1.2 ព័ត៌មានលម្អិតពីបុគ្គលសំខាន់ៗ និងស្ថាប័នដែលចូលរួមក្នុងការវាយតម្លៃ និងចងក្រងឯកសារនៃបច្ចេកទេស

ឈ្មោះគម្រោងដែលបានចងក្រងឯកសារ/ វាយតម្លៃលើបច្ចេកទេស (បើទាក់ទង)

Integrated Biodiversity Management, South Caucasus (IBiS)

ឈ្មោះអង្គភាពមួយ (ច្រើន) ដែលបានចងក្រងឯកសារ/ វាយតម្លៃបច្ចេកទេស (បើទាក់ទង)

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ)

1.3 លក្ខខណ្ឌទាក់ទងទៅនឹងការប្រើប្រាស់ទិន្នន័យដែលបានចងក្រងតាមរយៈ វ៉ូខេត

អ្នកចងក្រង និង(បុគ្គលសំខាន់ៗ)យល់ព្រមទទួលយកនូវលក្ខខណ្ឌនានាទាក់ទងទៅនឹងការប្រើប្រាស់ទិន្នន័យដែលបានចងក្រងតាមរយៈវ៉ូខេត:

បាទ/ចា៎

1.4 សេចក្តីប្រកាសស្តីពីចីរភាពនៃការពណ៌នាពីបច្ចេកទេស

តើបច្ចេកទេសដែលបានពណ៌នានេះមានបញ្ហាដែលផ្តោតលើការធ្លាក់ចុះគុណភាពដី, បើដូច្នេះវាមិនអាចត្រូវបានប្រកាសថាជាបច្ចេកទេសនៃការគ្រប់គ្រងប្រកបដោយចីរភាពទេ?

ទេ

1.5 ការយោងទៅលើកម្រងបញ្ជីសំណួរ (មួយ ឬច្រើន) នៃវិធីសាស្ត្រផ្សព្វផ្សាយ SLM (ដែលបានចងក្រងដោយទស្សនៈពិភពលោកស្តីពីវិធីសាស្ត្រ និងបច្ចេកទេសងអភិរក្ស WOCAT)

2.1 ការពណ៌នាដោយសង្ខេបពីបច្ចេកទេស

និយមន័យបច្ចេកទេស:

Land degradation contributes to biodiversity loss and the impoverishment of rural livelihoods in Tusheti. Above all, however, land degradation are triggered by climate change as traditional land use practise might not be adapted to new climate conditions which can cause or speed up degradation processes significantly. On the other hand, degraded land often leads to low biomass volumes and this reduces the ecosystem capability to stabilise local climate conditions. The concept of Land Degradation Neutrality (LDN) and the method of using remote sensing for monitoring land degradation are tools to identify the need for local planning processes. This showcase describes the LDN monitoring concept, national targets and the technology to assess indicators, mechanism and incentives for LDN.

2.2 ការពណ៌នាលម្អិតពីបច្ចេកទេស

ការពណ៌នា:

Purpose
The continuing global degradation of land resources threatens food security and the functioning of ecosystem services by reducing or losing their biological or economic productivity. Unsustainable land-use practices such as deforestation, overgrazing and inappropriate agricultural management systems trigger the loss and degradation of valuable land resources in Georgia. These effects are visible in all countries of the South Caucasus. About 35% of the agricultural land in Georgia is severely degraded, 60% is of low to middle production quality.

Land Degradation Neutrality (LDN)
LDN is a new international concept to combat the ongoing degradation of valuable soil resources. The LDN concept was developed by the UNCCD to encourage countries to take measures to avoid, reduce or reverse land degradation, with the vision of achieving a zero-net loss of productive land. To combat land degradation in Georgia, in 2017, the national LDN Working Group set voluntary national targets to address specific aspects of LDN, and submitted them to the UNCCD Secretariat.

