scholarly journals EMPREGO DO SENSORIAMENTO REMOTO PARA ANÁLISE DO USO E OCUPAÇÃO DO SOLO NO PERÍMETRO IRRIGADO VÁRZEAS DE SOUSA-PB

Irriga ◽  
2022 ◽  
Vol 1 (4) ◽  
pp. 722-729
Author(s):  
LEONCIO GONÇALVES RODRIGUES ◽  
ANA CÉLIA MAIA MEIRELES ◽  
CARLOS WAGNER OLIVEIRA
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Sustainable Development ◽  
Water Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ideal Tool ◽  
Major Producer ◽  
Irrigated Perimeter ◽  
Remote Sensing Techniques

EMPREGO DO SENSORIAMENTO REMOTO PARA ANÁLISE DO USO E OCUPAÇÃO DO SOLO NO PERÍMETRO IRRIGADO VÁRZEAS DE SOUSA-PB     LEONCIO GONÇALVES RODRIGUES1; ANA CÉLIA MAIA MEIRELES2 E CARLOS WAGNER OLIVEIRA3   1Mestrando em Desenvolvimento Regional Sustentável, Universidade Federal do Cariri-UFCA, Rua Ícaro Moreira de Sousa, nº 126, Muriti, 63130-025, Crato, Ceará, Brasil, [email protected]. 2 Professora titular do Programa de pós graduação em Desenvolvimento Regional Sustentável, Universidade Federal do Cariri-UFCA, Rua Ícaro Moreira de Sousa, nº 126, Muriti, 63130-025, Crato, Ceará, Brasil, [email protected]  3 Professor titular do Programa de pós graduação em Desenvolvimento Regional Sustentável, Universidade Federal do Cariri-UFCA, Rua Ícaro Moreira de Sousa, nº 126, Muriti, 63130-025, Crato, Ceará, Brasil, [email protected]     1 RESUMO   O perímetro irrigado várzeas de Sousa (PIVAS) é um grande produtor de culturas como coco, banana, sorgo, algodão dentre outras. Tem grande importância para o desenvolvimento econômico da região do alto sertão da Paraíba. Possui características impares como a distribuição de água para todos os lotes por potencial gravitacional. Para a sustentabilidade do perímetro é necessário o monitoramento constante de suas áreas, para se poder desenvolver estratégias que auxiliam no desenvolvimento sustentável. Nesse sentido, o sensoriamento remoto é uma ferramenta ideal por permitir a obtenção rápida e precisa de informações sobre uma área, o que pode auxiliar na tomada de decisão. Partindo desse pressuposto, o objetivo deste trabalho é apresentar um conjunto de técnicas de sensoriamento que possibilitem o monitoramento de áreas irrigadas ou ambientais. Para tanto foi determinado do uso e ocupação do solo, o índice de vegetação por diferença normalizada (NDVI) e o índice de vegetação ajustado ao solo (SAVI) para o PIVAS. Onde se observou que as técnicas de sensoriamento remoto auxiliam na compreensão de áreas no espaço e tempo.   Palavras-chave: monitoramento, manejo, satélite.     RODRIGUES, L. G.; MEIRELES, A. C. M.; OLIVEIRA, C, W. USE OF REMOTE SENSING TO ANALYZE THE USE AND OCCUPANCY OF THE SOIL IN THE PERIMETER IRRIGATED VÁRZEAS DE SOUSA-PB.     2 ABSTRACT   The floodplain-irrigated perimeter of Sousa (PIVAS) is a major producer of crops such as coconut, banana, sorghum, cotton, among others. It is of great importance for the economic development of the upper wilderness region of Paraiba. It has unique characteristics such as water distribution to all lots by gravitational potential. For the sustainability of the perimeter, constant monitoring of its areas is necessary, to be able to develop strategies that help in sustainable development. In this sense, remote sensing is an ideal tool as it allows for quick and accurate obtaining information about an area, which can help in decision making. Based on this assumption, this work aims to present a set of sensing techniques that enable monitoring of irrigated or environmental areas. For this purpose, the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI) were determined for the PIVAS. Where it was observed that remote sensing techniques help understand areas in space and time.   Keywords: monitoring, management, satellite.

scholarly journals MONITORING DESERTIFICATION IN SOME REGIONS OF IRAQ USING GIS TECHNIQUES

2021 ◽  
Vol 52 (3) ◽  
pp. 620-625
Author(s):  
Y. K. Al-Timimi
Keyword(s):  
Remote Sensing ◽  
Environmental Economic ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Marked Decrease ◽  
Social Systems ◽  
Sand Dunes ◽  
Rate Of Increase ◽  
Remote Sensing Techniques

