Applicability of MODIS land cover and Enhanced Vegetation Index (EVI) for the assessment of spatial and temporal changes in strength of vegetation in tropical rainforest region of Borneo

Vijith, H.; Dodge-Wan, Dominique

doi:10.1016/j.rsase.2020.100311

Cited by 30 publications

(18 citation statements)

References 62 publications

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“…In the Amazon, regenerating tropical forests, between 5 and 20 years old, showed NDVI comparable, or slightly higher, than the primary forest (Steininger, 1996). In Borneo, the rapid regrowth of secondary vegetation after logging (classified as woody savannas and grasslands) showed slightly higher EVI than the evergreen broad‐leaved forests, that is, vegetation prior to logging, but overall, these vegetation types were not distinguishable based on EVI (Vijith & Dodge‐Wan, 2020). The levels of NDVI and EVI in our data comply with previous observations from a seasonal tropical humid broad‐leaved forest in Tapajós, Brazil (Huete et al, 2002) and tropical broad‐leaved forest in Borneo (Vijith & Dodge‐Wan, 2020); both of these works were based on MODIS datasets as used in this work.…”

Section: Discussionmentioning

confidence: 99%

“…In Borneo, the rapid regrowth of secondary vegetation after logging (classified as woody savannas and grasslands) showed slightly higher EVI than the evergreen broad‐leaved forests, that is, vegetation prior to logging, but overall, these vegetation types were not distinguishable based on EVI (Vijith & Dodge‐Wan, 2020). The levels of NDVI and EVI in our data comply with previous observations from a seasonal tropical humid broad‐leaved forest in Tapajós, Brazil (Huete et al, 2002) and tropical broad‐leaved forest in Borneo (Vijith & Dodge‐Wan, 2020); both of these works were based on MODIS datasets as used in this work. However, the level of NDVI in our data was higher than what was previously reported in Kibale National Park by Hartter et al (2011) but slightly lower than that reported in the Amazon (Steininger, 1996); both works are based on LANDSAT images.…”

Section: Discussionmentioning

confidence: 99%

“…Both NDVI and EVI are low in areas with sparse vegetation cover (deserts), intermediate in shrublands and savannas (with high seasonal variation in grassland ecosystems), and reach the highest values in broadleaved forests (Huete et al, 2011). In the tropics, vegetation indices have been previously used for land‐cover classification and to detect land‐cover dynamics (Hartter et al, 2011; Setiawan et al, 2014; Tucker et al, 1985; Vijith & Dodge‐Wan, 2020; Wanyama et al, 2020), map forest disturbances (Murillo‐Sandoval et al, 2017), predict forest resilience to drought (Verbesselt et al, 2016), monitor natural succession (Caughlin et al, 2021), estimate large‐scale patterns in biomass (Anaya et al, 2009) or primary production (Sjöström et al, 2011), and detect seasonal phenological rhythms and photosynthetic capacity (Brando et al, 2010; Valtonen et al, 2013; Xiao et al, 2006). For example, in the Amazon, the first phase of forest regrowth can be detected as an increase in NDVI (Steininger, 1996).…”

Section: Introductionmentioning

confidence: 99%

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Remotely sensed vegetation greening along a restoration gradient of a tropical forest, Kibale National Park, Uganda

et al. 2021

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Restoration has now emerged as a global priority, with international initiatives such as the “UN Decade on Ecosystem Restoration (2021–2030)”. To fulfill the large‐scale global restoration ambitions, an essential step is the monitoring of vegetation recovery after restoration interventions. This study aimed to evaluate the utility of remotely sensed vegetation indices, using the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), to monitor the progress of forest regeneration across a tropical forest restoration project area in Kibale National Park, Uganda. Using the chronosequence approach, results indicated non‐linear patterns in NDVI and EVI across the first 25 years of recovery. Both NDVI and EVI increased for the first 10 years of forest regeneration. This 'greening' phase could be used as the indicator of the successful onset of forest recovery. In particular, the decline of elephant grass, and the consequent arrival of shrubs and trees, can be detected as an increase in NDVI. Primary forests differed from the 25‐year‐old regenerating forests based on the unique combination of low mean and low seasonal variation in EVI. Our results, therefore, suggest that the long‐term success of forest restoration could be monitored by evaluating how closely the combination of mean, and degree of seasonal variation in EVI, resembles that observed in the primary forest.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remotely sensed vegetation greening along a restoration gradient of a tropical forest, Kibale National Park, Uganda

et al. 2021

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“…First, we used the MRT (MODIS Reprojection Tool) to splice the data of the three scenes, selected the band of LST_Day_1 km, and added the geographic coordinate system [27], the scan width is 2330 km, and the observation data contain 36 discrete spectral bands [28]. This multi-wavelength observational equipment can closely scrutinize the dynamic changes of various features on the earth, such as changes in surface temperature [29], vegetation coverage [30], aerosol and atmospheric properties [31][32][33][34], etc. This paper employs the LST product retrieved by the land standard product MOD11A2 on the Aerra satellite (data source: https://ladsweb.modaps.eosdis.…”