To effectively set up counter measures to combat land degradation it is important to have detailed spatial information on land cover and land cover changes as well as on trends in degradation (like size of areas effected by erosion). Therefore a remote sensing toolset was developed and tested in the pilot are of Tusheti protected landscapes in the High Caucasus in Georgia. This region shows increasing soil erosion problems by uneven distribution of grazing activities and was selected for developing erosion control measures within the Integrated Biodiversity Management in the South Caucasus Program (IBiS) funded by the Deutsche Gesellschaft für internationale Zusammenarbeit (GIZ).

Sensitivity Model
The Integrated Biodiversity Management in the South Caucasus (IBiS) project in cooperation with national experts in Georgia, developed and applied a remote sensing toolset called "Erosion Sensitivity Model". This remote sensing toolset helps to assess the current state and the general erosion risk. The sensitivity model is based on the RUSLE – Revised Universal Soil Loss Equation. The tool allows the calculation of erosion caused by rainfall and surface run-off. The RUSLE equation incorporates a combination of different input factors such as precipitation (R), soil type (K), slope (LS), vegetation cover (C) and protection measures (P). In this way, the estimated average soil loss in tonnes per acre per year (A) can be calculated as follows: A = R * K * LS * C * P.

The rainfall factor (R) results from a quotient from the monthly and annual mean value of precipitation. The data come from the data platform “CHELSA – Climatologies at high resolution for the earth’s land surface areas”. For the soil type factor (K), a soil map of 1:200,000 was taken. Then, depending on the soil type, different contents of sand, silt, loam and clay were used to calculate the K factor. The slope length and steepness factor (LS) is calculated from a digital elevation model (DEM) with a raster resolution of 10x10m. The DEM is derived from the topographic map 1:25,000. The global elevation model derived from SRTM data (Shuttle Radar Topography Mission) has a resolution of 30x30 m and is available worldwide free of charge. The land cover factor (C) describes the vegetation cover that protects the soil from erosion. The vegetation cover slows down the speed of the raindrops and reduces the erosive effect of the rain. It slows down surface water runoff and stabilises the soil through root systems. The main indicators, land cover and productivity, can be assessed by remote sensing. The data from satellites need to be classified and calibrated by field data (ground truthing). The technology for the assessment of these indicators with Sentinel 2 satellite images was developed and applied in 2016 to 2018 in the Tusheti region (Akhmeta municipality) in the framework of the GIZ-IBiS project. Based on spectral information from airborne or satellite images, the density of the vegetation was calculated and mapped. There are well developed vegetation indices and classification systems to derive different land cover types and vegetation densities (mainly described by the Leaf Area Index LAI or biomass indices). The LAI is the area of the leaf surface (in square meters) per square meter ground surface. Since the real surface area of the leaves is hardly measurable, the amount of biomass is a proxy for the LAI. The P-factor is rarely considered in large-scale modelling of soil erosion risk as it is difficult to estimate it with very high accuracy. Therefore, to refine the model, a more detailed DEM (digital elevation model) is required (e.g., from satellite images). Based on the input factors, a soil erosion risk map was calculated for the whole territory of the Tusheti Protected Areas (113,660 ha). Based on the different spectral bands of the Sentinel 2 satellite image, a land cover map was calculated using the Support Vector Machine (SVM) technology and spectral image information.
The results have been integrated in the development of pasture management plans ("pasture passports"). This maps and documents are indicating areas of high erosion risk that need to be excluded from grazing and the maximum number of livestock has been calculated based on the biomass maps and will be integrated into the lease contracts.
The repetition of the remote sensing after some years (e.g. 5 years) will help to evaluate, if the measures in the pasture management have been successful to stop the degradation processes.

