National Directorate of Forestry
Ministry of Lands, Environment and Rural Development
Mozambique

Development of forest monitoring methods
and forest degradation issues in
MozambiqueTokyo, FFPRI Seminar
February 5-6th , 2019

Joaquim A. Macuácua
GIS and RS Expert
Head of division of mapping and Data management

Content
1.
2.
3.
4.
5.

2

General Information
Objective and output
Methodology
Development of reference forest cover maps and deforestation map;
Measurements of forest degradationn based on tree cover loss in minimumarea;
6. Application of IMPACT ToolBox and GUIDOtool box software forforestndegradation mapping;
7. Analysis of results from Collect Earth Tool;
8. Identification of deforestation areas by analises SAR images for the JJ-Fast
9. Challenges;
10. Conclusions;3

General information

 Area ~ 801 590 Sq km;

 Population ~ 27.216.000 (2017 census projection);
 Economy – Agro-based (cashew nuts, cotton); Resources: Water, Wood Products, Shrimps, Natural Gas, Coal,
Hydro-energy; Tropical climate with two seasons: wet season from October to March, and dry season from April to September; Institution responsible for Forest: National Directorate ofForestry under Ministry Land, Environment and Rural
Development.
 Deforestation rate: 0.79%/year with 34 Million ha of forest ?
(NFI, DINAF 2018);
 Supported by JICA and technical assistance of JOFCA &KOKUSAI KOGYO CO, LTD within 5 years project; Supported by JAXA in K&C#3 –K&C#4 initiatives.

2013 based forest map (DINAF, 2018)

Objectives
To develop potential improved methodologies for satellite
monitoring of tropical dry forest landscape, focusing on
deforestation and forest degradation assessment fornational forest monitoring system for REDD+ in the country.

The assessment aims the following output:

4Enhance forest national monitoring;Up-to-date forest monitoring products;
Building a relationship for forest monitoring;
Contribution to existing country projects and programs;

Specific Objectives?
- Identify the land affected by deforestation (xha)- Identify the land area affected by forest degradation? (x ha)- Measure the intensity of deforestation and forest degradation? ( x %, C/yr)- Define the nature of deforestation and forest degradation? (logging, overgrazing, small
scale forest clearing for faming for subsistence purposes which include livelihood cash
income, tree harvesting for firewood and charcoal, constructions, fire…)
Proposed operational definition is based on forest definition

Forest definition:

5

Minimum tree height:3 m

Minimum tree cover: >30 %

Minimum area

:1 ha

Forest degradation is a direct human-induced longterm loss (persisting for X
years or more) of at least Y% of forest carbon stocks(and forest values) since
time T and not qualifying as deforestation or anelected activity under Article
3.4 of the Kyoto Protocol. IPCC (2003a)

Forest degradation : Change in Forest land remaining forest land
 Mask out Non Forest land and measure change in remaining forest land
 Decrease of tree cover but not < 30% in a minimum area of 1 ha

Suitable for small scale forest clearing type of forest degradation

Methodology
Boundary mapping and pixel based detection of deforested and forest depredated areas;
Ground Truth survey in unsurvey area of Plantations and natural forest to support JICA
project and national forest monitoring;Obtain the samples data that are insufficient for the threshold setting;Comparison of different values (-3, -4, -5 dB) between the images before and after 1
year for deforestation including Non-forest area (Thicket) in order to identify the threshold
between forest/non-forest;

Measuring forest degradation based on tree cover loss in a minimum area;

6Ground Truth survey to support LTS International alongside the University of Edinburghwith European Space and WB for utilising Radar and Optical (sentinel 1 and 2 data with
developed toll for cloud free) of dense time series for continuous change monitoring andproxies of forest changes, and degradation using ALOS PALSAR mosaic in tropical dry
forest;Ground Truth survey to support JICA- JAXA, for data calibration of JJ-FAST system(analysed by SAR imagery) with technical support of Tokyo Denky University.

Activities overview of
7 RS of 5 years JICA
project- Detection of deforestation
areas at national level- Evaluation how to make theuse of the methodology- Ground survey for validationof the deforestation andlinear value

2016

2017

- Evaluation of thelinear value- Visual and - Analysis ofprecision
Automatic
detection of - Water body and- Understanding ofwater strim
deforestation
IR characteristics
Masking

2015

2014

- Detection test of2013 deforestation- Theory study- Radar images acquisition

Basic traing on IR

Training on job inMoz. and Jp. on
IRAplaied Practicaltraining on IR

Traing in Japan in
aplaied areas
analysis

4. Identification of deforestation areas using radar image analysis and evaluation of wayforwad with the information

Principles of dectation of Radar images
8

Deforestation sites are detected by comparing radar images (ALOS-2) at
two time series. The deforestation sites are those that indicate decreaseof back-scattering (return) of radar waves. Therefore, higher return(forest) sites with light color and lower return (non-forest) dark color.

