Iden%fica%on of agricultural crops in early stages using remote sensing images

Transcription

1 Iden%fica%on of agricultural crops in early stages using remote sensing images S.Valero, P. Ceccato, Walter E. Baethgen and J. Chanussot (1) CESBIO- CNES, Toulouse, France (2) InternaConal Research InsCtute for Climate and Society, Columbia University, USA (3) Gipsa- Lab Grenoble, France

2 Outline IntroducCon

3 Introduc%on The use of remote sensing images in accurate crop monitoring applicacons has steadily increased in the last decade Crop mapping Forest Global Monitoring for Environment Natural resources Spatial Planning Land Carbon Agri-Environment Food Security Water

4 Introduc%on Most common techniques are based on the study of the spectral characterisccs of crops over Cme 14/03/ /06/ /06/2006 Mul%- temporal %mes series : Repetitive observations describing a single phenological cycle of the vegetation

5 Introduc%on HSI vs temporal NDVI analysis Thème 6. Princip ba Introduc%on Maximum NDVI NDVI values The classical approach Images de reflect consists in studying Vegeta%on phenological NDVI profiles growth Vegeta%on 29 Juin 2006 describing the crop senescence Thème 6. Principes des modèles simples d efficience 26 July 2006 vegetation cycle Growing season 09 Sept. 200 basés sur la télédétection Days NDVI (Normalized Difference Vegetation Index) Images de reflectance Cartes LAI images 29 Juin July Sept Inversion d un modèle de transfert radiatif For each pixel 29 Juin July Sept Occ SOL Classifier - RPG

6 Introduc%on HSI vs temporal NDVI analysis Introduc%on Different crops have different NDVI Cmes series which make possible their discriminacon 1 NDVI 0.8 use wheat rape corn sunflower High- temporal- resolucon satellite observacon BeZer NDVI profile characterizacon

7 Introduc%on The low spacal resolucon is a limitacon in order to characterise the spectral properces of a field. The informacon contained in a single pixel is a mixture which leads to a difficult spectral analysis. The spacal resolucon of the sensor should be at or below the size of the fields of interest SpaCal resolucon 250 m SpaCal resolucon 30 m

8 Introduc%on Irregular temporal sampling (gaps) : clouds, gaps, noise,

9 Introduc%on Irregular temporal sampling (gaps) : clouds, gaps, noise, The high intra- variability of the NDVI profiles belonging to an unique crop type (e.g., local climate, soil properces, water content, etc.) NDVI values Each color corresponds to the NDVI profiles from an unique soybean fields Nov 26Nov 11Dec 28Dec 13Jan 29Jan 14Fev Days

10 Introduc%on Irregular temporal sampling (gaps) : clouds, gaps, noise, The high intra- variability of the NDVI profiles belonging to an unique crop type (e.g., local climate, soil properces, water content, etc.) NDVI values Corn Soybean NDVI values Corn Soybean Days Days

11 Introduc%on Irregular temporal sampling (gaps) : clouds, gaps, noise, The high intra- variability of the NDVI profiles belonging to an unique crop type (e.g., local climate, soil properces, water content, etc.) NDVI values Corn Soybean NDVI values Corn Soybean Days Days

12 Introduc%on Irregular temporal sampling (gaps) : clouds, gaps, noise, The high intra- variability of the NDVI profiles belonging to an unique crop type (e.g., local climate, soil properces, water content, etc.) The crop identification relies heavily on the temporal monitoring of crop evolution during the all growing season

13 Goal: Early crop iden%fica%on Early crop mapping can be important for a high number of reasons: Early crop identification Crop monitoring / forecast Farmers economic planning Agronomic yield management Yield price Governments food shortages Food security: Famine Early Warning Systems

14 Goal: Early crop iden%fica%on Early corn and soybean crop detection Study 1 : Comparison of hyperspectral / mulc- temporal data in Uruguay Planted period Growing season November January Study 2: ExtracCon the phenology parameters in the growing season NDVI values Growing days Vegeta%on growth Vegeta%on senescence Growing season Days

15 Outline IntroducCon

16 Goal Hyperion hyperspectral data Landsat NDVI Cmes series Goal: Early crop iden%fica%on Early corn and soybean crop detection Study 1 : Comparison of hyperspectral / mulc- temporal data in Uruguay Planted period Growing season November January Study 2: ExtracCon the phenology parameters in the growing season NDVI values Growing days Vegeta%on growth Vegeta%on senescence Growing season Days

17 Introduc%on HSI vs temporal NDVI analysis Goal Hyperion hyperspectral data Landsat NDVI Cmes series Study 1: Uruguay data set Study 1 : Detection of corn and soybean crops Planted period Growing season November January Note: the general season started in November Is it possible to discriminate between the different crop types at January? Hyperion hyperspectral image A single image from 06/01/ spectral bands SpaCal resolucon 30 m