2.3 ​រូបភាពនៃបច្ចេកទេស

2.5 ប្រទេស/តំបន់/ទីតាំងកន្លែង ដែលបច្ចេកទេសត្រូវបានអនុវត្ត និងបានគ្រប់ដណ្តប់ដោយការវាយតម្លៃនេះ

2.6 កាលបរិច្ឆេទនៃការអនុវត្ត

បង្ហាញឆ្នាំនៃការចុះអនុវត្ត:

2016

2.7 ការណែនាំពីបច្ចេកទេស

សូមបញ្ជាក់តើបច្ចេកទេសត្រូវបានណែនាំឱ្យអនុវត្តដោយរបៀបណា:

• តាមរយៈគម្រោង / អន្តរាគមន៍ពីខាងក្រៅ

3.1 គោលបំណងចម្បង (១​ ឬច្រើន)​ នៃបច្ចេកទេសនេះ

• ធ្វើឱ្យប្រសើរឡើងនូវផលិតកម្ម
• កាត់បន្ថយ, បង្ការ, ស្តារឡើងវិញនូវការធ្លាក់ចុះគុណភាពដី
• អភិរក្ស/ធ្វើឱ្យប្រសើរឡើងជីវចម្រុះ

• provide information to make a spatial-territorial planning

3.2 ប្រភេទដីប្រើប្រាស់មួយប្រភេទ (ច្រើនប្រភេទ) ដែលបានអនុវត្តបច្ចេកទេស

ដីប្រើប្រាស់ចម្រុះនៅលើដីតែមួយ:

បាទ/ចា៎

បញ្ជាក់ពីប្រភេទដីច្រើនប្រភេទ (ដីដាំដំណាំ/ដីចិញ្ចឹមសត្វ/ដីព្រៃឈើ):

• Agro-pastoralism ( រួមបញ្ចូលទាំងដំណាំ និងចិញ្ចឹមសត្វ)

3.3 បន្ទាប់ពីអនុវត្តបច្ចេកទេស តើដីប្រើប្រាស់មានការប្រែប្រួលដែររឺទេ?

បន្ទាប់ពីអនុវត្តបច្ចេកទេស តើដីប្រើប្រាស់មានការប្រែប្រួលដែររឺទេ?

• ទេ (បន្តទៅសំណួរ 3.4)

3.4 ការផ្គត់ផ្គង់ទឹក

ផ្សេងៗ (ឧ. ក្រោយពីទឹកជំនន់):

• rainfed and mixed rained-irrigation

3.5 ក្រុម SLM ដែលបច្ចេកទេសស្ថិតនៅក្នុង

• កសិរុក្ខកម្ម (pastoralism) និងការគ្រប់គ្រងដីសម្រាប់ចិញ្ចឹមសត្វ
• ធ្វើឱ្យប្រសើរឡើងគម្របដី/ ដំណាំគម្របដី

3.6 វិធានការ SLM ដែលបញ្ចូលនូវបច្ចេកទេស

មតិយោបល់:

On some pilot plots technologies to control erosion and stop land degradation have been tested. This includes fencing, rotational pasture management, mulching and installing check dams to stop gully erosion.

3.7 កំណត់ប្រភេទនៃការធ្លាក់ចុះគុណភាពដីសំខាន់ៗដែលបច្ចេកទេសនេះបានដោះស្រាយ

មតិយោបល់:

The main drivers of land degradation in the pilot area are overgrazing and trampling, off-road driving as well as infrastructure development (especially inappropriate road construction in steep slopes).

3.8 ការពារ កាត់បន្ថយ ឬស្តារឡើងវិញនៃការធ្លាក់ចុះគុណភាពដី

បញ្ជាក់ពីគោលដៅរបស់បច្ចេកទេស ដែលផ្តោតទៅការធ្លាក់ចុះគុណភាពដី:

• ការការពារការធ្លាក់ចុះគុណភាពដី
• ការកាត់បន្ថយការធ្លាក់ចុះគុណភាពដី

មតិយោបល់:

The monitoring tools presented here help to monitor the development of land degradation and to evaluate measures and development trends.