Forest• Spatially explicit
biomass estimates (noland cover classes
needed)• Can detect smallchanges over large
areas (low bias)

No Forest• No cloudcontamination,
day/night acquisition

4. Identification of deforested areas by radar image analysis and evaluation of the ways of using this information

Determination of linear value (threshold)
9
Sampling in Field (Ground Truth)
The threshold values of temporal differencesof the deforested areas were collected in 76
samples from Cabo Delgado, Niassa,
Zambezia, Manica, Inhambane and Gaza.

Study of the area with images
supportDeforested area

Interview with local
peopleYellow: GPS track White: GPS
point

NS12

9
-5
-4.5
-4
-3.5
-3
North

East

West

South

08/July/2015 25m resolution-

06/July/2016

25m resolution

Deforestation for Maize. Past forest type is
Mixed forest.
Deforestation area is 9.4 ha.
Big trees are deciduous (h=15m),Lower
trees are evergreen (Massuco, h= around7m) .

4. Identification of deforested areas by radar image analysis and evaluation of the ways of using this information

Identification of deforested areas by Radar Image Analysis
10

Determination of the linear value to detect the deforested area
-10

-15

-20

dB

Dense forest-12dB

Big difference-20dB

Forest open
Small difference※ Only deforestation data greater than 1.0 ha

-16dB

-20dB

Beginning of theperiodEnd of period11

4. Identification of deforested areas by radar image analysis and evaluation of the ways of using this information

Result of areas of forest loss(2008 - 2010)11

Province

Cabo
DelgadoNiassaNo. of sites
indicating
forest loss

Deforested
area（ha/year）

12.60019.26710.09821.158

Nampula10.57114.250

Zambézia

4.425

21.037Tete

17.34524.765

Manica22.66737.354

Sofala11.41526.413

Inhambane

3.947

4.488

Gaza

1.748

2.852

Maputo

1.042

3.156

Total

95.858174.740

4. Identification of deforested areas by radar image analysis and evaluation of the ways of using this information

How to leverage information
12

Size of deforested area by forest type (CD):We identified the area deforested by forest type, from C. Delgado, overlapping theimage with the 2008 forest coverage map.
Deforested
area (ha)(Semi-)
dense
ever
green

Semiopen
ever
green

Mangr
ove

(Semi-)
deciduous
dense(Semi-)
deciduous
Open

Total
(ha)

Deforestation in 2010-2013:
35,453 ha

1～2

279

122

1.915

3.137

5.452

2～3

202

63

1.070

1.794

3.129

3～4

119

45

657

1.171

1.992

4～5

124

48

501

871

1.548

5～6

97

22

388

835

1.341

6～7

91

19

368

486

964

7～8

90

8

304

240

641

8～9

43

288

400

731

9 ～ 10

47

180

238

10 ～

696

73

2.204

3.348

465 Map of forest cover and6.321 land use 2008

Total

1.787

399

4

7.875

12.51922.585% of forest
cover

0,81%

0,26%

0,01%

0,49%

0,34%

0,39% ※2 years during 2008 20104

4. Identification of deforested areas by radar image analysis and evaluation of the ways of using this information

How to leverage information
13

Size of deforested area by forest type (GZ):
We identified the deforested area by forest type, from Gaza, overlapping the image
with the forest cover map of 2008.
Deforestation in 2010-2013:
Deforested area(ha)(Semi-)
dense
green

(Semi-)
dense
ever
green

Mecrusse(Semi-)
dense
deciduous(Semi-)
open
deciduous

MopaneTotal(ha)5,342 ha

1～2

31

52

16

133

370

314

918

2～3

45

36

13

56

149

162

460

3～4

17

45

10

51

123

143

389

4～5

14

23

18

50

93

198

5～6

22

16

6

33

65

142

6～7

19

7

6

13

58

102

7～8

15

7

8

30

52

112

8～9

8

25

9

9

42

93

38

57

125

203

869

413

1.017

1.855

3.770

0,17%

※ 2 years during 2008 0,09% 2010

9 ～ 10
10 ～

51

13

Total

222

223

% of
forest
cover

0,58%

0,21%

39
0,01%

0,09%

0,05%

95 Map of forest cover andland use 2008
1.261

Measuring forest degradation based on tree cover loss
in a minimum area(approach from JRC-ISPRA)
Forest area (T1)

If grid size = Minimum area for forest definition
Forest degradation could be measured by the % ofchange (tree cover to non-tree cover) inside a cellIf the % of non-tree cover is > 70 % = deforestation
If the % of non-tree cover is < 70 % and there is achange from tree cover to non-tree cover = forest
degradation

