scholarly journals Long Term Vegetation Health Monitoring in Dibru-Saikhowa National Park using Remote Sensing & Gis

2020 ◽  
Vol 9 (4) ◽  
pp. 1901-1906
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Dynamics ◽  
National Park ◽  
Condition Index ◽  
Temperature Pattern ◽  
Positive Trend ◽  
Study Results ◽  
Wide Range ◽  
Vegetation Health

Remote sensing and GIS based vegetation monitoring offers lot of potential for ecosystem studies. This study utilized freely available moderate resolution Landsat images to quantify the changes in vegetation dynamics in Dibru-Saikhowa national park, India. A wide range of vegetation indices and temperature indices such as normalized difference vegetation index (NDVI), land surface temperature (LST), vegetation condition index (VCI), temperature condition index (TCI) and vegetation health index (VHI) was utilized for the purpose of the study. Results reveal that the study area has gone through changes in vegetation and temperature pattern affecting the land surface balances. The maximum NDVI value for the year 1996 was recorded between 0.5-0.8 whereas the maximum LST values ranged between 17.240C-34.850C. In 2019, the maximum NDVI values reduced to the range of 0.14-0.6 while LST increased to 18.950C-38.910C. Consequently, the VHI classes showed a negative trend. In 1996, healthy vegetation covered a total area of 14564.6 ha which reduced to 9872.1 ha in 2019. Conversely, the no vegetation class showed a significant positive trend from 951.3 ha to 3015.99. Such alteration in vegetation dynamics in the study area is affecting the local climate and regional ecosystem services and require instant attention of conservationist and policy makers

A novel agricultural drought monitoring framework using remote sensing products

2021 ◽  
Author(s):  
Trupti Satapathy ◽  
Meenu Ramadas ◽  
Jörg Dietrich
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Stress ◽  
Land Surface ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Crop Water ◽  
Vegetation Health ◽  
Crop Water Stress

<p>Among natural hazards, droughts are known to be very complex and disastrous owing to their creeping nature and widespread impacts. Specifically, the occurrence of agricultural droughts poses a threat to the productivity and socio-economic development of countries such as India. In this study, we propose a novel framework for agricultural drought monitoring integrating the different indicators of vegetation health, crop water stress and soil moisture, that are derived from remote sensing satellite data. The drought monitoring is performed over Odisha, India, for the period 2000-2019. Soil moisture and land surface temperature datasets from GLDAS Noah Land Surface Model and surface reflectance data from MODIS (MOD09GA) are used in this study. We compared the utility of popular indices: (i) soil moisture condition index, soil moisture deficit index and soil wetness deficit index to represent the soil moisture level; (ii) temperature condition index, vegetation condition index and normalised difference water index to indicate vegetation health; (iii) short wave infrared water stress to represent crop water stress condition. Correlation analyses between these indices and the seasonal crop yields are performed, and suitable indicators are chosen. The popular entropy weight method is then used to integrate the indices and develop the proposed composite drought index. The index is then used for monitoring the agricultural drought condition over the study area in drought periods. The proposed framework for week- to month-scale monitoring have potential applications in identification of agricultural drought hotspots, analysis of trends in drought severity, and drought early warning for agricultural water management.</p>

scholarly journals Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Antonio-Juan Collados-Lara ◽  
Steven R. Fassnacht ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Temperature Fields ◽  
Lapse Rate ◽  
Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

scholarly journals Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

2021 ◽  
Vol 13 (7) ◽  
pp. 1340
Author(s):  
Shuailong Feng ◽  
Shuguang Liu ◽  
Lei Jing ◽  
Yu Zhu ◽  
Wende Yan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Impacts ◽  
Land Surface ◽  
Vegetation Index ◽  
Highway Construction ◽  
Temporal Changes ◽  
Moisture Index ◽  
Buffer Zones ◽  
Wide Range ◽  
Spatial And Temporal Changes

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

scholarly journals Vegetation dynamics and soil water balance in a water-limited Mediterranean ecosystem on Sardinia, Italy

2008 ◽  
Vol 12 (6) ◽  
pp. 1257-1271 ◽  
Author(s):  
N. Montaldo ◽  
J. D. Albertson ◽  
M. Mancini
Keyword(s):  
Soil Water ◽  
Land Surface ◽  
Vegetation Dynamics ◽  
Mediterranean Ecosystems ◽  
Surface Fluxes ◽  
Annual Rainfall ◽  
Woody Vegetation ◽  
Wide Range ◽  
Hydrometeorological Conditions