4.1 គំនូសបច្ចេកទេសនៃបច្ចេកទេសនេះ

4.2 ព័ត៌មានទូទៅដែលពាក់ព័ន្ធនឹងការគណនាធាតុចូល និងថ្លៃដើម

កំណត់របៀបនៃការគណនាថ្លៃដើម និងធាតុចូល:

• ក្នុងតំបន់អនុវត្តបច្ចេកទេស

កំណត់រូបិយប័ណ្ណសម្រាប់ថ្លៃដើម:

• ដុល្លារ

4.3 សកម្មភាពបង្កើត

	សកម្មភាព	រយៈពេល​ (រដូវកាល)
1.	National level. Baseline: Field assessment for remote sensing calibration (1x/20 years)	2017
2.	Sentinel satellite image classification (multi temporal data from 2017)	2017
3.	Statistical data from GEOSTAT Agricultural census	2014-2016
4.	Analysis of soil carbon content from existing profiles	2003 - 2006
5.	Conduct ongoing monitoring	5 years intervals
6.	Update sentinel satellite image classification	1x year
7.	Update statistical data from GEOSTAT Agricultural census	4x/year
8.	Resampling of soil carbon content near existing profiles	1x/5 years
9.	Municipal level. Spatial planning: Assessment of current stage of land degradation, anticipated gains and losses	1x/10 years
10.	Revision of spatial planning on Municipal level.	1x / 5 years

4.4 ថ្លៃដើម និងធាតុចូលដែលត្រូវការសម្រាប់ការបង្កើតបច្ចេកទេស

	បញ្ជាក់ពីធាតុចូល	ឯកតា	បរិមាណ	ថ្លៃដើមក្នុងមួយឯកតា	ថ្លៃធាតុចូលសរុប	% នៃថ្លៃដើមដែលចំណាយដោយអ្នកប្រើប្រាស់ដី
កម្លាំងពលកម្ម	Remote Sensing analysis by Sentinel Satellite data	person days	50,0	200,0	10000,0
កម្លាំងពលកម្ម	Collecting field data for satellite image callibration	person days	40,0	200,0	8000,0
កម្លាំងពលកម្ម	Soil sampling (for carbon content)	person days	20,0	200,0	4000,0
កម្លាំងពលកម្ម	Including results in spatial planning	person days	10,0	200,0	2000,0
ថ្លៃដើមសរុបក្នុងការបង្កើតបច្ចេកទេស					24000,0
ថ្លៃដើមសរុបក្នុងការបង្កើតបច្ចេកទេសគិតជាដុល្លារ					24000,0

មតិយោបល់:

This covers the implementation of the baseline. Calibrating the model for erosion risk and land cover classification is an big investment but can be extended to larger areas than 1000 km² with similar resources.

4.5 សកម្មភាពថែទាំ

	សកម្មភាព	ពេលវេលា/ ភាពញឹកញាប់
1.	Repeating the application of the calibrated remote sensing model for monitoring repitition	with 5 years interval
2.	Repetition of soil samples for assessing soil carbon content	with 5 years interval
3.	Analysing the results and integrate them in spatial planning and policy making	with 5 years interval

4.6 កំណត់ថ្លៃដើមសម្រាប់ការថែទាំ/ សកម្មភាពរបស់បច្ចេកទេស (ក្នុងរយៈពេលមួយឆ្នាំ)

	បញ្ជាក់ពីធាតុចូល	ឯកតា	បរិមាណ	ថ្លៃដើមក្នុងមួយឯកតា	ថ្លៃធាតុចូលសរុប	% នៃថ្លៃដើមដែលចំណាយដោយអ្នកប្រើប្រាស់ដី
កម្លាំងពលកម្ម	Applying the calibrated remote sensing model for monitoring repetition	person days	20,0	200,0	4000,0
កម្លាំងពលកម្ម	Repetition of soil samples for assessing soil carbon content	person days	10,0	200,0	2000,0
កម្លាំងពលកម្ម	Analysing results and integrating in spatial planning	person days	10,0	200,0	2000,0
ថ្លៃដើមសរុបសម្រាប់ការថែទាំដំណាំតាមបច្ចេកទេស					8000,0
ថ្លៃដើមសរុបសម្រាប់ការថែទាំដំណាំតាមបច្ចេកទេសគិតជាដុល្លារ					8000,0

មតិយោបល់:

For the repetition of the remote sensing no new calibration of the GIS-model is needed. Only the field samples for soil carbon need to be repeated.

