ◽  
The Other ◽  
Landsat 8 ◽  
Forest Land ◽  
Field Surveys ◽  
Infrared Wavelength ◽  
Central Kalimantan ◽  
Middle Infrared

The Advanced Himawari Imager (AHI) is the sensor aboard the remote-sensing satellite Himawari-8 which records the Earth’s weather and land conditions every 10 minutes from a geostationary orbit. The imagery produced known as Himawari-8 has 16 bands which cover visible, near infrared, middle infrared and thermal infrared wavelength potentials to monitor forestry phenomena. One of these is forest/land fires, which frequently occur in Indonesia in the dry season. Himawari-8 can detect hotspots in thermal bands 5 and band 7 using absolute fire pixel (AFP) and possible fire pixel (PFP) algorithms. However, validation has not yet been conducted to assess the accuracy of this information. This study aims to validate hotspots identified from Himawari images based on information from Landsat 8 images, field surveys and burnout data. The methodology used to validate hotspots comprises AFP and PFP extraction, determining firespots from Landsat 8, buffering at 2 km from firespots, field surveys, burnout data, and calculation of accuracy. AFP and PFP hotspot validation of firespots from Landsat-8 is found to have higher accuracy than the other options. In using Himawari-8 hotspots to detect land/forest fires in Central Kalimantan, the AFP algorithm with 2km radius has accuracy of 51.33% while the PFP algorithm has accuracy of 27.62%.

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