Linking satellite-based forest cover change with rainfall and land surface temperature in Kerala, India

Forest cover change can cause strong local biophysical feedbacks on climate. Satellite observations of land surface temperature (T) and land cover distribution or forest cover change have been widely used to examine the effects of afforestation/deforestation on local surface temperature change (&#916;T). However, different approaches were used by previous analyses to quantifying &#916;T, and it remains unclear whether results of &#916;T by these approaches are comparable. We identified three influential approaches to quantifying &#916;T used by previous studies, namely the actual &#916;T resulting from actual changes in forest coverage over time and accounting for changes in background climate (&#916;Ta proposed by Alkama and Cescatti, 2016), potential &#916;T by hypothesizing potential shifts between non-forest and forest at given native spatial resolutions of satellite products (&#916;Tp1 by Li et al., 2015), and potential &#916;T, but using the singular value decomposition technique to derive &#916;T by hypothesizing a shift between a 100% complete non-forest and 100% forest (&#916;Tp2 by Duveiller et al., 2019). China realized large-scale afforestation making it a suitable test case to compare satellite-based approaches for estimating &#916;T following afforestation. We hypothesize that (1) &#916;Ta depends on the fraction of ground area that&#8217;s been afforested (Faff). (2) The relative magnitude between different approaches should be: &#916;Ta < &#916;Tp1 < &#916;Tp2. (3) When &#916;Ta is extended to a hypothetical case that Faff reaches 100%, it should be comparable to &#916;Tp1 or &#916;Tp2. We used multiple satellite observation products to test these hypotheses. The results show that the magnitude of actual daytime surface cooling by afforestation (&#916;Ta) increases with Faff, and is significantly lower than &#916;Tp1 and &#916;Tp2. But no significant difference was found between &#916;Tp1 and &#916;Tp2. A linear regression model established between &#916;Ta and Faff extends the &#916;Ta, when Faff reaches 100%, to a comparable magnitude than &#916;Tp1 and &#916;Tp2. Our study thus highlights the importance to consider the actual surface cooling impact by afforestation projects in contrast to the potential effects, and provides a first study to reconcile different approaches to quantify the land surface temperature change due to afforestation.

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A Time Series Analysis of Forest Cover and Land Surface Temperature Change Over Dudpukuria-Dhopachari Wildlife Sanctuary Using Landsat Imagery

Frontiers in Forests and Global Change ◽

10.3389/ffgc.2021.687988 ◽

2021 ◽

Vol 4 ◽

Author(s):

G. N. Tanjina Hasnat

Keyword(s):

Surface Temperature ◽

Temperature Change ◽

Land Surface Temperature ◽

Land Surface ◽

Forest Cover ◽

Thematic Mapper ◽

Forest Cover Change ◽

Wildlife Sanctuary ◽

Time Period ◽

The Impact

Forest cover change is an important criterion as it affects the environmental balance whereas land surface temperature is a significant parameter within the earth climate system. Spatio-temporal change of forest cover can be detected and land surface temperature can be retrieved by applying remote sensing technology. The present study aimed to capture the impact of forest cover change on land surface temperature in Dudpukuria-Dhopachari Wildlife Sanctuary (DDWS), Bangladesh, using multi-spectral and multi-temporal satellite data. To avoid the biasness in the calculation, leaf flash time was targeted for collecting Landsat images from United States Geological Survey (USGS) Earth Explorer and, based on availability, images were collected purposively which ones had closer time period:1990 (March 5, 1990), 2000 (February 5, 2000), 2010 (February 24, 2010) and 2020 (March 23, 2020). Unsupervised classification was applied over the images Landsat 4–5 Thematic Mapper (TM), 7 Enhanced Thematic Mapper Plus (ETM+), and 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data for detecting forest cover change. To retrieve the land surface temperature, Mono Window Algorithm (MWA) method was applied over similar images. Maximum forest degradation was observed in 2010 and the change found was 17% as compared to 1990. After 2010, the forest started to flourish. Land surface temperature dramatically changes over the time period. The highest land surface temperature in the forested area was observed in 2020 (32.2°C) and it was changed 7.7°C from that of the 1990 (24.5°C). In every 10 years, almost 2.3°C–3.0°C temperature change was detected. In the first three decades, a reverse relationship was observed between land surface temperature and forest cover; however, in the last decade, land surface temperature was found to increase with the increase of forest cover. Thus, the results of the study revealed that land surface temperature may not be relevant with the local forest cover change directly. It can be estimated from the results that local forest cover change may have limited impact on local temperature rather than global forest cover change, whereas global warming could play a vital role in changing land surface temperature locally as well as globally.

