scholarly journals Remote-Sensing Monitoring of Grassland Degradation Based on the GDI in Shangri-La, China

2019 ◽  
Vol 11 (24) ◽  
pp. 3030 ◽  
Author(s):  
Yanlin Yang ◽  
Jinliang Wang ◽  
Yun Chen ◽  
Feng Cheng ◽  
Guangjie Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Vegetation Index ◽  
Satellite Image ◽  
Animal Husbandry ◽  
Transformation Rate ◽  
Grassland Degradation ◽  
Degradation Index ◽  
Monitoring Period ◽  
Degraded Grassland

Grassland resources are important land resources. However, grassland degradation has become evident in recent years, which has reduced the function of soil and water conservation and restricted the development of animal husbandry. Timely and accurate monitoring of grassland changes and understanding the degree of degradation are the foundation for the scientific use of grasslands. The grassland degradation index of ground comprehensive evaluation (grassland degradation index, GDIg) is a digital expression of grassland growth that can accurately indicate the degradation of grasslands. In this research, the accuracy of GDIg in evaluating grassland degradation is discussed; the typical areas of grassland degradation in Shangri-La City, i.e., the towns of Jiantang and Xiaozhongdian, are selected as the research area. Through a field survey and spectroscopy combined with Huanjing-1 (HJ-1) satellite image data, grassland degradation was monitored in the study area from 2008 to 2017. The results show that: (1) GDIg based on six indicators, namely, above-ground biomass, cover level, height, biomass of edible herbage, biomass of toxic weeds, and species richness, can effectively indicate grassland degradation, with the accuracy of the degradation grade assessment reaching 98.6%. (2) The correlation between the GDIg and the grey values of 4 wavebands and 7 types of vegetation indexes derived from the HJ-1 is analysed, and the degraded grassland inversion model was built and revised based on HJ-1 data. The grassland degradation evaluation index of remote sensing (GDIrs) model indicates that grassland degradation is proportional to the ratio vegetation index (RVI). (3) The grassland area was 405.40 km2 in the initial monitoring period, accounting for 17.26% of the study area, while at the end of the monitoring period, the area was 338.87 km2, with a loss of 66.53 km2. From 2008 to 2017, the area of non-degraded and slightly degraded grassland in the study area presented a downward trend, with decreases of 59.87 km2 and 49.93 km2, respectively. In contrast, the area of moderately degraded grassland increased by 41.17 km2 from 91.58 km2 in 2008 to 132.74 km2 in 2017, accounting for 39.17% of the grassland. The area of severely degraded grassland was 78.32 km2, accounting for 23.11% of the grassland in 2017. (4) The degraded grasslands in the study area mainly transformed into the degradation-enhanced (deterioration) type. As the transformation rate gradually slows down, the current situation of grassland degradation is not hopeful.

scholarly journals ANALYSIS OF THE DEGREE OF GRASSLAND DEGRADATION USING REMOTE SENSING

2021 ◽  
Vol 22 (80) ◽  
pp. 201-219
Author(s):  
Jaiza Santos Motta ◽  
César Claudio Cáceres Encina ◽  
Eliane Guaraldo ◽  
Ariadne Brabosa Gonçalves ◽  
Roberto Macedo Gamarra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Grassland Degradation ◽  
Tropical Savanna ◽  
Degradation Index ◽  
Brazilian Savannah ◽  
Pasture Degradation ◽  
Multitemporal Remote Sensing

The objective of this study is to adapt the calculations of the Pasture Degradation Index (GDI) to the Brazilian savannah using medium spatial resolution satellite image for the dry season. Vegetation cover is the main evaluation parameter used to calculate the GDI. The extreme ranges of the grazing class were determined by the NDVI histogram of a single date. Pasture cover was distinguished into five classes called Vegetable Pasture Cover (GVC), derived from NDVI and compared with five other classes derived from field photographs, named Green Coverage Percentage (GCP). The similarity between GVC and GVP demonstrated that GVC can be used to classify pasture cover. As a product of GVC, GDI was obtained. The GDI showed that pasture degradation in Paraíso das Águas is very serious. Extremely severe and Severe degradation occupy 9.28% and 25.22% of the study area, moderate and light degradation of pasture occupy 8.29% and 4.50%, respectively, and the non-degradation area covers 1.43 % of pastures. The results suggest the possibility of applying the GDI, originally developed for natural fields and multitemporal remote sensing data, to evaluate the conditions of the tropical savanna planted fields by means of a unique image.

