Persistent negative changes in seasonal greenness over different forest types of India using MODIS time series NDVI data (2001–2014)

Chakraborty, Abhishek; Seshasai, M. V. R.; Reddy, C. Sudhakar; Dadhwal, V. K.

doi:10.1016/j.ecolind.2017.11.032

Cited by 58 publications

(30 citation statements)

References 28 publications

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“…Such analysis gives emphasis on implementing REDD+ as early as possible. Similar findings of negative change were reported by Chakraborty et al (2018) utilizing the long-term MODIS NDVI data. The negative change in vegetation not only leads to degradation in the natural forest but also into the agriculture and plantation sector adversely impact food security, livelihood resilience, regional biodiversity, and the climate.…”

Section: The Analysis Of Lulc Categoriessupporting

confidence: 86%

The Geospatial Understanding of Climate-Smart Agriculture and REDD+ Implementation: Indian Perspective

Ahmad¹,

Farooq

Goparaju³

et al. 2020

Ekológia (Bratislava)

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Geospatial technology has an enormous capacity to analyze large and diversified datasets for evaluating the hidden spatial relationship which provides a better comprehension of the subject and helps significantly in policymaking and planning future strategies.This study has examined the relationship among diversified remote sensing and GIS datasets such as GHG emission from cropland, rice cultivation area, agro-ecological region, Land use/Land cover (LULC) categories, long-term NDVI (1982−2006) based negative changes, agriculture vulnerability, drought-prone area and future (2021, 2050) climate change anomalies (RCP-6) of India for better understanding and knowledge of the GHG emission scenario, vegetation health, LULC, agriculture vulnerability, and future climate change impact. The LULC analysis revealed that 49.6% (1 628 959 km²) of the geographical area was found to be under category ‘cropland’. The 32.5% of the total cropland areas are used for rice cultivation whereas around 76% of this rice cultivation area is producing high GHG emission (>1000 Mg CO2 e/yr.). LULC categories ‘Cropland’ and ‘Plantation’ show the long-term (1982−2006) negative change equivalent to 19.7 and 70.2% respectively. Similarly, around 56% of LULC categories representing the forest show the long-term negative change whereas the maximum change (139 867 km²) was found in the category of ‘Deciduous Broadleaf Forest’. The 30.6% of the LULC category of ‘cropland’ falls in very high agriculture vulnerable areas whereas 31.7% of the same category falls in the drought-prone area. The significant increase in temperature and abrupt rainfall patterns were observed during Kharif and Rabi seasons in the future. Such variation of climate parameter in the future not only adversely affect the agriculture crop production but also the natural vegetation of India.The outcomes of the present study would support the policymakers of India to implement the climate-smart agriculture (CSA) and REDD+ on an urgent priority based on a proper evaluation of the socio-economic condition of the poor people. It will certainly help in the reduction of GHG emission, forest amelioration, will bring the resilience in livelihood and mitigate the poverty among the rural communities for the betterment of people.

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Section: The Analysis Of Lulc Categoriessupporting

confidence: 86%

The Geospatial Understanding of Climate-Smart Agriculture and REDD+ Implementation: Indian Perspective

Ahmad¹,

Farooq

Goparaju³

et al. 2020

Ekológia (Bratislava)

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“…We used satellite imagery from Sentinel-2 (SERCO, 2017) taken on June 23, 2016 with four-band (blue, green, red, near infrared) spectral regions at 10 m resolution. Multispectral bands were used to represent the study area topography and to derive the Normalized Difference Vegetation Index (NDVI; Rouse et al, 1974), which is characterized along a scale from À1 to +1 where values around or below 0 typically indicate no vegetation present, and higher values indicate green vegetation (Chakraborty et al, 2018). We rescaled a 2 m DEM from the Land Information Ontario database (Digital Raster Acquisition Project Eastern Ontario; Land Information Ontario, 2014) to 10 m to match the resolution of the multispectral bands.…”

Section: Habitat Usementioning

confidence: 99%

Population reinforcement accelerates subadult recruitment rates in an endangered freshwater turtle