Section: Modis Data Preprocessingmentioning

confidence: 99%

Spatiotemporal Differentiation of Land Surface Thermal Landscape in Yangtze River Delta Region, China

Wang

Liu

2021

Sustainability

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Advancements in the integrated development of the Yangtze River Delta are changing the structure and function of the surface thermal landscape and triggering a series of ecological and environmental problems. Therefore, examining the spatiotemporal differentiation characteristics and evolution laws of this land surface thermal landscape has great theoretical and practical significance in the context of optimizing functional zoning and realizing the harmonious development of the economy, society and nature. The paper uses the LST (land surface temperature) data retrieved by MODIS (MOD11A2) remote sensing satellites in 2007, 2010, 2013, 2016 and 2019 to extract a land surface thermal rating map of the Yangtze River Delta region, and to analyze the spatiotemporal differentiation in the land surface thermal landscape, combining of the land surface thermal landscape strip profile and thermal landscape pattern indices. The results show that the LST in the Yangtze River Delta region has increased in the past 12 years, the proportion of middle-, sub-high- and high-temperature zones increased by 33.42%, and the high-temperature zone has gradually extended into inland areas. The high-temperature zones in the areas surrounding core cities such as Shanghai, Nanjing, and Hangzhou have expanded. The corridor effect of thermal changes on the surface is obvious. The degree of aggregation in the lower-temperature areas has gradually decreased. The degree of aggregation in the higher-temperature regions has increased. The patch types of thermal landscape pattern increase, and the distribution of landscape area among various types tends to be even. this trend is most significant in optimized development region.

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“…Ürünün sahip olduğu 500 m' lik mekânsal çözünürlük küçük alanlardaki kullanım için kısıtlayıcı olsa da, bölgesel ölçekte arazi örtüsü geçişlerini yakalamak için uygundur. Literatürde bölgesel ve yerel değerlendirmeler için MODIS arazi örtüsü ürünleri kullanılmıştır (Sharma vd., 2017;Vijith & Dodge-Wan, 2020).…”

Section: Introductionunclassified

İç Anadolu Bölgesi’ ndeki Tarım Alanı Değişimlerinin Modis Uydu Verisi ile İzlenmesi

Matcı¹

2021

Turkish Journal of Remote Sensing

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nüfus artışı, hızlı ve kontrolsüz kentleşme ve iklim değişikliği gibi parametreler tarım alanların varlığını etkileyen sebeplerdendir. Artan nüfusun beslenme ihtiyaçlarının karşılanabilmesi için tarım alanlarının izlenmesi korunması ve planlanması gerekmektedir. Tarım alanlarının korunabilmesi için güvenli ve hızlı güncellenebilir verilerle izlenmesi gerekmektedir. Teknolojinin artmasıyla beraber uzaktan algılama yöntemleri çok değerli veriler sağlamaktadır. Böylece, arazi örtüsü sınıflarının alan kapsamını tahmin etmek için yaygın ve etkili bir yöntem haline gelmiştir. Bu çalışmada uzaktan algılanmış veriler yardımıyla İç Anadolu Bölgesindeki tarım arazilerin 2001-2019 yılları arasındaki değişim dinamikleri incelenmiştir. Bu doğrultuda alansal değişimlerini incelemek için Google Earth Engine(GEE) kullanılarak MCD12Q1.006 MODIS Land Cover Type Yearly Global 500 m verileri, yağış verilerini izlemek amacıyla Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) veri seti, nüfus verilerini incelemek için TUIK verileri ve sıcaklık verilerini incelemek amacıyla ERA5 Daily veri seti kullanılmıştır. Çalışma sonucunda elde edilen sonuçlar incelendiğinde tarım alanlarında artış olduğu gözlemlenmiştir. Bu değişimi tetikleyen faktörler incelendiğinde tarımsal alan değişiminin nüfus verisiyle anlamlı bir ilişkisi olduğu gözlemlenmiştir.

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Applicability of MODIS land cover and Enhanced Vegetation Index (EVI) for the assessment of spatial and temporal changes in strength of vegetation in tropical rainforest region of Borneo

Cited by 30 publications

References 62 publications

Remotely sensed vegetation greening along a restoration gradient of a tropical forest, Kibale National Park, Uganda

Remotely sensed vegetation greening along a restoration gradient of a tropical forest, Kibale National Park, Uganda

Spatiotemporal Differentiation of Land Surface Thermal Landscape in Yangtze River Delta Region, China

İç Anadolu Bölgesi’ ndeki Tarım Alanı Değişimlerinin Modis Uydu Verisi ile İzlenmesi

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