Acquisitions of Vegetation Coverage and Cultivated Land Occupation Ratio of Taiyuan Valley Plain for Example Using CBERS-02B CCD Image

2012 ◽  
Vol 518-523 ◽  
pp. 5663-5667
Author(s):  
Shi Wei Li ◽  
Ji Long Zhang ◽  
Jian Sheng Yang
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Vegetation Coverage ◽  
Cultivated Land ◽  
Land Occupation ◽  
Coverage Ratio

Vegetation covering situation is very important for the quality of air quality, soil and water conservation ability and soil forming in an area. By using the remote sensing image of Taiyuan Valley Plain, the application of Normalized Difference Vegetation Index (NDVI) and unsupervised classification, the vegetation coverage map which includes non-cultivated land disposition and cultivated land disposition was obtained using ERDAS Imagine software. To evaluate the accuracy of the results, 200 points were sampled randomly, the high spatial resolution remote sensing image from Google Earth was used as the reference. The overall classification accuracy is 82%, with the Kappa statistic of 0.81. By counting the totally pixel acreage, it was gotten that the vegetation coverage was 46% and the cultivated land coverage ratio was 31% in the study area.

scholarly journals Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

2019 ◽  
Vol 26 (3) ◽  
pp. 117
Author(s):  
Tri Muji Susantoro ◽  
Ketut Wikantika ◽  
Agung Budi Harto ◽  
Deni Suwardi
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Landsat 8 ◽  
Gas Field ◽  
Research Results ◽  
The Third ◽  
Third Phase ◽  
The Impact

This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.

scholarly journals Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Arturo Reyes-González ◽  
Jeppe Kjaersgaard ◽  
Todd Trooien ◽  
Christopher Hay ◽  
Laurent Ahiablame
Keyword(s):  
Remote Sensing ◽  
Irrigation Water ◽  
Water Conservation ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Crop Evapotranspiration ◽  
Crop Water ◽  
Landsat Images ◽  
Crop Water Consumption

Irrigation water is limited and scarce in many areas of the world, including Comarca Lagunera, Mexico. Thus better estimations of irrigation water requirements are essential to conserve water. The general objective was to estimate crop water demands or crop evapotranspiration (ETc) at different scales using satellite remote sensing-based vegetation index. The study was carried out in northern Mexico (Comarca Lagunera) during four growing seasons. Six, eleven, three, and seven clear Landsat images were acquired for 2013, 2014, 2015, and 2016, respectively, for the analysis. The results showed thatETcwas low at initial and early development stages, whileETcwas high during mid-season and harvest stages. These results are not new but give us confidence in the rest of ourETcresults. DailyETcmaps helped to explain the variability of crop water use during the growing season. Based on the results we can conclude thatETcmaps developed from remotely sensed multispectral vegetation indices are a useful tool for quantifying crop water consumption at regional and field scales. UsingETcmaps at the field scale, farmers can supply appropriate amounts of irrigation water corresponding to each growth stage, leading to water conservation.

scholarly journals Comprehensive Grassland Degradation Monitoring by Remote Sensing in Xilinhot, Inner Mongolia, China

2020 ◽  
Vol 12 (9) ◽  
pp. 3682 ◽  
Author(s):  
Xin Lyu ◽  
Xiaobing Li ◽  
Jirui Gong ◽  
Hong Wang ◽  
Dongliang Dang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Net Primary Production ◽  
Grazing Intensity ◽  
Scientific Quality ◽  
Grassland Degradation ◽  
Feeding Intensity ◽  
Degradation Monitoring ◽  
Degraded Grassland ◽  
And Function

Grassland degradation is a complex process and cannot be thoroughly measured by a single indicator, such as fractional vegetation cover (FVC), aboveground biomass (AGB), or net primary production (NPP), or by a simple combination of these indicators. In this research, we combined measured data with vegetation and soil characteristics to establish a set of standards applicable to the monitoring of regional grassland degradation by remote sensing. We selected indicators and set their thresholds with full consideration given to vegetation structure and function. We optimized the indicator simulation, based on which grassland degradation in the study area during 2014–2018 was comprehensively evaluated. We used the feeding intensity of herbivores to represent the grazing intensity. We analyzed the effects of climate and grazing activities on grassland degradation using the constraint line method. The results showed degradation in approximately 69% of the grassland in the study area and an overall continued recovery of the degraded grassland from 2014 to 2018. We did not identify any significant correlation between temperature and grassland degradation. The increase in precipitation promoted the recovery of degraded grassland, whereas increased grazing may have aggravated degradation. Our findings can not only improve the scientific quality and accuracy of grassland degradation monitoring by remote sensing but also provide clear spatial information and decision-making help in sustainable management of grassland regions.

scholarly journals Biophysical Parameters and Actual Evapotranspiration of Bean Culture by Means of Remote Sensing

