scholarly journals Spatial Agreement among Vegetation Disturbance Maps in Tropical Domains Using Landsat Time Series

2020 ◽  
Vol 12 (18) ◽  
pp. 2948
Author(s):  
Inacio T. Bueno ◽  
Greg J. McDermid ◽  
Eduarda M. O. Silveira ◽  
Jennifer N. Hird ◽  
Breno I. Domingos ◽  
...  
Keyword(s):  
Atlantic Forest ◽  
Input Data ◽  
Vegetation Index ◽  
Vegetation Type ◽  
Future Research ◽  
Moisture Index ◽  
Vegetation Disturbance ◽  
Specific Sensitivity ◽  
Disturbance Detection ◽  
Semi Arid

Detecting disturbances in native vegetation is a crucial component of many environmental management strategies, and remote sensing-based methods are the most efficient way to collect multi-temporal disturbance data over large areas. Given that there is a large range of datasets for monitoring, analyzing, and detecting disturbances, many methods have been well-studied and successfully implemented. However, factors such as the vegetation type, input data, and change detection method can significantly alter the outcomes of a disturbance-detection study. We evaluated the spatial agreement of disturbance maps provided by the Breaks For Additive Season and Trend (BFAST) algorithm, evaluating seven spectral indices in three distinct vegetation domains in Brazil: Atlantic forest, savanna, and semi-arid woodland, by assessing levels of agreement between the outputs. We computed individual map accuracies based on a reference dataset, then ranked their performance, while also observing their relationships with specific vegetation domains. Our results indicated a low rate of spatial agreement among index-based disturbance maps, which itself was minimally influenced by vegetation domain. Wetness indices produced greater detection accuracies in comparison to greenness-related indices free of saturation. The normalized difference moisture index performed best in the Atlantic forest domains, yet performed poorest in semi-arid woodland, reflecting its specific sensitivity to vegetation and its water content. The normalized difference vegetation index led to high disturbance detection accuracies in the savanna and semi-arid woodland domains. This study offered novel insight into vegetation disturbance maps, their relationship to different ecosystem types, and corresponding accuracies. Distinct input data can produce non-spatially correlated disturbance maps and reflect site-specific sensitivity. Future research should explore algorithm limitations presented in this study, as well as the expansion to other techniques and vegetation domains across the globe.

scholarly journals Mulching as water-saving technique in dryland agriculture: review article

2019 ◽  
Vol 43 (1) ◽  
Author(s):  
Mohammad Abdul Kader ◽  
Ashutus Singha ◽  
Mili Amena Begum ◽  
Arif Jewel ◽  
Ferdous Hossain Khan ◽  
...  
Keyword(s):  
Arid Regions ◽  
Review Article ◽  
Water Saving ◽  
Future Research ◽  
Agricultural Water ◽  
Plastic Mulch ◽  
Dryland Agriculture ◽  
Crop Cultivation ◽  
Agricultural Water Resources ◽  
Semi Arid

Abstract Agricultural water resources have been limited over the years due to global warming and irregular rainfall in the arid and semi-arid regions. To mitigate the water stress in agriculture, mulching has a crucial impact as a water-saving technique in rain-fed crop cultivation. It is important mainly for preserving soil moisture, relegating soil temperature, and limiting soil evaporation, which affects the crop yield. Mulching has many strategic effects on soil ecosystem, crop growth, and climate. Mulch insulates the soil, helping to provide a buffer from cold and hot temperatures that have a crucial activity in creating beautiful and protected landscapes. This study has accumulated a series of information about both organic and plastic mulch materials and its applicability on crop cultivation. Moreover, future research potentials of mulching with modeling were discussed to quantify water loss in agriculture.

scholarly journals Análisis del efecto de variables ambientales en la estimación de la erosionabilidad (Factor K) = Analysis of the Effect of Environmental Variables on the Soil Erodibility Estimation (K Factor)