4.7 កត្តាសំខាន់បំផុតដែលមានឥទ្ធិពលដល់ការចំណាយ

ពណ៌នាពីកត្តាប៉ះពាល់ចម្បងៗទៅលើថ្លៃដើម:

Field sample collection;
Remote sensing experts.

5.1 អាកាសធាតុ

5.2 សណ្ឋានដី

បញ្ជាក់ថាតើបច្ចេកទេសនេះត្រូវបានអនុវត្តន៍នៅក្នុង:

• មិនពាក់ព័ន្ធទាំងអស់

មតិយោបល់ និងបញ្ចាក់បន្ថែមអំពីសណ្ឋានដី :

The remote sensing approach was applied for the total landscape of Tusheti, including a great variety of land-forms, altitudes ranging from 1600-4000 m a.s.l.

5.3 ដី

5.4 ទឹកដែលអាចទាញមកប្រើប្រាស់បាន និងគុណភាពទឹក

តើមានបញ្ហាភាពទឹកប្រៃហូរចូលមកដែរឬទេ?

ទេ

តើទឹកជំនន់កំពុងកើតមាននៅតំបន់នេះដែររឺទេ?

ទេ

5.5 ជីវៈចម្រុះ

5.6 លក្ខណៈនៃអ្នកប្រើប្រាស់ដីដែលអនុវត្តបច្ចេកទេស

សូមបញ្ជាក់ពីលក្ខណៈពាក់ព័ន្ធផ្សេងទៀតអំពីអ្នកប្រើប្រាស់ដី:

The technology is applied by the Government.

5.7 ទំហំផ្ទៃដីជាមធ្យមនៃដីប្រើប្រាស់ដោយអ្នកប្រើប្រាស់ដី ក្នុងការអនុវត្ត​បច្ចេកទេស

5.8 ភាពជាម្ចាស់ដី កម្មសិទ្ធប្រើប្រាស់ដី និងកម្មសិទ្ធប្រើប្រាស់ទឹក

ភាពជាម្ចាស់ដី:

• រដ្ឋ

កម្មសិទ្ធិប្រើប្រាស់ដី:

• ជាក្រុម (មានដែនកំណត់)
• កិច្ចសន្យាជួល

កម្មសិទ្ធប្រើប្រាស់ទឹក:

• អាស្រ័យផលសេរី (មិនមានការកំណត់)

តើកម្មសិទ្ធប្រើប្រាស់ដី គឺផ្អែកលើប្រព័ន្ធច្បាប់បែបបុរាណ?

ទេ

5.9 ការប្រើប្រាស់សេវាកម្ម និងហេដ្ឋារចនាសម្ព័ន្ធ

6.1 ផលប៉ះពាល់ក្នុងបរិវេណអនុវត្តបច្ចេកទេសដែលកើតមាន

ផលប៉ះពាល់ទៅលើអេកូឡូស៊ី

ដី

ផលប៉ះពាល់ទៅលើអេកូឡូស៊ីផ្សេងៗ

បញ្ជាក់ពីការប៉ាន់ស្មាននៃផលប៉ះពាល់ក្នុងបរិវេណអនុវត្តបច្ចេកទេស (វាស់វែង):

The monitoring technology was applied for the first time to draw a baseline. Based on the results, activities have been planned and pilot measures have been implemented (exclusion from grazing, reforestation, regulation of grazing intensity). Future replications of the monitoring will show changes and evaluate success of measures. The technologies to control erosion are described separately in the WOCAT database (Community-based Erosion Control [Azerbaijan]; Pasture-weed control by thistle cutting [Georgia]; High-altitude afforestation for erosion control [Armenia]; Slope erosion control using wooden pile walls [Armenia])

6.2 ផលប៉ះពាល់ក្រៅបរិវេណអនុវត្តបច្ចេកទេសដែលកើតមាន

វាយតម្លៃផលប៉ះពាល់ក្រៅបរិវេណអនុវត្តបច្ចេកទេស (វាស់វែង):

The technology is only about the monitoring (see above).