Desertification is one of the phenomena that threatening the environmental, economic, and social systems. This study aims to evaluate and monitor desertification in the central parts of Iraq between the Tigris and Euphrates rivers through the use of remote sensing techniques and geographic information systems. The Normalized difference vegetation index NDVI and the crust index CI were used, which were applied to two of the Landsat ETM + and OLI satellite imagery during the years 1990 and 2019. The research results showed that the total area of ​​the vegetation cover was 2620 km2 in 1990, while there was a marked decrease in the area Vegetation cover 764 km2 in 2019, accounting for 34.8% (medium desertification) and 10.2% (high desertification), respectively. Also, the results showed that sand dunes occupied an area of ​​767 km2 in 1990, while the area of ​​sand dunes increased to 1723 km2 in 2019, with a rate of 10.2%) medium desertification (and 22.9% (severe desertification), respectively. It was noted that the overall rate of decrease in vegetation cover was 21.33 km2year-1 while the overall rate of increase in ground erosion in the area is 10.99 km2year-1.

scholarly journals Estimation of the Temporal Change in Carbon Stock of Muthupet Mangroves in Tamil Nadu Using Remote Sensing Techniques

2019 ◽  
pp. 1-7
Author(s):  
K. Narmada ◽  
K. Annaidasan
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Tamil Nadu ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Reliability And Validity ◽  
Avicennia Marina ◽  
Policy Makers ◽  
Remote Sensing Techniques ◽  
The Impact

Aim: To study the carbon storage potential of Muthupet mangroves in Tamil Nadu using Remote sensing techniques. Place and Duration: The study is carried out in Muthupet Mangroves for the years 2000, 2010 and 2017. Methodology: In this study the remote sensing images were processed using the ERDAS and ArcGIS software and the NDVI (Normalized Difference Vegetation Index) has also been applied to estimate the quantity of carbon sequestration capability for the Avicennia marina mangrove growing in the Muthupet region for the period 2000-2017. The formula proposed by Lai [10] was used to calculate the carbon stock using geospatial techniques. Results: The results show that the mangroves in Muthupet region has NDVI values between -0.671 and 0.398 in 2000, -0.93 and 0.621 in 2010 and -0.66 and 0.398 in 2017. The observation indicates the reliability and validity of the aviation remote sensing with high resolution and with near red spectrum experimented in this research for estimating the the Avicennia marina (Forsk.) mangrove growing in this region. The estimated quantity of carbon di oxide sequestrated by the mangrove was about 1475.642 Mg/Ha in 2000, 3646.312 Mg/Ha in 2010 and 1677.72 Mg/Ha in 2017. Conclusion: The capacity of the Avicennia marina growing in Muthupet region to sequestrate carbon show that it has a great potential for development and implementation. The results obtained in this research can be used as a basis for policy makers, conservationists, regional planners, and researchers to deal with future development of cities and their surroundings in regions of highly ecological and environmental sensitivity. Thus the finding shows that wetlands are an important ecological boon as it helps to control the impact of climate change in many different ways.

scholarly journals Classification of a burnt area based on spectral images

2018 ◽  
Vol 247 ◽  
pp. 00017
Author(s):  
Anna Szajewska
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Burnt Area ◽  
Forest Protection ◽  
The Earth ◽  
Remote Sensing Techniques ◽  
Spectral Ranges ◽  
Mathematical Operations

The use of remote sensing techniques allows obtaining information about processes that occur on the surface of the Earth. In the aspects of fire protection and forest protection, it is important to know a burnt area which was created as a result of a fire of the soil cover or a total fire. The knowledge of this area is necessary to assess losses. Remote sensing techniques allow obtaining images in various spectral ranges. Remote sensing satellites offer multi-band data. Mathematical operations that operate on values coming from different spectral ranges allow determining various remote sensing indicators. The manuscript presents the possibility of using the NDVI (Normalized Difference Vegetation Index) to classify the burnt area. The NDVI is relatively easy to obtain because it operates in the spectral ranges from 630 up to 915 nm, and is obtainable with one detector only. Thus, it can be obtained without any major problems using unmanned aerial vehicles, regardless of time and cloudiness, as is the case when acquiring satellite images. The manuscript describes experimental research and presents the results.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

scholarly journals Monitoring the Recent Activity of Landslides in the Mailuu-Suu Valley (Kyrgyzstan) Using Radar and Optical Remote Sensing Techniques

Geosciences ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 164
Author(s):  
Valentine Piroton ◽  
Romy Schlögel ◽  
Christian Barbier ◽  
Hans-Balder Havenith
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mining Area ◽  
Mass Movements ◽  
Optical Remote Sensing ◽  
Velocity Anomaly ◽  
In Situ Data ◽  
Remote Sensing Techniques ◽  
The Impact