14
T1
T2
NTC=10% NTC=20% NTC=40%
Forest degradation=
Decrease of TC [T1- T2] 10%, [T2-T3] 20%

T3

Application of IMPACT toolbox and GUIDOtoolbox
developed by JRC-ISPRA
Definition of forest degradation based on forest definition
Forest = Tree cover > 30 % Minimum area of 1ha
Step 1:Landsat map (2 dates T1 – T2): Tree cover – Non Tree CoverGrid of 3*3 pixels (0.81 ha)
Counting of pixels in each cell:

TC - TC
NTC - NTC

XTC-TC =number of pixels that stayed TC
XNTC-NTC = number of pixels that stayed NTC
XTC-NTC = number of pixels that changed from TC to NTC

TC - NTC

Step 2:
Classification of each cell based on XTC-TC XNTC-NTC XTC-NTC
Intact Forest (XTC-TC = 9)
Non Forest (XTC-TC < 3 AND XTC-NTC = 0)
Deforestation (XTC-TC < 3 AND XNTC-NTC < 7)

Degraded forest (XTC-TC > 2 AND XTC-NTC = 0 )light (XNTC-NTC = 1 or 2) moderate (3 or 4) severe (5 or 6)
Forest degradation (XTC-TC > 2 )15light (XTC-NTC = 1 or 2) moderate (3 or 4) severe (5 or 6)

Measurement of area affected by deforestation, forest degradation (light,
moderate or severe) (+ non forest and degraded forest)
Step 3:
Track over time: add a second time period T2-T3 over the same(record of state for each cell)grid

For this method we have not done theground truth to checked the accuracy
in our tropical dry forest (desk results)

Basics of
IMPACT

16

Recent Ground truth for JJ-Fast Calibration data in dry forest with Japan TDU
and DINAF

Collect Earth method
The data collection using this method was based in sampling visual interpretation of activity data using
high, medium and low resolution satellite data from the repository of Google Earth, Bing maps, EarthEngine Explorer and Code Editor. Used a grid sampling method of 48 894 samples covering the wholecountry to estimate deforestation (FAO method).
The evaluated period was from 2003 to 2016, executed by FNDS with Support of World Bank.

17

This method shows that the country lose 267 209 ha/year with the tax rate of 0,79% deforestation

Note: No ground truths was done on this method

Challenges?
Long-term effects How to differentiate between long-term decline or persistent decline and temporal variability
due to normal/good management or annual variability? How to differentiate between forest degradation andsustainable management of forest (cfr Forest code of practice? National/at logging company?)
Exclusion of deforestation How to be sure that forest degradation will not be reclassify as deforestation in the
future (precursor to deforestation)? (long-term reduction of carbon stocks but tree cover, height and area are
not under the threshold defined for forest land). How to define when the threshold for forest has been crossed?
Loss of forest carbon stock in forest land remaining forest land. The change should lead to a change in carbon
stocks. Emissions due to forest degradation will depend on carbon stock available for release anddegree/nature of the process (idem for removals);
Has to be operational, has quantifiable and measurable/detectable thresholds within a defined time frame, bepart of the GHG inventory (must be human-induced, describe change in carbon stocks at least), can beapplied consistently in the same biome, must be possible to quantify/verify the change;18

Key challenges to implement and provision technologies and data sources of forest inventories using
unmanned aerial vehicles (UAVs) or drone platforms to acquire both LiDAR and Very High Spatial Resolution(VHSR) imagery with high operational capacity to capture detailed data with less cost of hardware
infrastructure, advanced techniques of large extracted amount of data and store.
Definition of the most precise, accurate method considering the consistency with the reliable results for datacomparison in the county when forest management is being carried out. Full knowledge of the current groundfield is deed to support Remote Sensing ;Radar Signal saturates in dense tropical forests, Limited availability of long-wavelength radar data and Data
cost;

Conclusions
1.

Use of ALOS data provided by wall to wall forest cover map resulted in forest base map (2013) of thecountry to estimation changes of the forest cover is a good methodology to detect a time series ofdeforestation, forest remaining forest and gains.

2.

Deforested areas and forest degradation can be identified by RADAR image analysis and ScanSAR canbe produced for any weather and sunlight independent, Penetrates through canopy, Signal ‘scattered’
from trunks and branches, Interacts with woody biomass,

3.

Measuring forest degradation based on tree cover loss in a minimum area methods require groundtruth survey at large scale and biomass assessment and carbon estimation to confront the challenges
aforementioned in the our dry tropical forest due to the existence of certain species with wide canopy
cover (e.g Brachystegia sp) in order to be avoid bias;

4.

The used of Collect Earth Tool to get reliable results needs experienced technical staff with fullknowledge of the ground to get accurate results with statistical sample approach to have a minimum
acceptable error and the factor of expansion needs to be more elaborated and clear based in ecologicalarea rather than administrative boundaries;

5.

Scanning deforested areas using drone for small assessment and carbon of inaccessible areas is muchefficient and less costly compared to large satellite data of low resolution;

19

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Joaquim Macuácua
joaquimmacuacua@gmail.com