6.3 ភាពប្រឈម និងភាពរួសនៃបច្ចេកទេសទៅនឹងការប្រែប្រួលអាកាសធាតុ និងគ្រោះអាកាសធាតុ/ គ្រោះមហន្តរាយ (ដែលដឹងដោយអ្នកប្រើប្រាស់ដី)

ការប្រែប្រួលអាកាសធាតុ

ការប្រែប្រួលអាកាសធាតុ

	រដូវកាល	កើនឡើង ឬថយចុះ	លក្ខណៈឆ្លើយតបនៃបច្ចេកទេសទៅនឹងការប្រែប្រួលអាកាសធាតុ
បរិមាណទឹកភ្លៀងប្រចាំរដូវកាល	រដូវក្តៅ	ថយចុះ	ល្អណាស់

មតិយោបល់:

Technology is sensitive, it shows the climate change, the impact of the global change locally. The technology itself is not affected by climatic changes.

6.4 ការវិភាគថ្លៃដើម និងអត្ថប្រយោជន៍

តើផលចំណេញ និងថ្លៃដើមត្រូវបានប្រៀបធៀបគ្នាយ៉ាងដូចម្តេច (ទស្សនៈរបស់អ្នកប្រើប្រាស់ដី)?

តើផលចំណេញ និងការថែទាំ/ ជួសជុលត្រូវបានប្រៀបធៀបគ្នាយ៉ាងដូចម្តេច (ទស្សនៈរបស់អ្នកប្រើប្រាស់ដី)?

មតិយោបល់:

The monitoring technology was applied for the first time to draw a baseline. Based on the results, activities have been planned and pilot measures have been implemented (exclusion from grazing, reforestation, regulation of grazing intensity). Future replications of the monitoring will show changes and evaluate success of measures. The technologies to control erosion are described separately in the WOCAT database (Community-based Erosion Control [Azerbaijan]; Pasture-weed control by thistle cutting [Georgia]; High-altitude afforestation for erosion control [Armenia]; Slope erosion control using wooden pile walls [Armenia]).
The costs of the remote sensing approach have not been invested by the land owners but by GIZ and the Ministry. Therefore there are no direct negative impact caused by the investment. The maintenance will be covered by public authorities as well. The positive impact for the land users are the clearly delineated pasture unit giving the exact area of grassland and the accessible amount of fodder biomass. By this, the lease-rate can be found according to the productivity and the number of livestock can be adapted to the carrying capacity of the land within the lease contract.

6.5 ការទទួលយកបច្ចេកទេស

• តែមួយករណី /ពិសោធន៍

បើអាច សូមបញ្ជាក់ពីបរិមាណ (ចំនួនគ្រួសារ និង/ ឬតំបន់គ្របដណ្តប់):

The technology is desigend to be applied by national or regional addministrations and not by land owners themselves.

6.6 ការបន្សុំា

តើថ្មីៗនេះ បច្ចេកទេសនេះត្រូវបានកែតម្រូវ​ដើម្បី​បន្ស៊ាំ​ទៅនឹង​ស្ថាន​ភាព​ប្រែប្រួល​ដែរ​ឬទេ?

