Desertification Assessment and Monitoring Based on Remote Sensing (ID. 10367)

Gao Zhihai, Gabriel del Barrio, Li Xiaosong, Maria E. Sanjuán, Wang Bengyu, Juan Puigdefabregas, Bai Lina

Institute of Forest Resource Information Techniques（IFRIT）, CAF, China Arid Zone Research Station (EEZA), CSIC, Spain

Institute of Remote Sensing & Digital Earth, CAS, China

Project's partners 1

Project's objectives 2

Test sites and data acquisition 3

Field Campaigns 4

Outline

Research progress 5

Project's partners

Principal Investigators Prof. Gao Zhihai Institute of Forest Resource Information Techniques

(IFRIT), Chinese Academy of Forestry (CAF), China

Prof. Gabriel del Barrio Arid Zone Research Station (EEZA),

Scientific Research Council (CSIC), Spain

Dr. Li Xiaosong

Institute of Remote Sensing and Digital Earth,CAS

Dr. Wang Bengyu

IFRIT, CAF, China

Ms. Maria E. Sanjuán

EEZA, CSIC, Spain

Prof. Juan Puigdefabregas

EEZA, CSIC, Spain

Associ. Prof. Bai Lina

IFRIT, CAF, China

Project objectives

Techniques research for desertification assessment and monitoring in China using remote sensing data in combination with climate and environmental-related data.

Concrete topics:

1. Improved techniques for sparse vegetation parameters estimation 2. Mapping of sandy land types with new classification method 3. Assessment and monitoring of land degradation using NPP and RUE. 4. Assessment of human contribution to desertification under climate change.

Test site and data acquisition

Test sites

1600

700

DEM of Xilin Gol League (Resolution:30m) Land use/cover map of Xilin Gol League (2010)

Enclosed Grassland Sparse Grassland Semi-fixed Sandy Land

DEM

Test sites and data acquisition

Remote sensing data acquisition

DATA SCENE TIME COVERAGE

10-Day composite NDVI From Envisat-Meris

358 2002/04-2012/03 China

GF-1 14 2014 Xilin Gol League , Inner Mongolia

MODIS NDVI 5520 2001-2012 China

NPP estimation using Meris NDVI data NPP: Improved CASA model .

( , t) ( , t) ( , t)NPP x APAR x x= ×ε

( , t) ( , t) ( , t)APAR x SOL x FPAR x= × × 0. 5

1 max( , t) ( , t) ( , t) ( , t)x T x T x W x= ε ε2 εε × × ×ε

Annual NPP distribution over China

n=50

Meteorological data:

China’s high-resolution meteorological dataset, monthly rainfall, temperature

and solar radiation data. Resolution ：0.1°×0.1°

Soil data:

Chinese Academy of Sciences 12 Soil Categories 1:1,000,000

Precipitation(2003) Temperature(2003)

Other data acquisition

Soil Map of Xilin Gol League, Inner Mongolia

Terrestrial ecosystem data: Global Change Research Data Publisher & Repository (http://www.geodoi.ac.cn)

Terrestrial ecosystem spatial data sets of China. DOI:10.3974/geodb.2015.01.01.V1

Resolution ：100m×100m Time Cover: 2000, 2005, 2010

2000 2005 2010

Terrestrial ecosystem spatial distrubution of China(2000,2005,2010)

Field Campaigns • Duration: August 7-26, 2012；August 20-30, 2013； June 27-August 12, 2014 • Sites: Xilingol League, Inner Mongolia • Contents: Types of landform，Vegetation patterns，Vegetation fraction，Biomass， Soil organic matter(SOM)，In-situ spectra measurement, etc. • Number of sampling: 285（61, 54,170） • Size of samples: 30m×30m

Routes and sampling postions(2012-2014)

Academic Exchange & Joint Field Investigations in China

Field Investigation in China

Joint

Research

progress

Sandy lands are sheltered by vegetation, leading to mis-identification of sandy lands.

More types and complex surface cover of sandy lands affect the discrimination of sandy lands from non-sandy lands.

Spectral Mixture Analysis (SMA) was applied to solve the above-mentioned problems for mapping sandy lands.

Background

Flowchart of Sandy Lands Mapping by SMA

Sand abundance

Endmumber selection

Remote sensing image

Data preprocessing

Field data

PPI GVS

Spectral Mixture Analysis

Other endmumber abundance

Degraded land detectionMap of Sandy lands

Endmember Selection

Other endmember abundance

Zhenglan Qi in Xilin Gol

League, Inner Monggolia

Data type: GF-1

Spatial resolution : 16m

Imaging time: 2014.04.27

Test site and data

Pure Pixel Index (PPI)

Geometric vertex of scatterplot (GVS)

18

PPI is better than GVS for extracting the spectral information of different endmembers.

Endmember selection method

GVS

2D-splattering Endmember spectral plot MNF

PPI

PPI iteration 3D-splattering Endmumber spectral plot

if the number was greater than 6, it was prone to generate noise and error;

if the number was less than 5, the mixed pixel could not be decomposed effectively;

5 or 6 of endmember was more suitable in this study , the decomposition result was more accurate for sand lands mapping.

19

N=3 N=6

Number of Endmember

研究背景

20

Spectral plots of Endmembers

Endmember type

Main typical land cover types in GF-1 image during the non-growing season

Sandy land Shrubs Grassland Bare soil

Saline land Water Urban area Road

研究背景

Abundance distribution of different endmembers based on PPI

Sandy lands Bare soil Water

Grassland Shrubs Saline land

Linear Spectral Unmixing (LSU) ij

p

jijNi emD +α=

1=∑ 1=α+...+α+α 21 j

Spectral Mixture Analysis

Sandy land detection based on mixed pixel decomposition

22

If sand abundance accounted for more than 50% in the remaining endmember abundance except for vegetation, or less than 50% but it was the maximum , the pixels would be determined as sandy lands.

The total accuracy was 86.42%, the sandy lands with high vegetation coverage could be also extracted accurately and effectively.

Sandy Lands Identification

Projects Total number Sandy land Unsandy land

Sample number 162 81 81

Correct number 140 68 72

Accuracy 86.42% 83.95% 88.89%

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