Climate and Desertification in the Dry Land Asia

 

Future Climate Impact on the Desertification in the Dry Land Asia Using AVHRR GIMMS NDVI3g Data

by Lijuan MiaoPeilong Ye, Bin He, Lizi Chen and Xuefeng Cui

in Remote Sens. 2015, 7(4), 3863-3877

Abstract:

Dry Land Asia is the largest arid and semi-arid region in the northern hemisphere that suffers from land desertification. Over the period 1982–2011, there were both overall improvement and regional degeneration in the vegetation NDVI. We analyze future climate changes in these area using two ensemble-average methods from CMIP5 data. Bayesian Model Averaging shows a better capability to represent the future climate and less uncertainty represented by the 22-model ensemble than does the Simple Model Average. From 2006 to 2100, the average growing season temperature value will increase by 2.9 °C, from 14.4 °C to 17.3 °C under three climate scenarios (RCP 26, RCP 45 and RCP 85). We then conduct multiple regression analysis between climate changes compiled from the Climate Research Unit database and vegetation greenness from the GIMMS NDVI3g dataset. There is a general acceleration in the desertification trend under the RCP 85 scenario in middle and northern part of Middle Asia, northwestern China except Xinjiang and the Mongolian Plateau (except the middle part). The RCP 85 scenario shows a more severe desertification trend than does RCP 26. Desertification in dry land Asia, particularly in the regions highlighted in this study, calls for further investigation into climate change impacts and adaptations.

Bibliography

Satellite data to measure and monitor land degradation

 

UNCCD CST S-4 Side Event Discusses Use of Satellite Data

A side event organized by the Scientific and Technical Advisory Panel of the Global Environment Facility (GEF/STAP) during the fourth special session of the Committee on Science and Technology (CST S-4) of the UN Convention to Combat Desertification (UNCCD) considered ‘The use of satellite data to measure and monitor land degradation over time at multiple scales.’

Participants at the side event were informed of a new GEF project that will seek to provide guidance, methods and tools to monitor and assess land degradation using remote sensing, and they were encouraged to address the needs of users of remote sensing. Speakers noted challenges in harmonizing and interpreting data from different remote sensing products and how the data could be used to develop policy advice, among other topics.

Read the full text: IISD

Desertification and remote sensing

Photo credit: Google

Jilin City (red) in Jilin province (orange)

Study on Remote Sensing of the Degree of Land Desertification in the West of Ji Lin Province

Posted by Basic Science

See: Basic Science Paper

Land desertification that is an important content in the global change research is the one of most serious ecological problems all over the world. It is not only the threat to the survival of the human environment, but also the important factor to restrict the development of the global economy and affect Social stability. If we don’t take fundamental measures, the process of land desertificationwill not stop automatically, but will intensify to develop.We should start from the inherent attributes of the land desertification for learning to its type and extent. The comprehensive application and research of the Remote Sensing Data and Multiple Geo-information can be show attributes and inherent feature of the objects with a variety of ways and different scales. It’s very advantaged for Remote Sensing information to distinguish the object with their differentia of electromagnetic radiation features, but also it have some limitations. Practice has proved that we must relate Remote Sensing data to Multiple Geo-information that contain Geological data and Geochemical data with addition of the difficulty of geological work, and then we truly understand the nature of the objects and relation each other, also to be satisfied with the application of geological effects.

Heavenly Lake, Changbai Mountains in Jilin Province, China. September, 2003 - http://www.uoguelph.ca/~thsiang/visit/tianchi.jpg
Heavenly Lake, Changbai Mountains in Jilin Province, China. September, 2003 – http://www.uoguelph.ca/~thsiang/visit/tianchi.jpg

This article use the west of Ji Lin Province as study area. It make an estimate to desert land in working area as a whole with Remote Sensing Data (2007 ETM) and Geochemical Data (the results of 1:200000 soil geochemical survey in the west of Ji Lin Province).