Abstract. Mediterranean ecosystems are commonly heterogeneous savanna-like ecosystems, with contrasting plant functional types (PFTs, e.g. grass and woody vegetation) competing for water. Mediterranean ecosystems are also commonly characterized by strong inter-annual rainfall variability, which influences the distributions of PFTs that vary spatially and temporally. An extensive field campaign in a Mediterranean setting was performed with the objective to investigate interactions between vegetation dynamics, soil water budget and land-surface fluxes in a water-limited ecosystem. Also a vegetation dynamic model (VDM) is coupled to a 3-component (bare soil, grass and woody vegetation) Land surface model (LSM). The case study is in Orroli, situated in the mid-west of Sardegna within the Flumendosa river basin. The landscape is a mixture of Mediterranean patchy vegetation types: trees, including wild olives and cork oaks, different shrubs and herbaceous species. Land surface fluxes, soil moisture and vegetation growth were monitored during the May 2003–June 2006 period. Interestingly, hydrometeorological conditions of the monitored years strongly differ, with dry and wet years in turn, such that a wide range of hydrometeorological conditions can be analyzed. The coupled VDM-LSM model is successfully tested for the case study, demonstrating high model performance for the wide range of eco-hydrologic conditions. Results demonstrate also that vegetation dynamics are strongly influenced by the inter-annual variability of atmospheric forcing, with grass leaf area index changing significantly each spring season according to seasonal rainfall amount.

scholarly journals Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Erika Andujar ◽  
Nir Y. Krakauer ◽  
Chuixiang Yi ◽  
Felix Kogan
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Plant Stress ◽  
Meteorological Drought ◽  
Condition Index ◽  
Vegetation Health ◽  
Vegetation Health Index

Remote sensing is used for monitoring the impacts of meteorological drought on ecosystems, but few large-scale comparisons of the response timescale to drought of different vegetation remote sensing products are available. We correlated vegetation health products derived from polar-orbiting radiometer observations with a meteorological drought indicator available at different aggregation timescales, the Standardized Precipitation Evapotranspiration Index (SPEI), to evaluate responses averaged globally and over latitude and biome. The remote sensing products are Vegetation Condition Index (VCI), which uses normalized difference vegetation index (NDVI) to identify plant stress, Temperature Condition Index (TCI), based on thermal emission as a measure of surface temperature, and Vegetation Health Index (VHI), the average of VCI and TCI. Globally, TCI correlated best with 2-month timescale SPEI, VCI correlated best with longer timescale droughts (peak mean correlation at 13 months), and VHI correlated best at an intermediate timescale of 4 months. Our results suggest that thermal emission (TCI) may better detect incipient drought than vegetation color (VCI). VHI had the highest correlations with SPEI at aggregation times greater than 3 months and hence may be the most suitable product for monitoring the effects of long droughts.

scholarly journals Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa

2019 ◽  
Vol 11 (21) ◽  
pp. 2534 ◽  
Author(s):  
Willibroad Gabila Buma ◽  
Sang-Il Lee
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Condition Index ◽  
Drought Monitoring ◽  
World Population ◽  
Lake Chad ◽  
In Situ Data

As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data.

scholarly journals Special Issue on Remote Sensing of Snow and Its Applications

Geosciences ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 277 ◽  
Author(s):  
Ali Nadir Arslan ◽  
Zuhal Akyürek
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Land Surface ◽  
Climate Models ◽  
Weather Forecasting ◽  
Special Issue ◽  
Carbon Cycles ◽  
Wide Range ◽  
Remote Sensing Techniques

Snow cover is an essential climate variable directly affecting the Earth’s energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. The main objectives of this Special Issue, “Remote Sensing of Snow and Its Applications” in Geosciences are to present a wide range of topics such as (1) remote sensing techniques and methods for snow, (2) modeling, retrieval algorithms, and in-situ measurements of snow parameters, (3) multi-source and multi-sensor remote sensing of snow, (4) remote sensing and model integrated approaches of snow, and (5) applications where remotely sensed snow information is used for weather forecasting, flooding, avalanche, water management, traffic, health and sport, agriculture and forestry, climate scenarios, etc. It is very important to understand (a) differences and similarities, (b) representativeness and applicability, (c) accuracy and sources of error in measuring of snow both in-situ and remote sensing and assimilating snow into hydrological, land surface, meteorological, and climate models. This Special Issue contains nine articles and covers some of the topics we listed above.