2018 ◽  
pp. 195
Author(s):  
Pedro Perez Cutillas ◽  
Gonzalo G. Barberá ◽  
Carmelo Conesa García
Keyword(s):  
Remote Sensing ◽  
Environmental Variables ◽  
Input Data ◽  
Vegetation Index ◽  
Mineralogical Composition ◽  
Soil Erodibility ◽  
Estimation Algorithms ◽  
K Factor ◽  
Remote Sensing Techniques

El objetivo principal de este trabajo se centra en la determinación y análisis de las variables ambientales que influyen en las divergencias de las estimaciones de erosionabilidad a partir de dos métodos, aplicando tres algoritmos de estimación del Factor K. La exploración de esta información permite conocer el peso que ejerce el origen de los datos de entrada a los modelos en el cómputo de erosionabilidad y qué importancia tiene en función del algoritmo elegido para la estimación del Factor K. Los resultados muestran que las pendientes, así como los índices de vegetación (NDVI) y de composición mineralógico (IOI) obtenidos mediantes técnicas de teledetección han   mostrado los valores de asociación más elevados entre ambos métodos.The main goal of this work is to determine and analyze the influence of environmental variables on the changes of two erodibility methods, through the application of three estimation algorithms of K Factor. The analysis of this information allows knowing the significance of the input data to the models in the erodibility estimation, and likewise the consequence of the algorithm selected for the estimation of K Factor. The results show that the slopes, as well as the vegetation index (NDVI) and the mineralogical composition index (IOI), generated both by remote sensing techniques, have shown the highest values of association between methods.

scholarly journals Multispectral Remote Sensing of Wetlands in Semi-Arid and Arid Areas: A Review on Applications, Challenges and Possible Future Research Directions

2020 ◽  
Vol 12 (24) ◽  
pp. 4190
Author(s):  
Siyamthanda Gxokwe ◽  
Timothy Dube ◽  
Dominic Mazvimavi
Keyword(s):  
Remote Sensing ◽  
Arid Regions ◽  
Remote Sensing Data ◽  
Sub Saharan Africa ◽  
Future Research ◽  
Multispectral Imagery ◽  
Aquatic Life ◽  
Sensing Data ◽  
Spatial Coverage ◽  
Semi Arid

Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions. A literature search shows a significant increase in the number of papers published during the 2000–2020 period, with most of these studies being in semi-arid regions in Australia and China, and few in the sub-Saharan Africa. This paper reviews progress made in the use of remote sensing in detecting and monitoring of the semi-arid and arid wetlands, and focuses particularly on new insights in detection and monitoring of wetlands using freely available multispectral sensors. The paper firstly describes important characteristics of wetlands in semi-arid and arid regions that require monitoring in order to improve their management. Secondly, the use of freely available multispectral imagery for compiling wetland inventories is reviewed. Thirdly, the challenges of using freely available multispectral imagery in mapping and monitoring wetlands dynamics like inundation, vegetation cover and extent, are examined. Lastly, algorithms for image classification as well as challenges associated with their uses and possible future research are summarised. However, there are concerns regarding whether the spatial and temporal resolutions of some of the remote-sensing data enable accurate monitoring of wetlands of varying sizes. Furthermore, it was noted that there were challenges associated with the both spatial and spectral resolutions of data used when mapping and monitoring wetlands. However, advancements in remote-sensing and data analytics provides new opportunities for further research on wetland monitoring and assessment across various scales.