Central Asian mountain regions are prone to multiple types of natural hazards, often causing damage due to the impact of mass movements. In spring 2017, Kyrgyzstan suffered significant losses from a massive landslide activation event, during which also two of the largest deep-seated mass movements of the former mining area of Mailuu-Suu—the Koytash and Tektonik landslides—were reactivated. This study consists of the use of optical and radar satellite data to highlight deformation zones and identify displacements prior to the collapse of Koytash and to the more superficial deformation on Tektonik. Especially for the first one, the comparison of Digital Elevation Models of 2011 and 2017 (respectively, satellite and unmanned aerial vehicle (UAV) imagery-based) highlights areas of depletion and accumulation, in the scarp and near the toe, respectively. The Differential Synthetic Aperture Radar Interferometry analysis identified slow displacements during the months preceding the reactivation in April 2017, indicating the long-term sliding activity of Koytash and Tektonik. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of both landslides. Furthermore, the analysis of the Normalized Difference Vegetation Index revealed land cover changes associated with the sliding process between June 2016 and October 2017. In addition, in situ data from a local meteorological station highlighted the important contribution of precipitation as a trigger of the collapse. The multidirectional approach used in this study demonstrated the efficiency of applying multiple remote sensing techniques, combined with a meteorological analysis, to identify triggering factors and monitor the activity of landslides.

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 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.

Monitoring recent activity of the Koytash Landslide (Kyrgyzstan) using radar and optical remote sensing techniques

2020 ◽  
Author(s):  
Valentine Piroton ◽  
Romy Schlögel ◽  
Hans-Balder Havenith
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Annual Rainfall ◽  
Optical Remote Sensing ◽  
Emergency Situations ◽  
Remote Sensing Techniques ◽  
Uranium Tailings ◽  
Meteorological Analysis

<p>Landslides are recurrent in most mountainous areas of the world where they frequently have catastrophic consequences. Around the Fergana Basin and in the Maily-Say Valley (Kyrgyzstan), landslides are often reactivated due to intense rainfalls, especially during spring, and as a consequence of the high seismicity characterizing the region. In spring 2017, Kyrgyzstan suffered a massive activation event which caused 160 emergency situations, including the reactivation of Koytash, one of the largest deep-seated mass movements of the Maily-Say area. In this region, risks related to landslides are accentuated by the presence of uranium tailings, remnants of the former nuclear mining activity. In this study, we used multiple satellite remote sensing techniques to highlight deformation zones and identify displacements prior to the collapse of Koytash. The comparison of multi-temporal digital elevation models (DEMs; satellite and UAV-based) enabled us to highlight areas of depletion and accumulation, in the scarp and foothill zones respectively. A differential synthetic aperture radar interferometry (D-InSAR) analysis and the computation of deformation time series allowed us to identify slope displacements and estimate the evolution of the displacement rates over time. This analysis identified slow displacements during the months preceding the reactivation, indicating the long-term sliding activity of Koytash, well before the reactivation in April 2017. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of Koytash. Furthermore, the use of optical imagery, through the difference of NDVIs (Normalized Difference Vegetation Index), revealed landcover changes associated to the sliding process. In addition to remote sensing techniques, we performed a meteorological analysis to identify the conditions that triggered the massive failure of Koytash. In-situ data from a local station highlighted the important contribution of precipitations as a trigger of the landslide movement. Indeed, despite a relative decrease in annual rainfall in 2017 compared to the previous years, the month of April 2017 was characterised by heavy rains, including a major peak of rainfall the day of Koytash’s failure. The multidirectional approach used in this study, demonstrated the efficiency of using multiple remote sensing techniques, combined to a meteorological analysis, to identify triggering factors and monitor the activity of landslides.</p>

scholarly journals Degradação Ambiental na Microrregião do Carirí Paraibano (Environmental Degradation in the Micro Region of Cariri Paraibano)

2013 ◽  
Vol 6 (4) ◽  
pp. 823
Author(s):  
Glauciene Justino Ferreira da Silva ◽  
Nadjacleia Vilar Almeida ◽  
Lidiane Cristina Félix Gomes ◽  
Otávia Karla Apolinário dos Santos
Keyword(s):  
Remote Sensing ◽  
Environmental Degradation ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Semiarid Region ◽  
Savanna Vegetation ◽  
Remote Sensing Techniques ◽  
Cover Class