18 Introduc%on HSI vs temporal NDVI analysis Goal Hyperion hyperspectral data Landsat NDVI Cmes series Study 1: Uruguay data set For idencfying crops, two important parameters are the size an shape of the crop Soybeans have spread out leaf clumps Corn has tall stalks with long, narrow leaves and thin, tassletopped stems

19 Goal Hyperion hyperspectral data Landsat NDVI Cmes series Hyperspectral data The discriminacon between most of the crops have been possible Leaf Pigments Cell Structure Water Content 7000 Radiance level Corn Soybean Wavelengths Visible NIR SWIR

20 Goal Hyperion hyperspectral data Landsat NDVI Cmes series Hyperspectral data Soybean crops in low maturity stages are mixed with corn crops in high maturity stages Leaf Pigments Cell Structure Water Content 7000 Radiance level Corn Soybean Wavelengths Visible NIR SWIR

21 Introduc%on HSI vs temporal NDVI analysis Goal Hyperion hyperspectral data Landsat NDVI Cmes series Study 1: Uruguay data set Study 1 : Detection of corn and soybean crops Planted period Growing season November January Note: the general season started in November Is it possible to discriminate between the different crop types at January? Hyperion hyperspectral image A single image from 06/01/ spectral bands SpaCal resolucon 30 m Landsat 7 ETM 7 NDVI images 11/ /2012 SpaCal resolucon 30 m

22 Goal Hyperion hyperspectral data Landsat NDVI %mes series Landsat %mes series It exists important spectral differences among the soybean NDVI profiles Different plancng Cmes? Lack of nitrogen? Soil properces? Water content? Corn Soybean Hyperspectral acquisi%on NDVI values Nov 26Nov 11Dec 28Dec 13Jan 29Jan 14Fev Days

23 Outline IntroducCon

24 Goal Phenology parameters extraccon Class separability Goal: Early crop iden%fica%on Early corn and soybean crop detection Study 1 : Comparison of hyperspectral / mulc- temporal data in Uruguay Planted period Growing season November January Study 2: ExtracCon the phenology parameters in the growing season NDVI values Growing days Vegeta%on growth Vegeta%on senescence Growing season Days

25 Goal Phenology parameters extraccon Class separability Study 2: Phenology parameters Study 2: Extrac%on the phenology parameters in the growing season Can the phenology parameters from NDVI Cmes series in growing days be discriminatory? Images NDVI (Normalized de reflecta Difference Vegetation Index) images 29 Juin July Sept Growing days SPOT data set image 14 dates are available from Jan to Sep 2011 SpaCal resolucon 20 m France NDVI values Vegeta%on growth Growing season Days Vegeta%on senescence

26 Goal Phenology parameters extrac%on Class separability NDVI es%ma%on NDVI(t) =wndvi +(mndvi h 1 wndvi) 1 + exp( ms(t S)) exp(ma(t A)) i 1 (1) NDVI values NDVI feb 23 feb 20 abr 30 may 30 jun 07 jul time Days 04 ago 11 ago 01 sep 08 sep 15 sep 13 oct 20 oct 27 oct Levenberg- Marquardt IteraCve non linear regression method using wndvi =0.2

27 Introduc%on HSI vs temporal NDVI analysis Goal Phenology parameters extrac%on Class separability Parameter extrac%on corn fields Days NDVI NDVI profile escmacon NDVI soybean fields Days

28 Introduc%on HSI vs temporal NDVI analysis Goal Phenology parameters extrac%on Class separability Parameter extrac%on NDVI NDVI profile escmacon 500 corn fields Days 1 NDVI 0.8 Parameter extraccon 90 soybean fields Probability distribu%on of the extracted phenology parameters Days vegetacon growth, maximum NDVI LEFT SLOPE Vegeta%on growth slope MAXIMUM NDVI Maximum NDVI Both types of crops have similar characterisccs in the early stages

29 Goal Phenology parameters extraccon Class separability Class separability MAHALANOBIS DISTANCE The class separability is invescgated from the starcng of the season using the Mahalanobis distance d M (t) =( x s (t) x c (t)) 0 1 ( x s (t) x c (t)) DAYS = n s s + n c c n s + n c

30 Goal Phenology parameters extraccon Class separability Class separability MAHALANOBIS DISTANCE The class separability is invescgated from the starcng of the season using the Mahalanobis distance d M (t) =( x s (t) x c (t)) 0 1 ( x s (t) x c (t)) DAYS = n s s + n c c n s + n c

31 NDI analysis Outline IntroducCon

32 NDI analysis Early discrimination of crops is a challenging task. The strong influence of the stage of growth (i.e., the degree of crop maturity) which introduces some important limitations. The maximum NDVI has is found as the most discriminative parameter The separability between corn and soybean crops have been studied during the first 100 days of the phenological cycle Future works will be conducted by incorporating prior information during the classifcation process

33 NDI analysis Ques%on Time Thank you for your auen%on Crop mapping Forest Global Monitoring for Environment Natural resources Spatial Planning Land Carbon Agri-Environment Food Security Water