Major work is as follows:(1) The classification of desert land.We obtain the information of land desertification in the west of Ji Lin Province by the ways of unsupervised classification and supervised classification, and then compare the results of both so that to separate accurately desert land and undesert land. The result indicates that the method of supervised classification is more accurate.(2) Enhance the information of Clay minerals among Remote Sensing Data. The special configuration of the objects fix on their Physical and Chemical character. We can analyse the inherent attributes after studying their spectrum character. Clay minerals which play an important role in extent of Land Desertification is an important part of soil composing. But vegetation and clay minerals have similar characteristic absorbable spectrum because the west of Ji Lin Province locate moist area. We should make use of Band Ratio and Principal Components to avoid vegetation information that would disturb clay minerals information in Remote Sensing Data as a whole hog so that we can analyse accurately the negative correlation between the extent of desertification and content of clay minerals.(3) Research methods of combination and classification of elements about Geochemical Data.We make use of Genealogy Cluster Analysis to combine and classify various elements among Geochemical data. The establishment of equal deep image of minerals content such as Quartz, Clay Minerals, Carbonate and so on could reflect directly the distribution of various minerals about Geochemical Data. It validates the seasonable of classification through contrasting Remote Sensing Image.(4) Complete the classification evaluation of desert land and find the variety rule between Remote Sensing spectrum and mineral content.The substance composing of different degrees of desert land is different. There is a close relationship between mineral content and spectral characteristics. We can found the association of both after establishing relevant model of Remote Sensing Spectral Profile image and equal deep image of minerals content.This article first study on Remote Sensing of the degree of land desertification to study area by the ways of combining geochemical data. It’s also the innovation point. It not only has done the evaluation of distribution and degree of desertification land, but also has realized semi-quantitative analysis of clay minerals to desertification land of different degrees. Reached the conclusions as follows:(1) It’s a negative correlation between the extent of desertification and content of clay minerals. The more serious desertification, the less content of clay minerals. There is may be a potential desertification to crops and grassland that contain less clay minerals.(2) The contain of Quartz is more than 42.115077% and the contain of clay minerals is less than 6.410923% to desert land which degree is serious; The contain of Quartz is 39.673305~42.115077% and the contain of clay minerals is 6.410923~8.557119% to desert land which degree is moderate; The contain of Quartz is 34.789760~39.673305% and the contain of clay minerals is 8.557119~10.414073% to desert land which degree is mild.

How to keep track of the world’s carbon stocks ?

Photo credit: Pixabay

Victoria Falls rain forest (Zimbabwe)

Mapping forests’ carbon with lasers

VIDEO : http://youtu.be/ATBFfJFDOwU

EXCERPT

Measuring carbon emissions is crucial for planning a response to climate change. But scientists have so far struggled to keep track of the world’s carbon stocks and how they vary.

At the Carnegie Airborne Observatory, he has developed a lidar (light detection and ranging) system based on laser technology that is flown over the tropics to measure how much carbon is trapped in the forests, and where deforestation, illegal logging and mining activities are releasing it.

This video shows what pilots can see from above the forest, and how the system turns aerial imagery into colourful, animated carbon maps.

– See more at: http://www.scidev.net/global/forestry/multimedia/mapping-forests-carbon-lasers.html#sthash.V8e8qZff.dpuf

 

Water scarcity impacts and drought early warning system in Iran

Photo credit: GFCS

Zayandehrood-river, Iran

Implementation of Drought Early-warning System over IRAN (DESIR)

Iran’s precipitation is approximately one third of global average and distribution of the monthly rainfall has been changed in recent years. Water scarcity has many environmental and socio-economical impacts over Iran. Unlike to the floods that have limited coverage areas, water scarcity impacts cover vast regions. By increasing global mean temperature, drought and population, water and its consumption has become important. This may even become more significant in those countries where the volume of rainfall is limited. Occurrence of drought is one of the main reasons of the water crisis. Implementation of a drought early warning system is the most important priority for I. R. of Iran Meteorological Organization (IRIMO).

Read the full article: Global Framework Climate Services

Earth Observation (EO) data for desertification indicators

Photo credit: CERENA

Development of EO indicators for the Dynamic of Desertification in Southern Africa

This is a one year project involving a partnership with ISEGI, Mondelane U. and U. DELFT.

The aim of this project is the analysis of new dynamics of desertification in the region Southern Africa ( Mozambique , Zimbabwe and Northern part of South Africa ) by using and implementing the main achievements of the DW –E, a methodology developed by CERENA and a standard processing chain over Earth Observation data, in order to produce desertification indicators, allowing the monitoring of areas subject or in risk of desertification.

A second objective is to propose new methods for integration of EO data with different spatial and spectral resolutions, namely the new ESA Proba- V mission with products ranging in spatial resolution from 100m to 1km , and new field data in particular the survey and risk map of natural disasters made between 2010 and 2011 for Mozambique.

Read the full article: CERENA

The partial greening of the Sahel and climate change

Photo credit: Marc Pille

Reforestation project with Jatropha curcas in Mali 2009-10

A climate model-based review of drought in the Sahel: Desertification, the re-greening and climate change

by Alessandra Giannini Michela Biasutti

and Michel M. Verstraete

Abstract

We review the evidence that connects drought and desertification in the Sahel with climate change past, present and future.  Advances in climate modeling point to the oceans, not land, as the cause of the recent persistence of drought in the Sahel.

The current generation of global climate models reproduces the spatial extent, continental in scale, and the timing and duration of the shift to dry conditions that occurred in the late 1960’s given knowledge of observed surface oceanic conditions only.

The pattern statistically and dynamically associated with drought is one of warming of the tropical oceans, especially the Pacific and Indian Oceans, superimposed on an enhanced warming of the southern compared to the northern hemisphere most evident in the Atlantic.

These models, which include a prognostic description of land surface and/or vegetation, albeit crude, indicate that positive feedbacks between precipitation and land surface/cover may act to amplify the ocean-forced component of continental climate.

Despite the advances made in understanding the recent past, uncertainty dominates as we move forward in time, to the present, partial greening of the Sahel, and to the future of climate change projections.

Read the full article: Science Direct