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The Influence of Deforestation on Land Surface Temperature—A Case Study of Perak and Kedah, Malaysia

Forests ◽

10.3390/f11060670 ◽

2020 ◽

Vol 11 (6) ◽

pp. 670 ◽

Cited By ~ 9

Author(s):

Wan Shafrina Wan Mohd Jaafar ◽

Khairul Nizam Abdul Maulud ◽

Aisyah Marliza Muhmad Kamarulzaman ◽

Asif Raihan ◽

Syarina Md Sah ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Forest Cover ◽

Environmental Changes ◽

Rapid Change ◽

Spectral Radiance ◽

Landsat 8 ◽

Forest Cover Change

Over the past few decades, there has been a rapid change in forest and land cover, especially in tropical forests due to massive deforestation. The major factor responsible for the changes is to fulfill the growing demand of increasing population through agricultural intensification, rural settlements, and urbanization. Monitoring forest cover and vegetation are essential for detecting regional and global environmental changes. The present study evaluates the influence of deforestation on land surface temperature (LST) in the states of Kedah and Perak, Malaysia, between 1988 and 2017. The trend in forest cover change over the time span of 29 years, was analyzed using Landsat 5 and Landsat 8 satellite images to map the sequence of forest cover change. With the measurement of deforestation and its relationship with LST as an end goal, the Normalized Difference Vegetation Index (NDVI) was used to determine forest health, and the spectral radiance model was used to extract the LST. The findings of the study show that nearly 16% (189,423 ha) of forest cover in Perak and more than 9% (33,391 ha) of forest cover in Kedah have disappeared within these 29 years as a result of anthropogenic activities. The correlation between the LST and NDVI is related to the distribution of forests, where LST is inversely related to NDVI. A strong correlation between LST and NDVI was observed in this study, where the average mean of LST in Kedah (25 °C) is higher than in Perak (22.6 °C). This is also reflected by the decreased NDVI value from 0.6 to 0.5 in 2017 at both states. This demonstrated that a decrease in the vegetation area leads to an increase in the surface temperature. The resultant forest change map would be helpful for forest management in terms of identifying highly vulnerable areas. Moreover, it could help the local government to formulate a land management plan.

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LAND SURFACE TEMPERATURE ANOMALIES IN RESPONSE TO CHANGES IN FOREST COVER

International Journal of Engineering and Geosciences ◽

10.26833/ijeg.549944 ◽

2019 ◽

Author(s):

Behnam Khorrami ◽

Orhan Gunduz ◽

Nilanchal Patel ◽

Souad Ghouzlane ◽

Mohamed Najjar

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Forest Cover ◽

Temperature Anomalies

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An assessment of the Relationship between Vegetation (Forest Density Cover) Built-Up Area and City Land Surface Temperature, Generated by LANDSAT-8 OLI/ Thermal band - A Case study of Dehradun, India

Journal of Mountain Research ◽

10.51220/jmr.v16i3.13 ◽

2021 ◽

Vol 16 (3) ◽

Author(s):

Rajeev Shankhwar ◽

Rajlakshmi Datta ◽

Navendra Uniyal

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Areas ◽

Forest Cover ◽

Past Research ◽

Present Condition ◽

Landsat 8 ◽

Forest Density ◽

Open Land

Dehradun city is the capital of Uttarakhand state of India. Evidence from the past research and literature [e.g. CDP 2007, Singh et al 2013, Gupta et al 2014] shows that in the late 80s, Dehradun city was much greener compared to the present condition. In the current study, we tried to identify the correlation between land surface temperature (LST) with Forest cover density classes (FCDC) and built-up area with open land. The current study reveals that there is a relationship between FCDC and LST in the study area. The range of LST recorded is between 32.07 to 43.99 °C. Among all the classes, minimum LST record in VDF class is 32.07°C and maximum LST record in built-up area 43.99°C. The present study shows the importance of vegetation cover in urban areas to reduce LST, air temperature and maintain the urban microclimate as well as to help reduce air pollution.