2019 ◽  
Vol 11 (13) ◽  
pp. 156 ◽  
Author(s):  
Allisson Lucas Brandão Lima ◽  
Roberto Filgueiras ◽  
Everardo Chartuni Mantovani ◽  
Daniel Althoff ◽  
Robson Argolo dos Santos ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Simple Algorithm ◽  
Actual Evapotranspiration ◽  
Estimation Methods ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Biophysical Parameters

Agricultural irrigation is involved in an important chain that involves all sectors of the economy, either directly, by increasing food production, or indirectly, by withdrawing large amounts of fresh water. The relevance of this theme forces the search for alternatives to make water use as rational as possible. Evapotranspiration estimation methods based in remote sensing, such as the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model, become extremely relevant in these scenarios, since it is possible to estimate this parameter in large scales. Therefore, the aim of this research was to apply the SAFER model in the estimation of bean crop actual evapotranspiration using Landsat-8 satellite image data. One of the parameters used as input in the SAFER model is the NDVI (Normalized Difference Vegetation Index), which presented a coefficient of determination (r²) equal to 0.80 when compared to the crop coefficient. The actual evapotranspiration (ETa) estimated by the SAFER model were compared to the FAO 56 model estimates for later correlation between the models. This information is expected to assist the producer in a better management of water resources used in irrigation. The correlation between the two models presented a relevant coefficient of determination (r2 = 0.73), representing the potential of the SAFER model in relation to the FAO model 56.

scholarly journals Investigation on Aneuk Laot Lake Water Depreciation Based on Distribution of Minor Fault with a Remote Sensing Method

2019 ◽  
Vol 8 (2) ◽  
pp. 47-54
Author(s):  
Nindi Yusifa ◽  
Aljikri Yanto ◽  
Shiyasatusy Sairiyyah ◽  
Muhammad Isa
Keyword(s):  
Remote Sensing ◽  
Lake Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Distribution ◽  
Vegetation Density ◽  
Fracture Density ◽  
Lake Water Level ◽  
Landsat Satellite

Danau Aneuk Laut berasal dari bekas kepundan gunungapi yang telah mati dan secara bertahap terisi air. Sejak 15 tahun belakangan ini danau mengalami penurunan muka air, hal ini diduga akibat Gempa dan tsunami pada 26 Desembar 2004. Pemantauan penyusutan air danau dilakukan dengan metode penginderaan jauh menggunakan data DEM SRTM dan citra satelit Landsat. DEM SRTM digunakan untuk analisis struktur sesar dan rekahan melalui peta Fault Fracture Density (FFD). Citra satelit landsat digunakan untuk identifikasi sebaran vegetasi menggunakan transformasi Normalized Difference Vegetation Index (NDVI) dan klasifikasi tutupan lahan menggunakan metode Maximum likelhood dari tahun 2001-2017. Berdasarkan peta FFD ditemukan kelurusan tertinggi yaitu danau Aneuk Laot yang memiliki zona permeabel dari struktur geologi sehingga semakin kecil kerapatan struktur maka semakin besar permeabilitasnya. Peta penyusutan air danau dengan menghitung luas permukaan air danau dari periode 2001 -2017 telah mengalami penurunan sebesar 102.600 m². Untuk tahun 2001-2003 mengalami kenaikan sebesar 68700 m² dan pada tahun 2003-2004 mengalami penurunan sebesar -42300 m². Peta sebaran vegetasi di pulau Weh memiliki index vegetasi NDVI maksimum 0,863554 yang artinya memiliki sebaran vegetasi sangat rapat berwarna hijau pekat dan Index vegetasi minimum NDVI sebesar -0,375631 menunjukkan tidak adanya rapat vegetasi berwarna coklat. Aneuk Laot Lake comes from the former crater of a volcano that has died and gradually filled with water. For about 15 years lakes have decreased Lake water level, allegedly caused by earthquake and tsunami on 26 desembar 2004. Monitoring of lake water depreciation is done by remote sensing method using DEM SRTM data and Landsat satellite image. DEM SRTM is used for analysis of fault and fracture structures through the Fault Fracture Density (FFD) map. Landsat satellite imagery was used to identify vegetation distribution using Normalized Difference Vegetation Index (NDVI) transformation and land cover classification using Maximum likelihary method from 2001-2017. Based on the FFD map found the highest alignment of the Aneuk Laot lake that has a permeable zone of geological structure so that the smaller the density of the structure the greater the permeability. Map of the lake's water depreciation by calculating the lake surface area from 2001 -2017 has decreased by 102,600 m². For 2001-2003 increased by 68700 m² and in 2003-2004 decreased by -42300 m². The vegetation distribution map on Weh island has a maximum NDVI vegetation index of 0.863554 having very dense green vegetation density and a minimum vegetation index of NDVI-0.375631 indicating the absence of a brown vegetation meeting. Keywords: AneukLaot lake, DEM SRTM, Landsat, FFD

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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