Use of remote sensing to study the impact of land-cover/land-use change on the environment: a Baltimore area, Maryland case study

Abstract. Disturbance of vegetation is a critical component of land cover, but is generally poorly constrained in land surface and carbon cycle models. In particular, land-use change and fire can be treated as large-scale disturbances without full representation of their underlying complexities and interactions. Here we describe developments to the land surface model JULES (Joint UK Land Environment Simulator) to represent land-use change and fire as distinct processes which interact with simulated vegetation dynamics. We couple the fire model INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) to dynamic vegetation within JULES and use the HYDE (History Database of the Global Environment) land cover dataset to analyse the impact of land-use change on the simulation of present day vegetation. We evaluate the inclusion of land use and fire disturbance against standard benchmarks. Using the Manhattan metric, results show improved simulation of vegetation cover across all observed datasets. Overall, disturbance improves the simulation of vegetation cover by 35 % compared to vegetation continuous field (VCF) observations from MODIS and 13 % compared to the Climate Change Initiative (CCI) from the ESA. Biases in grass extent are reduced from −66 % to 13 %. Total woody cover improves by 55 % compared to VCF and 20 % compared to CCI from a reduction in forest extent in the tropics, although simulated tree cover is now too sparse in some areas. Explicitly modelling fire and land use generally decreases tree and shrub cover and increases grasses. The results show that the disturbances provide important contributions to the realistic modelling of vegetation on a global scale, although in some areas fire and land use together result in too much disturbance. This work provides a substantial contribution towards representing the full complexity and interactions between land-use change and fire that could be used in Earth system models.

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Land Use Land Cover Changes Using Remote Sensing and GIS Technique: A Case Study of Navsari District, Gujarat

Journal of Tree Sciences ◽

10.5958/2455-7129.2017.00019.x ◽

2017 ◽

Vol 36 (2) ◽

pp. 20

Author(s):

Dileswar Nayak ◽

Nilam Surve ◽

P.K. Shrivastava

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Remote Sensing And Gis ◽

Land Cover Changes ◽

Land Use Land Cover ◽

Gis Technique

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INTEGRATED USE OF REMOTE SENSING, GIS AND GPS TECHNOLOGY FOR MONITORING THE ENVIRONMENTAL PROBLEM OF SHYAMNAGAR

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-w1-357-2017 ◽

2017 ◽

Vol XLII-1/W1 ◽

pp. 357-364 ◽

Cited By ~ 1

Author(s):

M. S. Akbar ◽

M. H. Sarker ◽

M. A. Sattar ◽

G. M. Sarwar ◽

S. M. M. Rahman ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Salt Water ◽

Shrimp Farming ◽

Salt Water Intrusion ◽

Agricultural Practice ◽

Agriculture Land ◽

The Impact ◽

Shrimp Cultivation

Cultivation of shrimp mostly in unplanned way has been considered as one of the major environmental disasters of Shamnagar. Villagers surrounding the rivers are mainly involved with fish (shrimp) cultivation. So, fertile agriculture land has been converted to shrimp cultivation. Conversion of agriculture land to other usage is a common but acute problem for land resources of the country like Bangladesh. Conventional methods for collecting this information are relatively costly and time consuming. Contrarily, Remote Sensing satellite observation with its unique capability to provide cost-effective support in compiling the latest information about the natural resource. Remote sensing, in conjunction with GIS, has been widely applied and been recognized as a powerful and effective tool in detecting land use and land cover changes. RapidEye, Landsat8 images were used to identify land use and land cover of the area during the period 2008 and 2015. Google images were used to identify the micro-level land use features of the same period. Multi-spectral classifications using unsupervised and supervised classification were done and results have been compared based on the field investigation. The study reveals that during the period 2008 to 2015 agricultural practice has been reduced from 35&thinsp;% to 21&thinsp;% and shrimp cultivation area increased from 38&thinsp;% to 50&thinsp;%. Due to the impact of high salinity and salt water intrusion caused by natural disaster, agricultural activities is reduced and farmers have been converted to other practices, as a result shrimp farming is gaining popularity in the area.

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