Phenology-induced energy and carbon fluxes in land surface models

2020 ◽  
Author(s):  
Jan De Pue ◽  
José Miguel Barrios ◽  
Fabienne Maignan ◽  
Liyang Liu ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Terrestrial Ecosystems ◽  
Carbon Fluxes ◽  
Land Surface Models ◽  
Energy Fluxes ◽  
Wide Range ◽  
Surface Models ◽  
Modelling Strategies ◽  
Phenological Cycle

<p>The annual phenological cycle is of key importance for the carbon and energy fluxes in terrestrial ecosystems. Although the processes controlling budburst and leaf senescence are fairly well known, the connection between plant phenology and the carbon fluxes remains a challenging aspect in land surface modelling (LSM). In this study, the modelling strategies of three well stablished LSM are compared. The LSM considered in this study were: ORCHIDEE, ISBA-A-gs and the model driving the LSA-SAF evapotranspiration product (https://landsaf.ipma.pt). The latter model does not simulate the carbon fluxes but focuses on the computation of evapotranspiration and energy fluxes.<br>The phenological cycle is simulated explicitly in the ORCHIDEE model, using empirical relations based on temperature sum, water availability, and other variables. In the ISBA-A-gs model, phenology and LAI development is fully photosynthesis-driven. The phenology in the LSA-SAF model is driven by remote sensing forcing variables, such as LAI observations. Alternatively, the assimilation of remote sensing LAI products is a convenient method to improve the simulated phenological cycle in land surface models. A dedicated module for this operation is available in ISBA-A-gs.<br>Simulations were performed over a wide range of climatological conditions and plant functional types. The results were then validated with in-situ measurements conducted at Fluxnet stations. In addition to the comparison between measured and modelled carbon fluxes, the validation in this study included the intra-annual variation in the simulated phenological cycle.</p>

scholarly journals Contribution of Land Surface Temperature (TCI) to Vegetation Health Index: A Comparative Study Using Clear Sky and All-Weather Climate Data Records

2018 ◽  
Vol 10 (9) ◽  
pp. 1324 ◽  
Author(s):  
Virgílio Bento ◽  
Isabel Trigo ◽  
Célia Gouveia ◽  
Carlos DaCamara
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Weighted Average ◽  
Condition Index ◽  
Health Index ◽  
Climate Data ◽  
Clear Sky ◽  
Vegetation Health ◽  
Vegetation Health Index

The Vegetation Health Index (VHI) is widely used for monitoring drought using satellite data. VHI depends on vegetation state and thermal stress, respectively assessed via (i) the Vegetation Condition Index (VCI) that usually relies on information from the visible and near infra-red parts of the spectrum (in the form of Normalized Difference Vegetation Index, NDVI); and (ii) the Thermal Condition Index (TCI), based on top of atmosphere thermal infrared (TIR) brightness temperature or on TIR-derived Land Surface Temperature (LST). VHI is then estimated as a weighted average of VCI and TCI. However, the optimum weights of the two components are usually not known and VHI is usually estimated attributing a weight of 0.5 to both. Using a previously developed methodology for the Euro-Mediterranean region, we show that the multi-scalar drought index (SPEI) may be used to obtain optimal weights for VCI and TCI over the area covered by Meteosat satellites that includes Africa, Europe, and part of South America. The procedure is applied using clear-sky Meteosat Climate Data Records (CDRs) and all-sky LST derived by combining satellite and reanalysis data. Results obtained present a coherent spatial distribution of VCI and TCI weights when estimated using clear- and all-sky LST. This study paves the way for the development of a future VHI near-real time operational product for drought monitoring based on information from Meteosat satellites.

scholarly journals Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

2020 ◽  
Vol 12 (6) ◽  
pp. 1046 ◽  
Author(s):  
Nathalie Guimarães ◽  
Luís Pádua ◽  
Pedro Marques ◽  
Nuno Silva ◽  
Emanuel Peres ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Unmanned Aerial Vehicles ◽  
Land Surface ◽  
Weather Conditions ◽  
Agroforestry Systems ◽  
Data Availability ◽  
Operational Costs ◽  
Aerial Vehicles ◽  
Wide Range

Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.

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