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 Modelling spatial and temporal dynamics of gross primary production in the Sahel from earth-observation-based photosynthetic capacity and quantum efficiency

2017 ◽  
Vol 14 (5) ◽  
pp. 1333-1348 ◽  
Author(s):  
Torbern Tagesson ◽  
Jonas Ardö ◽  
Bernard Cappelaere ◽  
Laurent Kergoat ◽  
Abdulhakim Abdi ◽  
...  
Keyword(s):  
Primary Production ◽  
Quantum Efficiency ◽  
Vegetation Index ◽  
Photosynthetic Capacity ◽  
Gross Primary Production ◽  
Earth Observation ◽  
Arid Ecosystems ◽  
Spatially Explicit ◽  
Equivalent Water Thickness ◽  
Semi Arid

Abstract. It has been shown that vegetation growth in semi-arid regions is important to the global terrestrial CO2 sink, which indicates the strong need for improved understanding and spatially explicit estimates of CO2 uptake (gross primary production; GPP) in semi-arid ecosystems. This study has three aims: (1) to evaluate the MOD17A2H GPP (collection 6) product against GPP based on eddy covariance (EC) for six sites across the Sahel; (2) to characterize relationships between spatial and temporal variability in EC-based photosynthetic capacity (Fopt) and quantum efficiency (α) and vegetation indices based on earth observation (EO) (normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), enhanced vegetation index (EVI) and shortwave infrared water stress index (SIWSI)); and (3) to study the applicability of EO upscaled Fopt and α for GPP modelling purposes. MOD17A2H GPP (collection 6) drastically underestimated GPP, most likely because maximum light use efficiency is set too low for semi-arid ecosystems in the MODIS algorithm. Intra-annual dynamics in Fopt were closely related to SIWSI being sensitive to equivalent water thickness, whereas α was closely related to RDVI being affected by chlorophyll abundance. Spatial and inter-annual dynamics in Fopt and α were closely coupled to NDVI and RDVI, respectively. Modelled GPP based on Fopt and α upscaled using EO-based indices reproduced in situ GPP well for all except a cropped site that was strongly impacted by anthropogenic land use. Upscaled GPP for the Sahel 2001–2014 was 736 ± 39 g C m−2 yr−1. This study indicates the strong applicability of EO as a tool for spatially explicit estimates of GPP, Fopt and α; incorporating EO-based Fopt and α in dynamic global vegetation models could improve estimates of vegetation production and simulations of ecosystem processes and hydro-biochemical cycles.

scholarly journals The influence of the climatology of the semi-arid from Alagoas-Brazil on the raw milk microbiota

2017 ◽  
Vol 3 (1) ◽  
pp. 27
Author(s):  
Ticiano Gomes do Nascimento ◽  
Euridice Farias Falcão ◽  
Maria Cristina Delgado da Silva ◽  
Josicleide Nascimento Oliveira Silvino ◽  
Pierre Barnabé Escodro ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Thermal Comfort ◽  
Vegetation Index ◽  
Raw Milk ◽  
Climatic Conditions ◽  
The State ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Microbiological Analysis ◽  
Semi Arid

This study aimed to evaluate the influence of the climatology of the semi-arid from Alagoas-Brazil on the raw milk microbiota in semi-arid area of the 07 micro-regions of the State of Alagoas of Alagoas, Brazil. The climatic data were extracted from National Institute of Meteorology from the Brazilian government. The raw milk was collected after the dairy cow milking process in 12 small rural associations of the semi-arid from the State of Alagoas, during the 4 seasons and the raw milk was carried out procedures of sampling, transportation and microbiological analysis. A total of 58 samples were counted coliforms at 45°C, <em>Escherichia coli</em> and coagulase-positive <em>Staphylococcus</em>. Only 02 rural associations presented low levels of microbiological contamination, which were located in areas of climatic conditions and parameters of thermal comfort index and vegetation index favorable, but 10 rural associations presented high counting of coliforms at 45°C, <em>Escherichia coli</em>. The climatologic parameters (maximum temperature, atmospheric pressure), bovine comfort thermal index and vegetation index have showed to influence the growth of the coliforms at 45°C and <em>Escherichia coli</em> with high incidence during the summer weather. The precipitation parameter, bovine thermal comfort and vegetation index have displayed to influence coagulase-positive<em> Staphylococcus</em> counting especially during the period between the summer end and the autumn beginning seasons. New Actions, and Rural Education and Health Programs should be implemented as politics of Food Safety. New strategies and programs for dissemination more effective on the risks of transmission of pathogens and Foodborne Diseases are necessary as the part of emergence politics of the health and education areas. Regulatory Actions should be encouraged within the processes that improve the quality control of raw milk as well its bioproducts, with professional assistance relevant in agriculture area.