ទេ

6.7 ភាពខ្លាំង/ គុណសម្បត្តិ/ ឱកាសនៃបច្ចេកទេស

ភាពខ្លាំង/ គុណសម្បត្តិ/ ឱកាសនៅកន្លែងរបស់អ្នកប្រើប្រាស់ដី
The monitoring technology can help to find erosion and degradation hot spots and based on this spatial information counter measures can be applied to save the productivity of land. As the income from agricultural activities and livestock breeding is of high priority in this pilot region, the protection of the productivity of land is of high importance to the local land users.

ភាពខ្លាំង/ គុណសម្បត្តិ/ ឱកាស​ ទស្សនៈរបស់បុគ្គលសំខាន់ៗ

The presented remote sensing technologies are a cost efficient and objective way to monitor land degradation and land use changes on large areas on long time periods. Based on this spatial data, land use regulations can be integrated in spatial planning and other legal and practical frameworks (e.g. pasture lease contracts) to counter act the degradation processes. The success of the measures and the development of degradation and rehabilitation can be monitored by the same toolset.

6.8 ភាពខ្សោយ/ គុណវិបត្តិ/ ហានិភ័យនៃបច្ចេកទេស និងវិធីសាស្ត្រដោះស្រាយ

ភាពខ្សោយ/ គុណវិបត្តិ/ ហានិភ័យ ទស្សនៈរបស់អ្នកប្រើប្រាស់ដី	តើបច្ចេកទេសទាំងនោះបានដោះស្រាយបញ្ហាដូចម្តេច?
The technology is complex and cannot be applied by the land user her-/himself and is sometimes hard to understand. Therefore they might mistrust in the results and are not eager to accept regulations and measures to stop degradation.	Transparent documentation of the technology and regular field visits to evaluate together with the land owners and users the remote sensing results in the field.

ភាពខ្សោយ/ គុណវិបត្តិ/ ហានិភ័យ ទស្សនៈរបស់អ្នកចងក្រងឬបុគ្គលសំខាន់ៗ	តើបច្ចេកទេសទាំងនោះបានដោះស្រាយបញ្ហាដូចម្តេច?
The institutional setup on the national level for the regular application of the remote sensing technology and the storage and management of the monitoring data is not established yet. GIS, remote sensing and soil experts are of limited availability.	Institutional capacity building and academic training courses provided at the Georgian universities can help to overcome these limitations.
Field data for calibration of satellite images (biomass volumes, classified land cover types, soil types, land management types) with exact information on the spatial location are rare and costly to be created.	Such data and information should be organised and gathered on national level across different sectors (agriculture, forestry, spatial planing, nature conservation ...). This would help to reduce significantly the costs and remote sensing could be applied on much larger areas.

7.1 វិធីសាស្ត្រ/ ប្រភពនៃព័ត៌មាន

7.2 ឯកសារយោងដែលបានចេញផ្សាយ

ចំណងជើង អ្នកនិពន្ធ ឆ្នាំ ISBN:

Land Degradation Neutrality 25.10.2017

មានប្រភពមកពីណា? ថ្លៃដើមប៉ុន្មាន?

https://e-c-o.at/files/publications/downloads/D00813_ECO_policy_brief_LDN_Georgia_171025.pdf

7.3 ការភ្ជាប់ទៅកាន់ព័ត៌មានពាក់ព័ន្ធលើប្រព័ន្ធអនឡាញ

ចំណងជើង/ ពណ៌នា:

Tools for satellite image analysis

វេបសាយ:

http://step.esa.int/main/snap-2-0-out-now/

ចំណងជើង/ ពណ៌នា:

UNCCD Good Practice Guidance on SDG Indicator 15.31. (Sims et al. 2017)

វេបសាយ:

https://www.unccd.int/sites/default/files/relevant-links/2017-10/Good%20Practice%20Guidance_SDG%20Indicator%2015.3.1_Version%201.0.pdf

7.4 មតិយោបល់ទូទៅ

UNCCD Good Practice Guidance on SDG Indicator 15.31. (Sims et al. 2017) gives a detailed technical overview on methods and approaches to calculate LDN indicators by means of remote sensing data.