O desmatamento de grandes áreas de vegetação de caatinga para dar lugar às lavouras e servir de pasto aos rebanhos tem contribuído para a degradação ambiental na região Semiárida. Os poucos remanescentes do Bioma Caatinga no Semiárido nordestino sofrem com a pressão exercida pelo avanço agropecuário e pelo descaso de órgãos ambientais de fiscalização. Na microrregião do Cariri paraibano muitos municípios tem perdido a cobertura vegetal em virtude da necessidade de terras para cultivo. Diante do exposto, fica clara a necessidade de estudos sobre a degradação ambiental, para isso o uso das geotecnologias tem proporcionado o monitoramento das alterações provocadas sem manejo adequado dos recursos naturais. O Sensoriamento Remoto e as imagens de sensores orbitais têm sido amplamente empregados em estudos ambientais, possibilitando a extração de informações. Desta forma, este trabalho objetivou avaliar a dinâmica da ocupação do solo e a cobertura vegetal no município de Pararí-PB entre os anos de 1988 e 2005, por meio de técnicas de Sensoriamento Remoto e análise espacial, além de contribuir com o estudo da degradação ambiental no Semiárido. Os resultados obtidos com os mapas de cobertura do solo evidenciaram que a classe solo exposto ocupou as áreas anteriormente pertencentes à classe vegetação densa e rala, expondo o solo do município aos efeitos das chuvas intensas e irregulares. O Índice de Vegetação da Diferença Normalizada (NDVI) melhor representou o estado da cobertura vegetal existente nos anos estudados, e a resposta espectral do solo e vegetação foram influenciados pela precipitação na época em que as imagens foram obtidas. A B S T R A C T The deforestation of large areas of savanna vegetation to make way for crops and serve as pasture to flocks has contributed to environmental degradation in the semiarid region. The few remaining Caatinga Biome in the northeastern Caatinga semiarid suffer from the pressure exerted by agricultural advances and the neglect of environmental enforcement agencies. In micro Cariri many municipalities have lost vegetation cover due to the need for land for cultivation. Given the above, it is clear the need for studies on environmental degradation. In that purpose, the use of geotechnology has provided monitoring changes caused without proper management of natural resources. The Remote Sensing and images from satellite sensors have been widely used in environmental studies, enabling the extraction of information. Thus, this study aimed to evaluate the dynamics of land use and vegetation cover in the municipality of Pararí-PB between the years 1988 and 2005, using remote sensing techniques and spatial analysis, and contributing to the study of environmental degradation in the semiarid. The results obtained with the maps of land cover class showed exposed soil areas previously occupied belonging to the class sparse and dense vegetation, exposing the municipality to the effects of heavy rains. The normalized difference vegetation index (NDVI) best represented the state of vegetation existing in the years studied, and the spectral response of soil and vegetation were influenced by precipitation at the time the pictures were taken. Key-words: Semiarid, vegetation cover, Remote Sensing.

scholarly journals Impact of Environmental and Climate Factors on Spatial Distribution of Cutaneous Leishmaniasis in Northeastern Iran: Utilizing Remote Sensing

2020 ◽  
Author(s):  
Mohammad Reza Shirzadi ◽  
Mohammad Javanbakht ◽  
Hassan Vatandoost ◽  
Nahid Jesri ◽  
Abedin Saghafipour ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Soil Moisture ◽  
Cutaneous Leishmaniasis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Specific Humidity ◽  
Climate Factors ◽  
Northern Regions ◽  
Remote Sensing Techniques

Background: Cutaneous leishmaniasis (CL) is a dermal and parasitic disease.. The aim of this study was to determine the effect of environmental and climate factors on spatial distribution of CL in northeastern Iran by utilizing remote sensing from 20 March 2016 to 19 March 2017. Methods: In this ecological study, the data were divided into two parts: The descriptive data on human CL cases were gathered from Communicable Diseases center of Iran. The remote sensing techniques and satellite imagery data (TRMM, MODIS-Aqua, MODIS-Terra and AMSR-2 with spatial resolution 0.25°, 0.05°, 5600m and 10km) of environ­mental and climate factors were used to determine the spatial pattern changes of cutaneous leishmaniasis inci­dence. Results: The incidence of CL in North Khorasan, Razavi Khorasan, and South Khorasan was 35.80 per 100,000 people (309/863092), 34.14 per 100,000 people (2197/6,434,501) and 7.67 per 100,000 people (59/768,898), respectively. The incidence of CL had the highest correlation with soil moisture and evapotranspiration. Moreover, the incidence of dis­ease was significantly correlated with Normalized Difference Vegetation Index (NDVI) and air humidity while it had the lowest correlation with rainfall. Furthermore, the CL incidence had an indirect correlation relation with the air tem­perature meaning that with an increase in the temperature, the incidence of disease decreased. Conclusion: As such, the incidence of disease was also higher in the northern regions; most areas of North Khorasan and northern regions of Razavi Khorasan; where the rainfall, vegetation, specific humidity, evapotranspiration, and soil moisture was higher than the southern areas.

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