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Quantifying the Actual Impacts of Forest Cover Change on Surface Temperature in Guangdong, China

Remote Sensing ◽

10.3390/rs12152354 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2354

Author(s):

Wenjuan Shen ◽

Jiaying He ◽

Chengquan Huang ◽

Mingshi Li

Keyword(s):

Surface Temperature ◽

Air Temperature ◽

Land Surface ◽

Forest Cover ◽

Temperature Response ◽

Forest Loss ◽

Forest Cover Change ◽

Local Surface ◽

Forest Change ◽

Gain And Loss

Forest cover change is critical in the regulation of global and regional climate change through the alteration of biophysical features across the Earth’s surface. The accurate assessment of forest cover change can improve our understanding of its roles in the regulation processes of surface temperature. In spite of this, few researchers have attempted to discern the varying effects of multiple satellite-derived forest changes on local surface temperatures. In this study, we quantified the actual contributions of forest loss and gain associated with evapotranspiration (ET) and albedo to local surface temperature in Guangdong Province, China using an improved spatiotemporal change pattern analysis method, and explored the interrelationships between surface temperature and air temperature change. We specifically developed three forest change products for Guangdong, combining satellite observations from Landsat, PALSAR, and MODIS for comparison. Our results revealed that the adjusted simple change detection (SCD)-based Landsat/PALSAR forest cover data performed relatively well. We found that forest loss and gain between 2000 and 2010 had opposite effects on land surface temperature (LST), ET, and albedo. Forest gain led to a cooling of −0.12 ± 0.01 °C, while forest loss led to a warming of 0.07 ± 0.01 °C, which were opposite to the anomalous change of air temperature. A reduced warming to a considerable cooling was estimated due to the forest gain and loss across latitudes. Specifically, mid-subtropical forest gains increased LST by 0.25 ± 0.01 °C, while tropical forest loss decreased LST by −0.16 ± 0.05 °C, which can demonstrate the local differences in an overall cooling. ET induced cooling and warming effects were appropriate for most forest gain and loss. Meanwhile, the nearby temperature changes caused by no-change land cover types more or less canceled out some of the warming and cooling. Albedo exhibited negligible and complex impacts. The other two products (i.e., the GlobeLand30 and MCD12Q1) affect the magnitude of temperature response due to the discrepancies in forest definition, methodology, and data resolution. This study highlights the non-negligible contributions of high-resolution maps and a robust temperature response model in the quantification of the extent to which forest gain reverses the climate effects of forest loss under global warming.

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Optimal use of land surface temperature data to detect changes in tropical forest cover

Journal of Geophysical Research Atmospheres ◽

10.1029/2010jg001488 ◽

2011 ◽

Vol 116 (G2) ◽

Cited By ~ 26

Author(s):

Thijs T. van Leeuwen ◽

Andrew J. Frank ◽

Yufang Jin ◽

Padhraic Smyth ◽

Michael L. Goulden ◽

...

Keyword(s):

Surface Temperature ◽

Tropical Forest ◽

Land Surface Temperature ◽

Land Surface ◽

Forest Cover ◽

Temperature Data ◽

Surface Temperature Data

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Spaceborne Thermal Remote Sensing for Characterization of the Land Surface Temperature of Manmade and Natural Features

Proceedings ◽

10.3390/asec2020-07568 ◽

2020 ◽

Vol 67 (1) ◽

pp. 2

Author(s):

Sakshi Jain ◽

Shashi Kumar

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Forest Cover ◽

Atmospheric Correction ◽

Landsat 8 ◽

Doon Valley ◽

Window Method ◽

Urban Settlements

The changes in land surface temperature (LST) concerning time and space are mapped with the help of satellite remote sensing techniques. These measurements are used for determining several geophysical parameters including soil moisture, evapotranspiration, thermal inertia, and vegetation water stress. This study aims at calculating and analyzing the LST of manmade and natural features of Doon Valley, Uttarakhand, India. The study area includes the forest range of Doon Valley, agricultural areas, and urban settlements. Spaceborne multitemporal thermal bands of Landsat 8 were used to calculate the LST of various features of the study area. Split-window algorithm and emissivity-based algorithms were tested on the Landsat-8 data for LST calculation. The study also explored the effect of atmospheric correction on the temperature calculation. The land surface temperature determined using an emissivity based method that did not provide atmospheric correction was found to be less accurate as compared to the results by the split-window method. The LST for urban settlements is higher than the forest cover. A temporal analysis of the data shows an increase in the temperature for October 2018. The study shows the potential of the spaceborne thermal sensors for the multitemporal analysis of the LST measurement of manmade and natural features.

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Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

Annals of Valahia University of Targoviste Geographical Series ◽

10.2478/avutgs-2018-0002 ◽

2018 ◽

Vol 18 (1) ◽

pp. 14-22 ◽

Cited By ~ 1

Author(s):

Georgiana Grigoraș ◽

Bogdan Urițescu

Keyword(s):

Surface Temperature ◽

Air Temperature ◽

Land Surface Temperature ◽

Hot Spots ◽

Land Surface ◽

Hot Spot ◽

Monitoring Network ◽

Residential Areas ◽

Hotspot Analysis ◽

The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

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