Carstairs

Paterson

Jäger

et al. 2019

Animal Conservation

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Wildlife diversity and abundance are declining globally and population reinforcement with captive‐reared animals is a common intervention used to prevent extinctions. Released captive‐reared individuals may undergo an acclimation period before their behavior and success is comparable to wild‐reared individuals because they lack experience with predators, complex habitats and variable environmental conditions. Quantifying post‐release acclimation effects on fitness and behavior is important for maximizing the success of reintroduction programs and for predicting the number of captive‐reared animals required for release. Endangered Blanding's turtles Emydoidea blandingii exhibit low recruitment and may benefit from population reinforcement with captive‐reared, ‘headstarted’ individuals (headstarts). We used 6 years of data to compare survival, growth, habitat use and movement ecology between wild‐hatched juvenile turtles and headstarts reared from eggs rescued from injured females. We found strong evidence of an acclimation effect in headstarts, with lower movement, growth, and survival during the first one to two years post‐release. Following this acclimation period, headstarts had movement, growth and survival similar to wild‐hatched juveniles. Habitat use did not differ between headstarts and wild‐hatched juveniles. We hypothesize that the acclimation period occurred because headstarts were introduced directly into the wild (i.e. ‘hard release’) and that providing additional support before or after release may improve the success of headstarts. Headstarts had a monthly survival probability of 0.89 in the first year post‐release, and 0.98 after the first year post‐release. We estimated that headstarts at our sites have approximately three times higher probability of surviving to 10 years of age, compared to wild‐hatched individuals at other sites. Our results highlight that headstarts should be released into habitat individually rather than in clusters, and highlight the need to investigate whether post‐release mortality of captive‐reared animals could be mitigated by increased acclimation to wild conditions, for example through prerelease periods in outdoor pens.

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“…Values close to zero indicate sparse vegetation on bare soil [24]. The NDVI is the remote sensing product most widely used worldwide to analyse and map differences in vegetation types and plant phenology [25] including to estimate the diversity of trees over large areas when the vegetation is at the maximum growing season [26][27][28][29]. The NDVI can be utilized for future urban planning, urban restoration and monitoring of urban tree health in Bangkok.…”

Section: Discussionmentioning

confidence: 99%

Ecological Structure of a Tropical Urban Forest in the Bang Kachao Peninsula, Bangkok

Sommeechai

Wachrinrat

Dell

et al. 2018

Forests

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Abstract:Rapid urbanization has changed the structure and function of natural ecosystems, especially floodplain ecosystems in SE Asia. The ecological structure of vegetation stands and the usefulness of satellite images was investigated to characterize a disturbed tropical urban forest located in the Chao Phraya River lower floodplain, Thailand. Nine sample plots were established on the Bang Kachao Peninsula (BKP) within 4 tropical forest types in an urban area: rehabilitation forest, home-garden agroforestry, mangrove and park. The tree habitats were beach forest, swamp forest, moist evergreen forest, dry evergreen forest, mangrove forest and abandoned orchard or home-garden. Normalized difference vegetation index (NDVI) values obtained from Landsat 7 satellite images were correlated with plant structure from field surveys. NDVI had the highest relationship with stand factors for number of families, number of species, Shannon-Weiner index and total basal area. Linear regression predicted well the correlation between NDVI and stand factors for families and basal area. NDVI trends reflected urban tropical forest typing and biodiversity, being high in rehabilitation and mangrove forests, moderate in home-gardens and low in parks. We suggest that the application of NDVI for assessments can be useful for future planning, monitoring and management of the BKP and hence may contribute for increasing biodiversity and complexity of these urban forests.

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Persistent negative changes in seasonal greenness over different forest types of India using MODIS time series NDVI data (2001–2014)

Cited by 58 publications

References 28 publications

The Geospatial Understanding of Climate-Smart Agriculture and REDD+ Implementation: Indian Perspective

The Geospatial Understanding of Climate-Smart Agriculture and REDD+ Implementation: Indian Perspective

Population reinforcement accelerates subadult recruitment rates in an endangered freshwater turtle

Ecological Structure of a Tropical Urban Forest in the Bang Kachao Peninsula, Bangkok

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