scholarly journals Relating surface backscatter response from TRMM Precipitation Radar to soil moisture: results over a semi-arid region

2009 ◽  
Vol 6 (5) ◽  
pp. 6425-6454
Author(s):  
H. Stephen ◽  
S. Ahmad ◽  
T. C. Piechota ◽  
C. Tang
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Arid Regions ◽  
Vegetation Index ◽  
Temporal Trends ◽  
Model Parameters ◽  
Precipitation Radar ◽  
Semi Arid

Abstract. The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using Variable Infiltration Capacity (VIC) model whereas measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.

Trade‐off between vegetation type, soil erosion control and surface water in global semi‐arid regions: A meta‐analysis

2020 ◽  
Vol 57 (5) ◽  
pp. 875-885 ◽  
Author(s):  
Gao‐Lin Wu ◽  
Yi‐Fan Liu ◽  
Zeng Cui ◽  
Yu Liu ◽  
Zhi‐Hua Shi ◽  
...  
Keyword(s):  
Surface Water ◽  
Soil Erosion ◽  
Arid Regions ◽  
Vegetation Type ◽  
Meta Analysis ◽  
Erosion Control ◽  
Trade Off ◽  
Soil Erosion Control ◽  
Semi Arid

scholarly journals Remote Sensing of Mine Site Rehabilitation for Ecological Outcomes: A Global Systematic Review

2020 ◽  
Vol 12 (21) ◽  
pp. 3535 ◽  
Author(s):  
Phillip B. McKenna ◽  
Alex M. Lechner ◽  
Stuart Phinn ◽  
Peter D. Erskine
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Mining Industry ◽  
Ecological Monitoring ◽  
Earth Observation ◽  
Future Research ◽  
Vegetation Development ◽  
Diverse Range ◽  
Mine Site ◽  
Site Rehabilitation

The mining industry has been operating across the globe for millennia, but it is only in the last 50 years that remote sensing technology has enabled the visualization, mapping and assessment of mining impacts and landscape recovery. Our review of published literature (1970–2019) found that the number of ecologically focused remote sensing studies conducted on mine site rehabilitation increased gradually, with the greatest proportion of studies published in the 2010–2019 period. Early studies were driven exclusively by Landsat sensors at the regional and landscape scales while in the last decade, multiple earth observation and drone-based sensors across a diverse range of study locations contributed to our increased understanding of vegetation development post-mining. The Normalized Differenced Vegetation Index (NDVI) was the most common index, and was used in 45% of papers; while research that employed image classification techniques typically used supervised (48%) and manual interpretation methods (37%). Of the 37 publications that conducted error assessments, the average overall mapping accuracy was 84%. In the last decade, new classification methods such as Geographic Object-Based Image Analysis (GEOBIA) have emerged (10% of studies within the last ten years), along with new platforms and sensors such as drones (15% of studies within the last ten years) and high spatial and/or temporal resolution earth observation satellites. We used the monitoring standards recommended by the International Society for Ecological Restoration (SER) to determine the ecological attributes measured by each study. Most studies (63%) focused on land cover mapping (spatial mosaic); while comparatively fewer studies addressed complex topics such as ecosystem function and resilience, species composition, and absence of threats, which are commonly the focus of field-based rehabilitation monitoring. We propose a new research agenda based on identified knowledge gaps and the ecological monitoring tool recommended by SER, to ensure that future remote sensing approaches are conducted with a greater focus on ecological perspectives, i.e., in terms of final targets and end land-use goals. In particular, given the key rehabilitation requirement of self-sustainability, the demonstration of ecosystem resilience to disturbance and climate change should be a key area for future research.

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